Study on Three-Dimensional Geological Sweet Spot Evaluation of Tight Sandstone Reservoirs Based on Well Logging Data

Study on Three-Dimensional Geological Sweet Spot Evaluation of Tight Sandstone Reservoirs Based on Well Logging Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Study on Three-Dimensional Geological Sweet Spot Evaluation of Tight Sandstone Reservoirs Based on Well Logging Data Hui Xiao, Lei Zhang, Junchen Liu, Jiuzhou Xiang, Chunbing Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3844170/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tight sandstone oil and gas resources have emerged as a crucial element in the future energy landscape. The primary focus of geological evaluation for the development of tight sandstone reservoirs involves the comprehensive assessment and selection of oil layers with high potential, commonly referred to as 'sweet spots.' This study proposes a geological sweet spot evaluation model for tight sandstone reservoirs, which utilizes eight conventional well logging curve data as samples: crosswave time difference, compressional wave time difference, natural gamma, density, wellbore diameter, phase resistivity, amplitude resistivity, and average neutron porosity. The model applies factor analysis and normalization principles to quantitatively assess the vertical distribution of geological sweet spots within the reservoir. Validation of the model is conducted using exemplar wells. Additionally, the study integrates seismic data with well logging data and employs waveform indication simulation techniques to simulate the stratigraphic parameters of the reservoir. This approach facilitates the establishment of a three-dimensional geological sweet spot evaluation model, enabling the inversion of key parameters of geological sweet spots in the target stratum. Consequently, the model allows for a quantitative assessment of the lateral distribution of geological sweet spots in Block DF11-2. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Since the 1970s, tight sandstone oil and gas have been significant in unconventional oil and gas exploration and development. According to data from the International Energy Agency (IEA) in 2018, the United States has nearly 900 tight sandstone oil and gas fields, with a total proven recoverable reserves of 5×10 12 m 3 , accounting for about 25% of the country's total oil and gas production 1 . More than half of the discovered oil and gas reserves are also tight sandstone oil and gas 2–5 . These oil and gas reservoirs are characterized by their wide distribution, high reserves, poor physical properties, and difficulty in development. Tight sandstone oil and gas resources play a crucial role in future energy security. The primary task is to evaluate and predict tight sandstone oil and gas reservoirs, which serves as the foundation for the efficient development of these resources. This research holds significant reference value for the exploration and development of unconventional oil and gas resources. The term 'sweet spot' was originally used to describe areas with better reservoir quality in conventional oil and gas exploration 6 . As unconventional oil and gas exploration has developed, this concept has also been applied to unconventional exploration. When evaluating sweet spots in unconventional low-permeability oil and gas, Chorn, et al. 7 suggest considering factors such as reservoir quality and completion quality. Reservoir quality indicates production capacity, while completion quality relates to fracturing effectiveness, and both are influenced by geomechanical parameters. Hashmy, et al. 8 suggest considering factors such as reservoir quality and completion quality. Reservoir quality indicates production capacity, while completion quality relates to fracturing effectiveness, and both are influenced by geomechanical parameters. Zhou Dehua and Jiao Fangzheng 9 argue that sweet spots can also refer to areas and horizons with exploration advantages, or target areas for unconventional oil enrichment and high production. According to Zhu Rukai, et al. 10 , priority should be given to target areas for exploration and development of unconventional oil enrichment and high production, considering economic and technical conditions. Chen Fuli, et al. 11 suggest that, under current economic and technical conditions, sweet spots refer to geological units with actual development benefits, including geologically advantageous reservoirs and economic benefits in development. In conclusion, sweet spots can encompass different types, such as geological sweet spots, engineering sweet spots, production sweet spots, and economic sweet spots. Geological sweet spots are crucial considerations in exploration and development as they determine the resource potential of sandstone reservoirs. Currently, there are three main methods used to evaluate the geological sweet spots of reservoirs. The first method is the comprehensive factor superposition method 12,13 , which selects key parameters in specific areas based on geological variations and integrates them to determine the sweet spot areas for shale oil. However, this method is primarily used for qualitative evaluation in a planar range and has relatively low accuracy. The second method is the seismic prediction method 14–17 , which analyzes rock physics data, selects sensitive seismic attribute parameters, builds a sweet spot seismic characterization model, and predicts the spatial distribution characteristics of shale oil sweet spots. However, this method has low prediction resolution and cannot meet the evaluation requirements for individual wells. The third method is the comprehensive evaluation index method 18,19 , which combines various evaluation factors to assess the sweet spots. However, further research is needed to improve the accuracy and resolution of this method for individual well evaluations.The quantification of key geological parameters and their weights is used in the comprehensive evaluation index method to analyze shale oil geological sweet spots. This method provides high resolution in the vertical direction but is influenced by various factors, leading to suboptimal fracturing effects. Scholars have proposed alternative evaluation methods. For instance, Chorn, et al. 7 conducted systematic studies on factors such as shale thickness, organic matter abundance, burial conditions, and thermal evolution, identifying highly evolved and large-scale shale formations as the primary exploration targets for sweet spots. C.H. Sondergeld, et al. 20 suggested evaluating and selecting based on parameters such as oil content, maturity, organic carbon content, and permeability. Frantz Jr., et al. 21 utilized numerical simulation techniques to demonstrate the significant impact of matrix porosity, permeability, and natural fractures on productivity. Li Jinbu, et al. 22 emphasized that the evaluation parameters for shale oil sweet spots mainly encompass lithology, oil saturation, and mobility. According to Yang Zhi, et al. 23 , the formation of sweet spots is primarily influenced by factors such as organic matter abundance, organic matter maturity, porosity, degree of fracture development, local structure, and pressure coefficient. Xie Xinong, et al. 24 suggest that the evaluation of geological sweet spots can be done by considering parameters such as organic carbon abundance in mudstone, thermal evolution degree of organic matter, oil saturation index, and stratigraphic pressure coefficient. In a similar vein, Zhu Rukai, et al. 10 evaluate geological sweet spots by establishing threshold values based on the study of parameters such as lithology, physical properties, oil saturation, and source rock characteristics. Building upon this research, Wei Yongbo, et al. 25 propose a method for evaluating shale oil sweet spots using five parameters, namely oil content, movable oil ratio, pressure coefficient, and permeability. Additionally, they determine the distribution characteristics of high-productivity intervals in shale formations. Several studies have focused on predicting and evaluating geological sweet spots in tight sandstone reservoirs. Zhu Haiyan, et al. 26 proposed a model that considers factors like total hydrocarbon content and reservoir porosity. Mi Honggang, et al. 27 utilized drilling, logging, and seismic data to provide a detailed description of three-dimensional spatial sequence stratigraphy and predict 'sweet spot' reservoirs in tight sandstones. Liu Yan 28 evaluated existing sweet spots and proposed a method that combines accumulation and seismic research to improve gas content prediction accuracy. This paper quantitatively characterizes geological sweet spots by evaluating parameters such as porosity, permeability, and saturation. Li Liuzhong, et al. 29 , Hui Wei 30 , and Han Cheng, et al. 31 have also made progress in interpreting and evaluating gas-bearing tight sandstone reservoirs using various logging methods. Dai Jianquan and Lu Zhengxiang 32 established a quantitative relationship model between pore-throat and permeability, enhancing the evaluation of ultra-tight sandstone reservoirs. Tang Jun, et al. 33 discussed the effectiveness of evaluating tight sandstone reservoirs using Stoneley wave characteristic parameters under water-based mud conditions. Chen Bixiao and Xu Binggao 34 investigated the feasibility of three gas content detection techniques for evaluating tight sandstone reservoirs in the Xujiahe Formation in western Sichuan. Zhang Songyang 35 developed a reservoir productivity prediction formula by optimizing logging parameters through synthetic analysis of tight reservoir sections, providing a basis for preferential exploitation of low-permeability gas reservoirs. Cheng Zhigang, et al. 36 established evaluation criteria for gas-bearing layers based on logging responses of residual movable gas saturation variations in different lithofacies. Wen Long, et al. 37 formulated an index formula for comprehensive discrimination of tight sandstone gas reservoirs, considering both reservoir quality and connectivity. However, the petrophysical properties of tight sandstone reservoirs are influenced by multiple complex factors, making it difficult to effectively describe pore-permeability characteristics with a single parameter. Solely relying on pore-permeability parameters also poses challenges for the effective evaluation of tight sandstone reservoirs 38–42 . Therefore, this study aimed to evaluate the geological "sweet spots" of tight sandstone reservoirs using multiple conventional well logging curves as a basis. Factor analysis and normalization methods were employed to establish an evaluation model, supplemented by seismic data to construct a three-dimensional geological "sweet spot" model and analyze the distribution trends of sweet spots within the block. Evaluation model for single well geological sweet spots Factor Analysis Factor Analysis is a statistical method used to study the relationships and structure among multiple variables. It aims to explore and explain the underlying structure and relationships among multiple variables. The factor analysis method is based on the following assumptions: ( 1 ) Multiple observed variables are influenced by a small number of latent factors. ( 2 ) The latent factors are independent of each other and unrelated. The basic principle of factor analysis is to transform the original variables into a smaller number of unrelated variables (factors). This transformation helps us understand the underlying dimensions and relationships among variables in the data. Factor analysis simplifies data analysis, facilitates the exploration of variable relationships, and provides valuable insights into the data. However, it is important to assess the suitability of the data, the representativeness of the sample, and the fulfillment of assumptions before using factor analysis. Factor analysis is characterized by several fundamental aspects. Firstly, it aims to reduce the dimensions of the original variables by extracting shared variance information and condensing a set of correlated variables into a smaller number of unrelated variables called factors. This simplifies the data and allows for a more concise description and interpretation. Secondly, factor analysis explores the presence of unobserved latent variables (factors) that influence the relationships among the observed variables. It helps uncover these underlying factors and understand their joint influence on the observed variables. Additionally, factor analysis not only provides information on the correlations between variables but also explains the underlying structure of these correlations. It helps us determine which variables jointly represent a concept or dimension, and the importance of variables in the factors can be assessed through factor loading coefficients. Furthermore, factor analysis allows for the interpretation and naming of each extracted factor through factor loading matrices and factor labels. Loading coefficients indicate the contribution of each variable to the factor and aid in understanding the concept or dimension represented. Lastly, rotation techniques can enhance the interpretability and interpretive power of the factors by making the relationships between factors and original variables clearer and easier to understand and interpret. The main objective of log interpretation in geological work is to establish a connection between well logging data and geological information. This involves transforming well logging data, which includes parameters like resistivity, natural gamma, sonic travel time, and rock bulk density, into geological information using appropriate methods. For example, natural gamma and spontaneous potential curves are effective in reflecting lithology and clay content of reservoirs. Additionally, resistivity values in gas-bearing intervals are higher than those in water-bearing intervals, and as gas saturation increases, resistivity shows a clear positive anomaly and a significant increasing trend in values. Reservoir characteristics often require the use of multiple well logging parameters, making the interpretation work complex and time-consuming. To simplify the analysis process, factor analysis can be employed. Factor analysis reduces the dimensionality of multiple variables and simplifies the analysis while retaining the essential data information. Therefore, in our study, we propose using factor analysis to identify and evaluate tight sandstone reservoirs with a reduced number of factor parameters. Standard deviation normalization processing In this study, we chose specific well logging parameters (sonic travel time, natural gamma, density, well diameter, phase resistivity, amplitude resistivity, and average neutron porosity) that are known to be influenced by reservoir characteristics. To ensure accurate analysis, we applied standard deviation normalization to the well logging data. This was necessary because the well logging sequences varied in dimensions and had significant differences in numerical values. $$s=\sqrt {\frac{{\left[ {{{\left( {{a_1} - \overline {a} } \right)}^2}+{{\left( {{a_2} - \overline {a} } \right)}^2}+ \ldots +{{\left( {{a_N} - \overline {a} } \right)}^2}} \right]}}{N}}$$ 1 $${Z_i}=\frac{{({a_i} - \overline {a} )}}{s}$$ 2 Among them, s represents the standard deviation, a i represents the sample data for i = 1,..., N , represents the sample mean, N represents the sample size, and represents the normalized sample data. Factor analysis is employed to extract factors and establish a geological "sweet spot" evaluation model for sandstone reservoirs. Data validation Before conducting factor analysis, it is necessary to validate the data to determine if it is suitable for analysis using this method. Therefore, the data is subjected to KMO and Bartlett's tests to assess the feasibility of factor analysis. KMO test: $$M=\frac{R}{{R+Q}}=\frac{{\sum\nolimits_{{i=1}}^{N} {\sum\nolimits_{{j=1}}^{N} {{r_{ij}}^{2}} } }}{{\sum\nolimits_{{i=1}}^{N} {\sum\nolimits_{{j=1}}^{N} {{r_{ij}}^{2}} } +\sum\nolimits_{{i=1}}^{N} {\sum\nolimits_{{j=1}}^{N} {{q_{ij}}^{2}} } }}$$ 3 In this equation, M represents the KMO value, R and Q are the sums of squares of all feature Pearson correlation coefficients and partial correlation coefficients, r ij represents the Pearson correlation coefficient of the variables, q ij represents the partial correlation coefficient controlling for the remaining variables, and N represents the sample size. Bartlett's test: $${\mathbf{C}}=\left[ {\begin{array}{*{20}{c}} {{r_{11}}}&{{r_{12}}}& \ldots &{{r_{1N}}} \\ {{r_{21}}}&{{r_{22}}}& \ldots &{{r_{2N}}} \\ \vdots & \vdots & \ddots & \vdots \\ {{r_{N1}}}&{{r_{N2}}}& \ldots &{{r_{NN}}} \end{array}} \right]$$ 4 In this equation, C represents the correlation coefficient matrix. Based on the degrees of freedom and the test statistic, the associated probability of Bartlett's test is approximated by consulting the chi-square distribution table. By considering the relationship between the associated probability and the significance level, it can be determined if factor analysis is appropriate. The KMO value is used to determine the suitability of using factor analysis. A KMO value above 0.9 is considered highly suitable, between 0.7 and 0.9 is appropriate, between 0.6 and 0.7 is still acceptable, between 0.5 and 0.6 indicates poor suitability, and a value below 0.5 suggests that factor analysis should be abandoned. In the context of Bartlett's test, if the P -value is less than 0.05 and the null hypothesis is rejected, it suggests that factor analysis can be performed. On the other hand, if the null hypothesis is not rejected, it indicates that these variables may provide independent information and are not suitable for factor analysis. Table 1 KMO Test and Bartlett's Test Results. Note: ***, **, and * represent the significance levels of 1%, 5%, and 10% respectively KMO value 0.683 Bartlett sphericity test Approximate chi-square 140871.19 d f 28 P 0.000*** Based on the results of KMO test and Bartlett's test in Table 1 , it can be concluded that the selected data is suitable for conducting factor analysis. Factor number determination After standardizing the standard deviation of logging curves, such as horizontal shear wave travel time (DTSM), compressional wave travel time (DTCO), natural gamma ray (GR), density (RHOB), borehole diameter (DCAV), phase resistivity (P40H), amplitude resistivity (A40H), and average neutron porosity (TNPH), for the target reservoirs in the DF block, a correlation coefficient matrix is constructed using the standardized data. This matrix is then used to calculate all eigenvalues ( λ 1 , λ 2 , ..., λ N ) and eigenvectors ( µ 1 , µ 2 , ..., µ N ). The variance contribution rate and cumulative variance contribution rate are also computed. Based on the criterion that eigenvalues should be greater than 1, several factors are extracted as common factors. The final results are presented in Table 2 . Table 2 Variance Explained. Total variance explained Components Variance explained before rotation Variance explained after rotation Eigenvalues Variance explained ratio(%) Cumulative variance explained ratio(%) Eigenvalues Variance explained ratio(%) Cumulative variance explained ratio(%) 1 3.187 39.836 39.836 239.829 29.979 29.979 2 2.052 25.65 65.486 221.096 27.637 57.616 3 1.119 13.986 79.472 174.851 21.856 79.472 4 0.814 10.18 89.652 5 0.435 5.436 95.088 6 0.266 3.326 98.415 7 0.095 1.183 99.597 8 0.032 0.403 100 According to the principle of factor analysis where eigenvalues are greater than 1, the number of factors can be determined as p = 3. Identification model establishment Based on all the calculated eigenvectors, the loading matrix A 0 is constructed. The rotated loading matrix A is obtained using the maximum variance method, and the factor score matrix G is calculated using the regression estimation method. $${{\mathbf{A}}^0}=\left[ {\begin{array}{*{20}{c}} {{a^0}_{{11}}}&{{a^0}_{{12}}}& \ldots &{{a^0}_{{1H}}} \\ {{a^0}_{{21}}}&{{a^0}_{{22}}}& \ldots &{{a^0}_{{2H}}} \\ \vdots & \vdots & \ddots & \vdots \\ {{a^0}_{{N1}}}&{{a^0}_{{N1}}}& \ldots &{{a^0}_{{NH}}} \end{array}} \right]=\left[ {\begin{array}{*{20}{c}} {{\mu _{11}}\sqrt {{\lambda _1}} }&{{\mu _{21}}\sqrt {{\lambda _2}} }& \ldots &{{\mu _{H1}}\sqrt {{\lambda _H}} } \\ {{\mu _{21}}\sqrt {{\lambda _1}} }&{{\mu _{22}}\sqrt {{\lambda _2}} }& \ldots &{{\mu _{H2}}\sqrt {{\lambda _H}} } \\ \vdots & \vdots & \ddots & \vdots \\ {{\mu _{1N}}\sqrt {{\lambda _1}} }&{{\mu _{2N}}\sqrt {{\lambda _2}} }& \ldots &{{\mu _{HN}}\sqrt {{\lambda _1}} } \end{array}} \right]$$ 5 $${\mathbf{A}}=\left[ {\begin{array}{*{20}{c}} {{a_{11}}}&{{a_{12}}}& \ldots &{{a_{1H}}} \\ {{a_{21}}}&{{a_{22}}}& \ldots &{{a_{2H}}} \\ \vdots & \vdots & \ddots & \vdots \\ {{a_{N1}}}&{{a_{N1}}}& \ldots &{{a_{NH}}} \end{array}} \right]$$ 6 $${\mathbf{G}}={{\mathbf{C}}^{ - 1}}{\mathbf{A}}={\left[ {\begin{array}{*{20}{c}} {{r_{11}}}&{{r_{12}}}& \ldots &{{r_{1N}}} \\ {{r_{21}}}&{{r_{22}}}& \ldots &{{r_{2N}}} \\ \vdots & \vdots & \ddots & \vdots \\ {{r_{N1}}}&{{r_{N1}}}& \ldots &{{r_{NN}}} \end{array}} \right]^{ - 1}}\left[ {\begin{array}{*{20}{c}} {{a_{11}}}&{{a_{12}}}& \ldots &{{a_{1H}}} \\ {{a_{21}}}&{{a_{22}}}& \ldots &{{a_{2H}}} \\ \vdots & \vdots & \ddots & \vdots \\ {{a_{N1}}}&{{a_{N1}}}& \ldots &{{a_{NH}}} \end{array}} \right]$$ 7 In this equation, a 0 NH represents the matrix element before rotation, a NH represents the matrix element after rotation, and µ HN represents the eigenvector. According to the factor loading heat map (Fig. 1 ), it can be concluded that F1 is mainly influenced by DCAV, TNPH, and DTSM. An increase in DCAV and TNPH values and a decrease in RHOB values lead to an increase in F1, indicating that F1 mainly reflects the porosity of the reservoir, making it the porosity factor. F2 is primarily influenced by A40H and P40H, showing a positive correlation with them, indicating that F2 mainly reflects the gas content of the reservoir, making it the gas factor. F3 is mainly influenced by GR and RHOB, reflecting the clay content of the reservoir, making it the clay factor. The factor score coefficients obtained by composing the loading matrix with eigenvectors are shown in Table 3 . Table 3 Factor score coefficient table. Name Component Porosity factor (F 1 ) Gas factor (F 2 ) Clay factor (F 3 ) A40H 0.137 0.501 0.073 P40H 0.095 0.474 0.053 DCAV 0.464 0.261 0.184 GR 0.196 0.01 0.378 RHOB 0.014 -0.032 0.474 TNPH 0.303 -0.021 0.009 DTCO 0.077 -0.156 -0.444 DTSM 0.347 -0.011 -0.138 F1 = 0.137×A40H + 0.095×P40H + 0.464×DCAV + 0.196×GR + 0.014×RHOB + 0.303×TNPH + 0.077×DTCO + 0.347&ti F 2 = 0.501×A40H + 0.474×P40H + 0.261×DCAV + 0.01×GR-0.032×RHOB-0.021×TNPH-0.156× DTCO − 0.011×DTSM ( 9 ) F 3 = 0.073×A40H + 0.053×P40H + 0.184×DCAV + 0.378×GR + 0.474×RHOB + 0.009×TNPH-0.444×DTCO-0.138×DTSM ( 10 ) Based on the positive and negative correlations of each factor with geological compressibility, the values of each factor are normalized as follows: Positive indicator calculation formula: $$Y=\frac{{a - {a_{\hbox{min} }}}}{{{a_{\hbox{max} }} - {a_{\hbox{min} }}}}$$ 11 Negative indicator calculation formula: $$Y=\frac{{{a_{\hbox{max} }} - a}}{{{a_{\hbox{max} }} - {a_{\hbox{min} }}}}$$ 12 In this equation, S represents the normalized parameter value, Y represents the original parameter value, a min represents the minimum value of the parameter, and a max represents the maximum value of the parameter. The weights of each factor are obtained based on the variance explained ratio and cumulative variance explained ratio, as shown in Table 4 . Table 4 Factor weight analysis. Name Variance explained ratio after rotation (%) Cumulative variance explained ratio after rotation (%) Weight (%) Factor 1 0.3 29.979 37.722 Factor 2 0.276 57.616 34.776 Factor 3 0.219 79.472 27.502 The evaluation model for tight sandstone reservoirs can be derived from the above: F = 0.38×F1 + 0.35×F2 + 0.27×F 3 13 Model validation In order to ensure the accuracy of the geological sweet spot evaluation model for this well, it is now being validated through well WC9-7-11. WC9-7-11 well is a vertical well located in the southern part of Wenchang A sag. The well logging interpretation of the fracturing test target layer reveals the following characteristics: water saturation ranges from 51.8–63.4%, clay content ranges from 1.0–24.8%, porosity ranges from 6.5–10.8%, and permeability ranges from 0.18mD to 0.50mD. The fracturing interval spans from a depth of 4190m to 4215.7m, while the perforation interval ranges from 4200.2m to 4215.7m. Based on the aforementioned model, the geological sweet spot curve of the well is illustrated in Fig. 2 . The figure clearly indicates that the perforation interval exhibits a relatively high geological sweet spot index. This suggests that the model possesses a certain level of accuracy, as the optimal sweet spot area should be selected when determining the perforation location. Three-dimensional geological evaluation model The establishment of a three-dimensional geological sweet spot evaluation model relies on seismic-related data and well logging data to determine the key parameters of the geological sweet spot in the target layer. These parameters, including porosity factor, gas content factor, clay content factor, and geological compressibility, are then used to generate a three-dimensional distribution map. Thus, calculating the corresponding parameters is crucial in developing the three-dimensional geological sweet spot evaluation model. In the evaluation of the three-dimensional geological sweet spot, simulation and inversion techniques heavily rely on three-dimensional seismic inversion calculations. Three-dimensional seismic data is a type of densely and structurally organized data that allows for the identification of spatial variations in different sedimentary environments, lithology combinations, and elastic parameters 43 . To achieve this, we employ the principles of sedimentology to select relevant well samples by assessing the similarity of seismic waveforms. By considering the spatial distribution distance and curve distribution characteristics of the samples, an initial model is established. Additionally, we propose the concept of 'phase cutoff frequency' to expand the frequency range, based on the analysis of elastic parameter curves within the same lithofacies. Statistical analysis of the longitudinal wave impedance of the sample wells is conducted to establish a prior probability function. The initial model is then matched and filtered with the seismic wave impedance volume to generate a likelihood function, gradually removing high-frequency components. Using Bayesian theory, the likelihood distribution is combined with the prior distribution to obtain the posterior probability distribution, which serves as the objective function. The samples selected based on waveform indication exhibit a strong spatial correlation, and the Metropolis-Hastings sampling algorithm is employed to sample the posterior probability distribution. The solution that maximizes the objective function is chosen as a feasible random realization, and the average of multiple feasible realizations is calculated as the expected output 43 . Therefore, this inversion method effectively utilizes the lateral variations of seismic waveforms, resulting in improved vertical resolution and reduced randomness, ultimately enabling more accurate simulation of the desired parameters. This calculation process relies on a comprehensive seismic interpretation, utilizing well-established well data and seismic information within the study area. The waveform indication simulation technique is employed to replicate the formation parameters of the target layer as per the procedure outlined in Fig. 3 44 . To ensure clarity and flow, the following steps were taken in the study: Step 1: A seismic interpreted horizon-based isochronous framework model was established. Step 2: The predicted seismic waveforms were compared and analyzed with the waveforms near the wells. Wells with similar waveform characteristics were selected as valid samples. The curve structure on these sample wells was analyzed to establish an initial model. Step 3: The initial model was filtered using seismic mid-frequency impedance as a standard. High-frequency information that did not conform to the standard was removed. A likelihood function was constructed, and random simulations were performed repeatedly to ensure that the simulated results simultaneously conformed to the seismic mid-frequency impedance and the well curve structure characteristics. Step 4: The final simulation results were outputted. These results can be randomly realized or the mean of multiple realizations can be taken as the expected value. In order to accurately reflect the conditions of stratigraphic deposition, it is crucial to analyze the contact relationships between the different layers. If the layering features are clearly visible and the interpretation of horizons is consistently continuous, it is important to consider the stratigraphic characteristics when establishing the geological model. Therefore, the selection of stratigraphic contact relationships should be defined as parallel to the concept of 'equally divided'. By employing the aforementioned methods, it is possible to obtain an initial inversion model that provides a more precise representation of the structural morphology and internal structure of the target layer. The profile of the initial model is depicted in Fig. 4 . The establishment of the initial model involves several steps. Firstly, well logging data is used to perform seismic synthetic record processing, which helps in obtaining accurate time-depth relationships and ensuring the geological horizon calibration is reasonable. This step is crucial for maintaining the accuracy of well time-depths during inversion. Secondly, a framework model of the stratigraphic structure is established based on seismic structural interpretation results. Geometric control structures between the strata are defined to create a detailed stratigraphic model. In this case, the stratigraphic contact relationship is selected as 'equally divided'. Finally, the intermediate parameter curves of the control points are combined with the detailed stratigraphic model. A three-dimensional spatial curve interpolation method is applied to generate the three-dimensional image of the inverted initial model. Case study DF1-1 structure is located in the northern area of the central depression of the Yinggehai Basin. It is a mid-level (HL Group 1) anticlinal closure that has developed on the background of the Neogene bottom sag. The porosity of the HL Group reservoirs in the DF area is mainly distributed between 15% and 25%, and the permeability generally ranges from (0.1 to 100)×10 − 3 µm 2 . The reservoirs primarily consist of mesoporous and medium to low-permeability reservoirs. However, there are significant variations in reservoir properties. In the DF13-2 area, the reservoir properties are relatively good, with porosity mainly distributed between 15% and 20%, and permeability mainly distributed between (1.0 to 100)×10 − 3 µm 2 . Moreover, there are also notable differences in reservoir properties among different wells within the same area. For instance, in the case of DF1-1-13, the well logging interpretation covers a total depth of 76.4m with 7 layers. This includes 29.8m of gas-bearing layers in 2 sub-layers, 13.3m of sub-gas layers in 2 sub-layers, and 33.3m of dry layers in 3 sub-layers. The comprehensive interpretation of the entire well amounts to 236.4m with 17 layers. This includes 110.8m of gas-bearing layers in 5 sub-layers, 92.3m of sub-gas layers in 10 sub-layers, and 33.3m of dry layers in 3 sub-layers. Based on the established geological sweet spot model discussed in the previous section, the three-factor values were calculated individually using the logging curve data from well DF1-1-13 (Fig. 5 ). Subsequently, the geological sweet spot index of the well was determined. Figure 6 illustrates the generation of the three factor curves and the geological sweet spot index curve. The geological sweet spot index of well DF1-1-13 predominantly ranges between 0.3 and 0.7, exhibiting notable vertical variations. The geological sweet spot curves were analyzed for their distribution range using data from three wells (DF1-1-12, DF13-1-10, and DF13-1-7) within the DF11-2 block, where well DF1-1-13 is located. The analysis, shown in Fig. 7 , reveals that the porosity factor values of the four wells range between 0.1 and 0.6, with the highest concentration between 0.2 and 0.4. Similarly, the mud factor values range from 0.2 to 0.7, with the majority falling between 0.3 and 0.6. The water content factor values show a distribution range of 0.1 to 0.7, with the majority falling between 0.2 and 0.6. Lastly, the geological compressibility values range from 0.2 to 0.6, with the majority falling between 0.3 and 0.5. Overall, the distribution trends of the porosity factor, gas content factor, mud factor, and geological compressibility curve of the four wells are consistent, with similar peak ranges. These findings meet the requirements of curve standardization and fulfill the standardization needs for data inversion. From the extracted along-layer slices (Fig. 8 ), The porosity factor values in the HL group are primarily distributed between 0.3–0.4, with a concentrated high-value area in the southern middle section of the study area. Along the layers, the gas content factor values in the HL group are mainly distributed between 0.24–0.3, with relatively lower values in the central and southern parts of the study area. However, the range of gas content factor values is small, resulting in insignificant numerical differences. The mud factor values in the HL group are mainly distributed between 0.3–0.6, with a localized high-value area in the southwestern part of the study area. The values of geological compressibility in the HL group are primarily distributed between 0.25–0.45, with a localized high-value area in the southwestern part of the study area. Conclusion A geological sweet spot model for tight sandstone reservoirs was established using factor analysis and the principle of normalization, based on the data of eight conventional well logging curves. The model quantitatively evaluated the vertical distribution of the reservoir's geological sweet spots and was verified to be highly accurate through well examples. A three-dimensional geological sweet spot evaluation model is established by combining seismic-related data and logging data. This model is used to invert the key parameters of the geological sweet spots in the target layer and quantitatively evaluate the lateral distribution of the reservoir's geological sweet spots. Declarations Data availability The data used is confidential. Acknowledgements The authors sincerely thank the Scientific and Technological Research Program of Chongqing Municipal Education Commission (GN:KJQN202201521); the Natural Science Foundation Project of Chongqing Science and Technology Bureau (GN:cstc2020jcyj-zdxmX0001) and the General project of Chongqing Natural Science Foundation, China (GN:cstc2021jcyj-msxmX0790), for their financial support. Author contributions All authors contributed to the study conception and design. Te frst draf of the manuscript was written by L.Z, manuscript review and editing were performed by J.X, J.L, C.W and H.Z. H.X. advised the students and corrected the manuscript. All authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript. Competing interests Te authors declare no competing interests. References Jin, Z. J. & Zhang, F. Q. Status and major advancements in study of hydrocarbon migration. Oil & Gas Geology . 26 , 263-270, https://doi.org/10.3321/j.issn:0253-9985.2005.03.001 (2005). Zou, C. N. et al. New advances in global unconventional oil and gas exploration and theoretical research. China Institute of Petroleum Exploration and Development . (2013). Zou, C. N. et al. Geological concepts, characteristics, resource potential and key techniques of unconventional hydrocarbon: On unconventional petroleum geology Petroleum Exploration and Development. 40 , 385-399,454, https://doi.org/10.11698/ped.2013.04.01 (2013). Zou, C. N. et al. Types, characteristics, genesis and prospects of conventional and unconventional hydrocarbon accumulations: taking tight oil and tight gas in China as an instance. Acta Petrolei Sinica. 33 , 173-187, https://doi.org/10.7623/syxb201202001 (2012). Jia, C. Z., Zou, C. N., Li, J. Z., Li, D. H. & Zhen, M. Assessment criteria, main types, basic features and resource prospects of the tight oil in China. Acta Petrolei Sinica. 33 , 343-350, https://doi.org/10.7623/syxb201203001 (2012). Prise, G. J., Stewart, D. R., Bird, T. M., Holland, B. & Wilson, W. W. Successful completion operations on Ravenspurn North Development. SPE Offshore Europe . https://doi.org/10.2118/26744-MS (1993). Chorn, L., Yarus, J., Rosario-Davis, S. d. & Pitcher, J. Identification of Shale Sweet Spots Using Key Property Estimates from Log Analysis and Geostatistics. https://doi.org/10.1190/urtec2013-154 (2013). Hashmy, K., Abueita, S., Petroleum, A., Barnett, C. & Jonkers, J. Log-Based Identification of Sweet Spots for Effective Fracs in Shale Reservoirs. Canadian Unconventional Resources Conference . https://doi.org/10.2118/149278-MS (2011). Zhou, D. H. & Jiao, F. Z. Evaluation and prediction of shale gas sweet spots: a case study in JurassicofJiannan area, Sichuan Basin. Petroleum Geology & Experiment. 34 , 109-114, https://doi.org/10.3969/j.issn.1001-6112.2012.02.001 (2012). Zhu, R. K. et al. Mechanism for generation and accumulation of continental tight oil in China. Oil & Gas Geology . 40 , 1168-1184, https://doi.org/10.11743/ogg20190602 (2019). Chen, F. L., Tong, M., Yan, L., Liu, L. F. & Wang, S. J. Sweetness evaluation method for “sweet spot” of tight oil reservoir. Special Oil & Gas Reservoirs. 24 , 12-17, https://doi.org/10. 3969/j. issn. 1006－6535. 2017. 02. 003 (2017). Zou, C. N. et al. Formation mechanism, geological characteristics and development strategy of nonmarine shale oil in China. Petroleum Exploration and Development. 40 , 14-26 (2013c). Yang, Z. et al. Formation, distribution and resource potential of the "sweet areas (sections)" of continental shale oil in China. Marine & Petroleum Geology . 102 , 48-60, https://doi.org/10.1016/j.marpetgeo.2018.11.049 (2019). Dong, Y., Xu, D. S., Qian, G. B., Wang, X. H. & Dai, Y. J. Shale Oil “Sweet－Spot” Prediction in Jimusar Sag. Special Oil & Gas Reservoirs. 27 , 54-59, https://doi.org/10.3969/j.issn.1006-6535.2020.03.009 (2020). Pan, R. F., Chen, M. L., Zhang, C. M. & Pan, J. Seismic prediction of Paleogene shale oil "sweet spots" and its influencing factor analysis in the Bonan sub-sag, Jiyang depression. Earth Science Frontiers. 25 , 142-154, https://doi.org/10.13745/j.esf.sf.2018.5.26 (2018). Gao, Q. J. et al. Well-seismic joint technology for quantitative evaluation of “sweet spot” in continental shale oil：A case study of Lower Es 3 Member of Luojia area in Jiyang Depression. Petroleum Geology and Recovery Efficiency. 26 , 165-173, https://doi.org/10.13673/j.cnki.cn37-1359/te.2019.01.017 (2019). Guo, X. G. et al. Evaluation and application of key technologies of "sweet area" of shale oil in Junggar Basin: Case study of Permain Lucaogou Formation in Jimusar Depression. Natural Gas Geoscience. 30 , 1168-1179, https://doi.org/10.11764/j.issn.1672-1926.2019.05.020 (2019). Zhang, P. F. et al. Identification method of sweet spot zone in lacustrine shale oil reservoir and its application: A case study of the Shahejie Formation in Dongying Sag, Bohai Bay Basin. Oil & Gas Geology . 40 , 1339-1350, https://doi.org/10.11743/ogg20190618 (2019). Zhao. X. Z. et al. Geological characteristics and exploration breakthrough of shale oil in Member 3 of Shahejie Formation of Qibei subsag, Qikou sag. Acta Petrolei Sinica. 41 , 643-657, https://doi.org/10.7623/syxb202006001 (2020). C.H. Sondergeld, K.E. Newsham, T. Comisky, M.C. Rice & C.S. Rai. Petrophysical Considerations in Evaluating and Producing Shale Gas Resources. SPE Unconventional Gas Conference . https://doi.org/10.2118/131768-MS (2010). Frantz Jr., J. H. et al. Evaluating Barnett Shale Production Performance Using an Integrated Approach. SPE Annual Technical Conference and Exhibition . https://doi.org/ 10.2118/96917-MS (2012). Li, J. B. et al. A new method for predicting sweet spots of shale oil using conventional well logs. Marine & Petroleum Geology . 113 , 104097, https://doi.org/10.1016/j.marpetgeo.2019.104097 (2020). Yang, Z. et al. Formation conditions and “sweet spot ” evaluation of tight oil and shale oil. Petroleum Exploration and Development. 42 , 555-565, https://doi.org/10.11698/PED.2015.05.02 (2015). Xie, X. N. et al. Differential enrichment mechanism and key technology of shale gas in complex areas of south China. Geoscience . 42 , 1045-1056, https://doi.org/10.3799/dqkx.2017.084 (2017). Wei, Y. B. et al. Comprehensive evaluation method of sweet spot zone in lacustrine shale oil reservoir and its application: A case study of shale oil in lower 1st member of the Shahejie formation in the Raoyang sag. Journal of China University of Mining & Technology. 50 , 813-824, https://doi.org/10.13247/j.cnki.jcumt.001223 (2021). Zhu, H. Y., Gong, D. & Zhang, B. Amulti-scale geology-engineering sweet spot evaluation method for tight sandstone gas reservoirs. Natural Gas Industry. 43 , 76-86, https://doi.org/10.3787/j.issn.1000-0976.2023.06.007 (2023). Mi, H. G., Zhang, B., Zhu, G. H., Su, Y. & Zhang, H. F. Geoological characteristics and development potential analysis of Linxing tight sandstone gas reservoir. Special Oil & Gas Reservoirs. 29 , 65-72, https://doi.org/10.3969/j.issn.1006-6535.2022.06.008 (2022). Liu, Y. Integrated sweets spots evaluation technology for tight sandstone gas reservoirs in Zhongjiang Gas Field. Journal of Southwest Petroleum University(Science & Technology Edition). 44 , 12-25, https://doi.org/10.11885/j.issn.1674-5086.2022.01.27.03 (2022). Li, L. Z., Han, C., Wang, P. & Deng, C. H. Logging evaluation method of tight-gas-sandstone reservoirs in Northern Mountain Front. Tuha Oil & Gas. 15 , 257-262, https://doi.org/CNKI:SUN:THYQ.0.2010-02-017 (2010). Hui, W. Logging evaluation method of tight sandstone reservoir in Sichuan basin. Petroleum Geology and Engineering. 29 , 80-83，148, https://doi.org/CNKI:SUN:SYHN.0.2015-02-022 (2015). Han, C., Gao, X., Wang, J. X., Wang, S. Z. & Pan, H. F. Logging evaluation on tight sands reservoir in Tuha basin. Tuha Oil & Gas. 17 , 1-7, https://doi.org/CNKI:SUN:THYQ.0.2012-01-003 (2012). Dai, J. Q. & Lu, Z. X. Assessment of Hypercompact SandstoneReservoirs of the Upper Triassic Xujiahe Formation in West Sichuan. Acta Geologica Sichuan. 30 , 450-453, https://doi.org/10.3969/j.issn.1006-0995.2010.04.020 (2010). Tang, J., Zhang, C. G. & Cai, D. Y. Effectiveness evaluation of tight sandstone reservoir based on stoneley wave characteristic parameters. Journal of Oil and Gas Technology. 35 , 79-85，77, https://doi.org/10.3969/j.issn.1000-9752.2013.06.015 (2013). Chen, B. X. & Xu, B. G. Logging evaluation technique for compact clastic rocks in West Sichuan. Journal of Oil and Gas Technology. 31 , 108-114，184, https://doi.org/10.3969/j.issn.1000-9752.2009.06.020 (2009). Zhang, S. Y. Logging evaluation technique for tight sandstone reservoir in Daniudi Gasfield. Geophysical Prospecting for Petroleum. 49 , 415-420，420, https://doi.org/10.3969/j.issn.1000-1441.2010.04.013 (2010). Cheng, Z. G. et al. Classification of petrophysical facies and gas evaluation in tight reservoir-case of re-evaluation of old wells in eastern Sulige. Petroleum Geology and Recovery Efficiency. 20 , 23-25，27，32，112, https://doi.org/10.13673/j.cnki.cn37-1359/te.2013.05.006 (2013). Wen, L., Liu, A. P., Zhong, Z. C., Yuan, J. & Li, H. L. Method of evaluating upper triassic tight sandstone reservoirs in West Sichuan foreland basin. Natural Gas Industry. 25 , 49-53,48, https://doi.org/CNKI:SUN:TRQG.0.2005-S1-013 (2005). Jiang, Y. Q. et al. Characterization techniques and trends of the pore structure of tight reservoirs. Geological Science and Technology Information. 33 , 63-70, https://doi.org/CNKI:SUN:DZKQ.0.2014-03-010 (2014). Yang, Z. M., Jiang, H. Q., Zhu, G. Y., Li, S. T. & Shan, W. W. Research on reservoir evaluation index for low-permeability water-bearing gas reservoir. Acta Petrolei Sinica. 29 , 252-256, https://doi.org/10.3321/j.issn:0253-2697.2008.02.017 (2008). Chen, Z. G. Analogy analysis of West Sichuan depression and Northern America sandstone gas rrservoirs. Journal of Southwest Petroleum University(Science & Technology Edition). 34 , 71-76, https://doi.org/10.3863/j.issn.1674-5086.2012.01.011 (2012). Meng, Z. Y. et al. Combined mercury porosimetry to characterize the microscopic pore structure and pore size distribution of tight reservoirs: a case of Chang 6 reservoir in Wuqi area, Ordos Basin. Geological Science and Technology Information. 38 , 208-216, https://doi.org/10.19509/j.cnki.dzkq.2019.0224 (2019). Ye, L. Y., Zhong, B., Xiong, W., Liu, H. X. & Hu, Z. M. An integrated evaluation method of Xujiahe low-permeability sandstone gas reservoirs in Middle Sichuan Basin. Natural Gas Industry. 32 , 43-46，116-117, https://doi.org/10.3787/j.issn.1000-0976.2012.11.010 (2012). Han, C. C., Lin, C. Y., Ren, L. H., Dong Chunmei & Wei Ting. Waveform-indication-based seismic inversion of carbonate reservoirs:A case study of the Lower-Middle Ordovician in Tahe oilfield,Tarim Basin. Oil & Gas Geology . 38 , 822-830, https://doi.org/10.11743/ogg20170419 (2017). Yue, L. J. & Qian, Y. L. Prediction of the narrow-thin reservoir based on seismic waveform indication inversion technology. Petroleum Geology & Oilfield Development in Daqing. 39 , 135-140, https://doi.org/10.19597/j.issn.1000-3754.201909027 (2020). Additional Declarations No competing interests reported. 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12:59:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123903,"visible":true,"origin":"","legend":"\u003cp\u003eGeological Sweet Spot Curve of Well WC9-7-11.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3844170/v1/28f3be0841821a369858f350.png"},{"id":50557353,"identity":"768ffc9c-f03c-4651-81aa-fd4628c131e1","added_by":"auto","created_at":"2024-02-02 12:59:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139301,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic Waveform Indication Simulation Technique Flowchart.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3844170/v1/43704297c0ae51d7736dcaa5.png"},{"id":50557355,"identity":"26603d27-7a13-4b3d-a02b-39a625104420","added_by":"auto","created_at":"2024-02-02 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12:59:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":464539,"visible":true,"origin":"","legend":"\u003cp\u003eDF1-1-13 Three-Factor Curve and Geological Sweet Spot Curve.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3844170/v1/d058bd0737a28d43e2c19df3.png"},{"id":50557356,"identity":"46b191a2-12ad-41de-a259-c122dbc77f84","added_by":"auto","created_at":"2024-02-02 12:59:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":400597,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution histogram plot. (a) Porosity factor curve. (b) Clay factor curve. (c) Gas factor curve. (d) Geological Sweet Spot Curve.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3844170/v1/36e1d025437d6c6505eaa80a.png"},{"id":50557354,"identity":"28f4e0b8-4825-4804-8877-cc6445e9be74","added_by":"auto","created_at":"2024-02-02 12:59:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1134606,"visible":true,"origin":"","legend":"\u003cp\u003eDF11-2 Block Inversion Volume Along Layer Slice Map. (a) Gas factor along-layer slice map. (b) Clay factor along-layer slice map. (c) Porosity factor along-layer slice map. (d) Geological Sweet Spot along-layer slice map.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3844170/v1/f837c351f355abee52776067.png"},{"id":50949180,"identity":"fdc2704a-b5dd-4aff-8afd-b926182299f4","added_by":"auto","created_at":"2024-02-10 09:53:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4241556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3844170/v1/b83cf6fc-bccd-4125-9c40-c0e975de3d7f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on Three-Dimensional Geological Sweet Spot Evaluation of Tight Sandstone Reservoirs Based on Well Logging Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the 1970s, tight sandstone oil and gas have been significant in unconventional oil and gas exploration and development. According to data from the International Energy Agency (IEA) in 2018, the United States has nearly 900 tight sandstone oil and gas fields, with a total proven recoverable reserves of 5×10\u003csup\u003e12\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003e, accounting for about 25% of the country's total oil and gas production \u003csup\u003e1\u003c/sup\u003e. More than half of the discovered oil and gas reserves are also tight sandstone oil and gas \u003csup\u003e2–5\u003c/sup\u003e. These oil and gas reservoirs are characterized by their wide distribution, high reserves, poor physical properties, and difficulty in development. Tight sandstone oil and gas resources play a crucial role in future energy security. The primary task is to evaluate and predict tight sandstone oil and gas reservoirs, which serves as the foundation for the efficient development of these resources. This research holds significant reference value for the exploration and development of unconventional oil and gas resources.\u003c/p\u003e \u003cp\u003eThe term 'sweet spot' was originally used to describe areas with better reservoir quality in conventional oil and gas exploration \u003csup\u003e6\u003c/sup\u003e. As unconventional oil and gas exploration has developed, this concept has also been applied to unconventional exploration. When evaluating sweet spots in unconventional low-permeability oil and gas, Chorn, et al. \u003csup\u003e7\u003c/sup\u003e suggest considering factors such as reservoir quality and completion quality. Reservoir quality indicates production capacity, while completion quality relates to fracturing effectiveness, and both are influenced by geomechanical parameters. Hashmy, et al. \u003csup\u003e8\u003c/sup\u003e suggest considering factors such as reservoir quality and completion quality. Reservoir quality indicates production capacity, while completion quality relates to fracturing effectiveness, and both are influenced by geomechanical parameters. Zhou Dehua and Jiao Fangzheng \u003csup\u003e9\u003c/sup\u003e argue that sweet spots can also refer to areas and horizons with exploration advantages, or target areas for unconventional oil enrichment and high production. According to Zhu Rukai, et al. \u003csup\u003e10\u003c/sup\u003e, priority should be given to target areas for exploration and development of unconventional oil enrichment and high production, considering economic and technical conditions. Chen Fuli, et al. \u003csup\u003e11\u003c/sup\u003e suggest that, under current economic and technical conditions, sweet spots refer to geological units with actual development benefits, including geologically advantageous reservoirs and economic benefits in development. In conclusion, sweet spots can encompass different types, such as geological sweet spots, engineering sweet spots, production sweet spots, and economic sweet spots. Geological sweet spots are crucial considerations in exploration and development as they determine the resource potential of sandstone reservoirs.\u003c/p\u003e \u003cp\u003eCurrently, there are three main methods used to evaluate the geological sweet spots of reservoirs. The first method is the comprehensive factor superposition method \u003csup\u003e12,13\u003c/sup\u003e, which selects key parameters in specific areas based on geological variations and integrates them to determine the sweet spot areas for shale oil. However, this method is primarily used for qualitative evaluation in a planar range and has relatively low accuracy. The second method is the seismic prediction method \u003csup\u003e14–17\u003c/sup\u003e, which analyzes rock physics data, selects sensitive seismic attribute parameters, builds a sweet spot seismic characterization model, and predicts the spatial distribution characteristics of shale oil sweet spots. However, this method has low prediction resolution and cannot meet the evaluation requirements for individual wells. The third method is the comprehensive evaluation index method \u003csup\u003e18,19\u003c/sup\u003e, which combines various evaluation factors to assess the sweet spots. However, further research is needed to improve the accuracy and resolution of this method for individual well evaluations.The quantification of key geological parameters and their weights is used in the comprehensive evaluation index method to analyze shale oil geological sweet spots. This method provides high resolution in the vertical direction but is influenced by various factors, leading to suboptimal fracturing effects. Scholars have proposed alternative evaluation methods. For instance, Chorn, et al. \u003csup\u003e7\u003c/sup\u003e conducted systematic studies on factors such as shale thickness, organic matter abundance, burial conditions, and thermal evolution, identifying highly evolved and large-scale shale formations as the primary exploration targets for sweet spots. C.H. Sondergeld, et al. \u003csup\u003e20\u003c/sup\u003e suggested evaluating and selecting based on parameters such as oil content, maturity, organic carbon content, and permeability. Frantz Jr., et al. \u003csup\u003e21\u003c/sup\u003e utilized numerical simulation techniques to demonstrate the significant impact of matrix porosity, permeability, and natural fractures on productivity. Li Jinbu, et al. \u003csup\u003e22\u003c/sup\u003e emphasized that the evaluation parameters for shale oil sweet spots mainly encompass lithology, oil saturation, and mobility. According to Yang Zhi, et al. \u003csup\u003e23\u003c/sup\u003e, the formation of sweet spots is primarily influenced by factors such as organic matter abundance, organic matter maturity, porosity, degree of fracture development, local structure, and pressure coefficient. Xie Xinong, et al. \u003csup\u003e24\u003c/sup\u003e suggest that the evaluation of geological sweet spots can be done by considering parameters such as organic carbon abundance in mudstone, thermal evolution degree of organic matter, oil saturation index, and stratigraphic pressure coefficient. In a similar vein, Zhu Rukai, et al. \u003csup\u003e10\u003c/sup\u003e evaluate geological sweet spots by establishing threshold values based on the study of parameters such as lithology, physical properties, oil saturation, and source rock characteristics. Building upon this research, Wei Yongbo, et al. \u003csup\u003e25\u003c/sup\u003e propose a method for evaluating shale oil sweet spots using five parameters, namely oil content, movable oil ratio, pressure coefficient, and permeability. Additionally, they determine the distribution characteristics of high-productivity intervals in shale formations.\u003c/p\u003e \u003cp\u003eSeveral studies have focused on predicting and evaluating geological sweet spots in tight sandstone reservoirs. Zhu Haiyan, et al. \u003csup\u003e26\u003c/sup\u003e proposed a model that considers factors like total hydrocarbon content and reservoir porosity. Mi Honggang, et al. \u003csup\u003e27\u003c/sup\u003e utilized drilling, logging, and seismic data to provide a detailed description of three-dimensional spatial sequence stratigraphy and predict 'sweet spot' reservoirs in tight sandstones. Liu Yan \u003csup\u003e28\u003c/sup\u003e evaluated existing sweet spots and proposed a method that combines accumulation and seismic research to improve gas content prediction accuracy. This paper quantitatively characterizes geological sweet spots by evaluating parameters such as porosity, permeability, and saturation. Li Liuzhong, et al. \u003csup\u003e29\u003c/sup\u003e, Hui Wei \u003csup\u003e30\u003c/sup\u003e, and Han Cheng, et al. \u003csup\u003e31\u003c/sup\u003e have also made progress in interpreting and evaluating gas-bearing tight sandstone reservoirs using various logging methods. Dai Jianquan and Lu Zhengxiang \u003csup\u003e32\u003c/sup\u003e established a quantitative relationship model between pore-throat and permeability, enhancing the evaluation of ultra-tight sandstone reservoirs. Tang Jun, et al. \u003csup\u003e33\u003c/sup\u003e discussed the effectiveness of evaluating tight sandstone reservoirs using Stoneley wave characteristic parameters under water-based mud conditions.\u003c/p\u003e \u003cp\u003eChen Bixiao and Xu Binggao \u003csup\u003e34\u003c/sup\u003e investigated the feasibility of three gas content detection techniques for evaluating tight sandstone reservoirs in the Xujiahe Formation in western Sichuan. Zhang Songyang \u003csup\u003e35\u003c/sup\u003e developed a reservoir productivity prediction formula by optimizing logging parameters through synthetic analysis of tight reservoir sections, providing a basis for preferential exploitation of low-permeability gas reservoirs. Cheng Zhigang, et al. \u003csup\u003e36\u003c/sup\u003e established evaluation criteria for gas-bearing layers based on logging responses of residual movable gas saturation variations in different lithofacies. Wen Long, et al. \u003csup\u003e37\u003c/sup\u003e formulated an index formula for comprehensive discrimination of tight sandstone gas reservoirs, considering both reservoir quality and connectivity. However, the petrophysical properties of tight sandstone reservoirs are influenced by multiple complex factors, making it difficult to effectively describe pore-permeability characteristics with a single parameter. Solely relying on pore-permeability parameters also poses challenges for the effective evaluation of tight sandstone reservoirs \u003csup\u003e38–42\u003c/sup\u003e. Therefore, this study aimed to evaluate the geological \"sweet spots\" of tight sandstone reservoirs using multiple conventional well logging curves as a basis. Factor analysis and normalization methods were employed to establish an evaluation model, supplemented by seismic data to construct a three-dimensional geological \"sweet spot\" model and analyze the distribution trends of sweet spots within the block.\u003c/p\u003e "},{"header":"Evaluation model for single well geological sweet spots","content":"\u003cp\u003eFactor Analysis\u003c/p\u003e\n\u003cp\u003eFactor Analysis is a statistical method used to study the relationships and structure among multiple variables. It aims to explore and explain the underlying structure and relationships among multiple variables. The factor analysis method is based on the following assumptions: (\u003cspan\u003e1\u003c/span\u003e) Multiple observed variables are influenced by a small number of latent factors. (\u003cspan\u003e2\u003c/span\u003e) The latent factors are independent of each other and unrelated. The basic principle of factor analysis is to transform the original variables into a smaller number of unrelated variables (factors). This transformation helps us understand the underlying dimensions and relationships among variables in the data. Factor analysis simplifies data analysis, facilitates the exploration of variable relationships, and provides valuable insights into the data. However, it is important to assess the suitability of the data, the representativeness of the sample, and the fulfillment of assumptions before using factor analysis. Factor analysis is characterized by several fundamental aspects. Firstly, it aims to reduce the dimensions of the original variables by extracting shared variance information and condensing a set of correlated variables into a smaller number of unrelated variables called factors. This simplifies the data and allows for a more concise description and interpretation. Secondly, factor analysis explores the presence of unobserved latent variables (factors) that influence the relationships among the observed variables. It helps uncover these underlying factors and understand their joint influence on the observed variables. Additionally, factor analysis not only provides information on the correlations between variables but also explains the underlying structure of these correlations. It helps us determine which variables jointly represent a concept or dimension, and the importance of variables in the factors can be assessed through factor loading coefficients. Furthermore, factor analysis allows for the interpretation and naming of each extracted factor through factor loading matrices and factor labels. Loading coefficients indicate the contribution of each variable to the factor and aid in understanding the concept or dimension represented. Lastly, rotation techniques can enhance the interpretability and interpretive power of the factors by making the relationships between factors and original variables clearer and easier to understand and interpret.\u003c/p\u003e\n\u003cp\u003eThe main objective of log interpretation in geological work is to establish a connection between well logging data and geological information. This involves transforming well logging data, which includes parameters like resistivity, natural gamma, sonic travel time, and rock bulk density, into geological information using appropriate methods. For example, natural gamma and spontaneous potential curves are effective in reflecting lithology and clay content of reservoirs. Additionally, resistivity values in gas-bearing intervals are higher than those in water-bearing intervals, and as gas saturation increases, resistivity shows a clear positive anomaly and a significant increasing trend in values. Reservoir characteristics often require the use of multiple well logging parameters, making the interpretation work complex and time-consuming. To simplify the analysis process, factor analysis can be employed. Factor analysis reduces the dimensionality of multiple variables and simplifies the analysis while retaining the essential data information. Therefore, in our study, we propose using factor analysis to identify and evaluate tight sandstone reservoirs with a reduced number of factor parameters.\u003c/p\u003e\n\u003cp\u003eStandard deviation normalization processing\u003c/p\u003e\n\u003cp\u003eIn this study, we chose specific well logging parameters (sonic travel time, natural gamma, density, well diameter, phase resistivity, amplitude resistivity, and average neutron porosity) that are known to be influenced by reservoir characteristics. To ensure accurate analysis, we applied standard deviation normalization to the well logging data. This was necessary because the well logging sequences varied in dimensions and had significant differences in numerical values.\u003c/p\u003e\n\u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$s=\\sqrt {\\frac{{\\left[ {{{\\left( {{a_1} - \\overline {a} } \\right)}^2}+{{\\left( {{a_2} - \\overline {a} } \\right)}^2}+ \\ldots +{{\\left( {{a_N} - \\overline {a} } \\right)}^2}} \\right]}}{N}}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${Z_i}=\\frac{{({a_i} - \\overline {a} )}}{s}$$\u003c/div\u003e\n \u003cdiv\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong them, \u003cem\u003es\u003c/em\u003e represents the standard deviation, \u003cem\u003ea\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e represents the sample data for i\u0026thinsp;=\u0026thinsp;1,...,\u003cem\u003eN\u003c/em\u003e, represents the sample mean, \u003cem\u003eN\u003c/em\u003e represents the sample size, and represents the normalized sample data. Factor analysis is employed to extract factors and establish a geological \u0026quot;sweet spot\u0026quot; evaluation model for sandstone reservoirs.\u003c/p\u003e\n\u003cp\u003eData validation\u003c/p\u003e\n\u003cp\u003eBefore conducting factor analysis, it is necessary to validate the data to determine if it is suitable for analysis using this method. Therefore, the data is subjected to KMO and Bartlett\u0026apos;s tests to assess the feasibility of factor analysis.\u003c/p\u003e\n\u003cp\u003eKMO test:\u003c/p\u003e\n\u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$M=\\frac{R}{{R+Q}}=\\frac{{\\sum\\nolimits_{{i=1}}^{N} {\\sum\\nolimits_{{j=1}}^{N} {{r_{ij}}^{2}} } }}{{\\sum\\nolimits_{{i=1}}^{N} {\\sum\\nolimits_{{j=1}}^{N} {{r_{ij}}^{2}} } +\\sum\\nolimits_{{i=1}}^{N} {\\sum\\nolimits_{{j=1}}^{N} {{q_{ij}}^{2}} } }}$$\u003c/div\u003e\n \u003cdiv\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn this equation, \u003cem\u003eM\u003c/em\u003e represents the KMO value, \u003cem\u003eR\u003c/em\u003e and \u003cem\u003eQ\u003c/em\u003e are the sums of squares of all feature Pearson correlation coefficients and partial correlation coefficients, \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the Pearson correlation coefficient of the variables, \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the partial correlation coefficient controlling for the remaining variables, and \u003cem\u003eN\u003c/em\u003e represents the sample size.\u003c/p\u003e\n\u003cp\u003eBartlett\u0026apos;s test:\u003c/p\u003e\n\u003cdiv id=\"Equ4\"\u003e\n \u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$${\\mathbf{C}}=\\left[ {\\begin{array}{*{20}{c}} {{r_{11}}}\u0026amp;{{r_{12}}}\u0026amp; \\ldots \u0026amp;{{r_{1N}}} \\\\ {{r_{21}}}\u0026amp;{{r_{22}}}\u0026amp; \\ldots \u0026amp;{{r_{2N}}} \\\\ \\vdots \u0026amp; \\vdots \u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{r_{N1}}}\u0026amp;{{r_{N2}}}\u0026amp; \\ldots \u0026amp;{{r_{NN}}} \\end{array}} \\right]$$\u003c/div\u003e\n \u003cdiv\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn this equation, \u003cem\u003eC\u003c/em\u003e represents the correlation coefficient matrix. Based on the degrees of freedom and the test statistic, the associated probability of Bartlett\u0026apos;s test is approximated by consulting the chi-square distribution table. By considering the relationship between the associated probability and the significance level, it can be determined if factor analysis is appropriate.\u003c/p\u003e\n\u003cp\u003eThe KMO value is used to determine the suitability of using factor analysis. A KMO value above 0.9 is considered highly suitable, between 0.7 and 0.9 is appropriate, between 0.6 and 0.7 is still acceptable, between 0.5 and 0.6 indicates poor suitability, and a value below 0.5 suggests that factor analysis should be abandoned.\u003c/p\u003e\n\u003cp\u003eIn the context of Bartlett\u0026apos;s test, if the \u003cem\u003eP\u003c/em\u003e-value is less than 0.05 and the null hypothesis is rejected, it suggests that factor analysis can be performed. On the other hand, if the null hypothesis is not rejected, it indicates that these variables may provide independent information and are not suitable for factor analysis.\u003c/p\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKMO Test and Bartlett\u0026apos;s Test Results. Note: ***, **, and * represent the significance levels of 1%, 5%, and 10% respectively\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKMO value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBartlett sphericity test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140871.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed\u003cem\u003ef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the results of KMO test and Bartlett\u0026apos;s test in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, it can be concluded that the selected data is suitable for conducting factor analysis.\u003c/p\u003e\n\u003cp\u003eFactor number determination\u003c/p\u003e\n\u003cp\u003eAfter standardizing the standard deviation of logging curves, such as horizontal shear wave travel time (DTSM), compressional wave travel time (DTCO), natural gamma ray (GR), density (RHOB), borehole diameter (DCAV), phase resistivity (P40H), amplitude resistivity (A40H), and average neutron porosity (TNPH), for the target reservoirs in the DF block, a correlation coefficient matrix is constructed using the standardized data. This matrix is then used to calculate all eigenvalues (\u003cem\u003e\u0026lambda;\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003e\u0026lambda;\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, ..., \u003cem\u003e\u0026lambda;\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e) and eigenvectors (\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, ..., \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e). The variance contribution rate and cumulative variance contribution rate are also computed. Based on the criterion that eigenvalues should be greater than 1, several factors are extracted as common factors. The final results are presented in Table \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eVariance Explained.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eTotal variance explained\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eComponents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVariance explained before rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVariance explained after rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvalues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance explained ratio(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative variance explained ratio(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvalues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance explained ratio(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative variance explained ratio(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the principle of factor analysis where eigenvalues are greater than 1, the number of factors can be determined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.\u003c/p\u003e\n\u003cp\u003eIdentification model establishment\u003c/p\u003e\n\u003cp\u003eBased on all the calculated eigenvectors, the loading matrix A\u003csup\u003e0\u003c/sup\u003e is constructed. The rotated loading matrix A is obtained using the maximum variance method, and the factor score matrix \u003cem\u003eG\u003c/em\u003e is calculated using the regression estimation method.\u003c/p\u003e\n\u003cdiv id=\"Equ5\"\u003e\n \u003cdiv id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$${{\\mathbf{A}}^0}=\\left[ {\\begin{array}{*{20}{c}} {{a^0}_{{11}}}\u0026amp;{{a^0}_{{12}}}\u0026amp; \\ldots \u0026amp;{{a^0}_{{1H}}} \\\\ {{a^0}_{{21}}}\u0026amp;{{a^0}_{{22}}}\u0026amp; \\ldots \u0026amp;{{a^0}_{{2H}}} \\\\ \\vdots \u0026amp; \\vdots \u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{a^0}_{{N1}}}\u0026amp;{{a^0}_{{N1}}}\u0026amp; \\ldots \u0026amp;{{a^0}_{{NH}}} \\end{array}} \\right]=\\left[ {\\begin{array}{*{20}{c}} {{\\mu _{11}}\\sqrt {{\\lambda _1}} }\u0026amp;{{\\mu _{21}}\\sqrt {{\\lambda _2}} }\u0026amp; \\ldots \u0026amp;{{\\mu _{H1}}\\sqrt {{\\lambda _H}} } \\\\ {{\\mu _{21}}\\sqrt {{\\lambda _1}} }\u0026amp;{{\\mu _{22}}\\sqrt {{\\lambda _2}} }\u0026amp; \\ldots \u0026amp;{{\\mu _{H2}}\\sqrt {{\\lambda _H}} } \\\\ \\vdots \u0026amp; \\vdots \u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{\\mu _{1N}}\\sqrt {{\\lambda _1}} }\u0026amp;{{\\mu _{2N}}\\sqrt {{\\lambda _2}} }\u0026amp; \\ldots \u0026amp;{{\\mu _{HN}}\\sqrt {{\\lambda _1}} } \\end{array}} \\right]$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ6\"\u003e\n \u003cdiv id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$${\\mathbf{A}}=\\left[ {\\begin{array}{*{20}{c}} {{a_{11}}}\u0026amp;{{a_{12}}}\u0026amp; \\ldots \u0026amp;{{a_{1H}}} \\\\ {{a_{21}}}\u0026amp;{{a_{22}}}\u0026amp; \\ldots \u0026amp;{{a_{2H}}} \\\\ \\vdots \u0026amp; \\vdots \u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{a_{N1}}}\u0026amp;{{a_{N1}}}\u0026amp; \\ldots \u0026amp;{{a_{NH}}} \\end{array}} \\right]$$\u003c/div\u003e\n \u003cdiv\u003e6\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ7\"\u003e\n \u003cdiv id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$${\\mathbf{G}}={{\\mathbf{C}}^{ - 1}}{\\mathbf{A}}={\\left[ {\\begin{array}{*{20}{c}} {{r_{11}}}\u0026amp;{{r_{12}}}\u0026amp; \\ldots \u0026amp;{{r_{1N}}} \\\\ {{r_{21}}}\u0026amp;{{r_{22}}}\u0026amp; \\ldots \u0026amp;{{r_{2N}}} \\\\ \\vdots \u0026amp; \\vdots \u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{r_{N1}}}\u0026amp;{{r_{N1}}}\u0026amp; \\ldots \u0026amp;{{r_{NN}}} \\end{array}} \\right]^{ - 1}}\\left[ {\\begin{array}{*{20}{c}} {{a_{11}}}\u0026amp;{{a_{12}}}\u0026amp; \\ldots \u0026amp;{{a_{1H}}} \\\\ {{a_{21}}}\u0026amp;{{a_{22}}}\u0026amp; \\ldots \u0026amp;{{a_{2H}}} \\\\ \\vdots \u0026amp; \\vdots \u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{a_{N1}}}\u0026amp;{{a_{N1}}}\u0026amp; \\ldots \u0026amp;{{a_{NH}}} \\end{array}} \\right]$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn this equation, \u003cem\u003ea\u003c/em\u003e\u003csup\u003e0\u003c/sup\u003e\u003csub\u003eNH\u003c/sub\u003e represents the matrix element before rotation, \u003cem\u003ea\u003c/em\u003e\u003csub\u003eNH\u003c/sub\u003e represents the matrix element after rotation, and \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003eHN\u003c/sub\u003e represents the eigenvector.\u003c/p\u003e\n\u003cp\u003eAccording to the factor loading heat map (Fig. \u003cspan\u003e1\u003c/span\u003e), it can be concluded that F1 is mainly influenced by DCAV, TNPH, and DTSM. An increase in DCAV and TNPH values and a decrease in RHOB values lead to an increase in F1, indicating that F1 mainly reflects the porosity of the reservoir, making it the porosity factor. F2 is primarily influenced by A40H and P40H, showing a positive correlation with them, indicating that F2 mainly reflects the gas content of the reservoir, making it the gas factor. F3 is mainly influenced by GR and RHOB, reflecting the clay content of the reservoir, making it the clay factor.\u003c/p\u003e\n\u003cp\u003eThe factor score coefficients obtained by composing the loading matrix with eigenvectors are shown in Table \u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFactor score coefficient table.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePorosity factor (F\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGas factor (F\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClay factor (F\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA40H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP40H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCAV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRHOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDTCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDTSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eF1\u0026thinsp;=\u0026thinsp;0.137\u0026times;A40H\u0026thinsp;+\u0026thinsp;0.095\u0026times;P40H\u0026thinsp;+\u0026thinsp;0.464\u0026times;DCAV\u0026thinsp;+\u0026thinsp;0.196\u0026times;GR\u0026thinsp;+\u0026thinsp;0.014\u0026times;RHOB\u0026thinsp;+\u0026thinsp;0.303\u0026times;TNPH\u0026thinsp;+\u0026thinsp;0.077\u0026times;DTCO\u0026thinsp;+\u0026thinsp;0.347\u0026amp;ti\u003c/h3\u003e\n\u003ch2\u003eF\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.501\u0026times;A40H\u0026thinsp;+\u0026thinsp;0.474\u0026times;P40H\u0026thinsp;+\u0026thinsp;0.261\u0026times;DCAV\u0026thinsp;+\u0026thinsp;0.01\u0026times;GR-0.032\u0026times;RHOB-0.021\u0026times;TNPH-0.156\u0026times; DTCO \u0026minus;\u0026thinsp;0.011\u0026times;DTSM (\u003cspan\u003e9\u003c/span\u003e)\u003c/h2\u003e\n\u003ch2\u003eF\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.073\u0026times;A40H\u0026thinsp;+\u0026thinsp;0.053\u0026times;P40H\u0026thinsp;+\u0026thinsp;0.184\u0026times;DCAV\u0026thinsp;+\u0026thinsp;0.378\u0026times;GR\u0026thinsp;+\u0026thinsp;0.474\u0026times;RHOB\u0026thinsp;+\u0026thinsp;0.009\u0026times;TNPH-0.444\u0026times;DTCO-0.138\u0026times;DTSM (\u003cspan\u003e10\u003c/span\u003e)\u003c/h2\u003e\n\u003cp\u003eBased on the positive and negative correlations of each factor with geological compressibility, the values of each factor are normalized as follows:\u003c/p\u003e\n\u003cp\u003ePositive indicator calculation formula:\u003c/p\u003e\n\u003cdiv id=\"Equ8\"\u003e\n \u003cdiv id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$$Y=\\frac{{a - {a_{\\hbox{min} }}}}{{{a_{\\hbox{max} }} - {a_{\\hbox{min} }}}}$$\u003c/div\u003e\n \u003cdiv\u003e11\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eNegative indicator calculation formula:\u003c/p\u003e\n\u003cdiv id=\"Equ9\"\u003e\n \u003cdiv id=\"FileID_Equ9\" name=\"EquationSource\"\u003e$$Y=\\frac{{{a_{\\hbox{max} }} - a}}{{{a_{\\hbox{max} }} - {a_{\\hbox{min} }}}}$$\u003c/div\u003e\n \u003cdiv\u003e12\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn this equation, \u003cem\u003eS\u003c/em\u003e represents the normalized parameter value, \u003cem\u003eY\u003c/em\u003e represents the original parameter value, \u003cem\u003ea\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e represents the minimum value of the parameter, and \u003cem\u003ea\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e represents the maximum value of the parameter.\u003c/p\u003e\n\u003cp\u003eThe weights of each factor are obtained based on the variance explained ratio and cumulative variance explained ratio, as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFactor weight analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariance explained ratio after rotation (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCumulative variance explained ratio after rotation (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe evaluation model for tight sandstone reservoirs can be derived from the above:\u003c/p\u003e\n\u003ch3\u003eF\u0026thinsp;=\u0026thinsp;0.38\u0026times;F1\u0026thinsp;+\u0026thinsp;0.35\u0026times;F2\u0026thinsp;+\u0026thinsp;0.27\u0026times;F 3\u003c/h3\u003e\n\u003cp\u003e13\u003c/p\u003e"},{"header":"Model validation","content":"\u003cp\u003eIn order to ensure the accuracy of the geological sweet spot evaluation model for this well, it is now being validated through well WC9-7-11.\u003c/p\u003e\u003cp\u003eWC9-7-11 well is a vertical well located in the southern part of Wenchang A sag. The well logging interpretation of the fracturing test target layer reveals the following characteristics: water saturation ranges from 51.8–63.4%, clay content ranges from 1.0–24.8%, porosity ranges from 6.5–10.8%, and permeability ranges from 0.18mD to 0.50mD. The fracturing interval spans from a depth of 4190m to 4215.7m, while the perforation interval ranges from 4200.2m to 4215.7m. Based on the aforementioned model, the geological sweet spot curve of the well is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The figure clearly indicates that the perforation interval exhibits a relatively high geological sweet spot index. This suggests that the model possesses a certain level of accuracy, as the optimal sweet spot area should be selected when determining the perforation location.\u003c/p\u003e\u003cp\u003eThree-dimensional geological evaluation model\u003c/p\u003e\u003cp\u003eThe establishment of a three-dimensional geological sweet spot evaluation model relies on seismic-related data and well logging data to determine the key parameters of the geological sweet spot in the target layer. These parameters, including porosity factor, gas content factor, clay content factor, and geological compressibility, are then used to generate a three-dimensional distribution map. Thus, calculating the corresponding parameters is crucial in developing the three-dimensional geological sweet spot evaluation model.\u003c/p\u003e\u003cp\u003eIn the evaluation of the three-dimensional geological sweet spot, simulation and inversion techniques heavily rely on three-dimensional seismic inversion calculations. Three-dimensional seismic data is a type of densely and structurally organized data that allows for the identification of spatial variations in different sedimentary environments, lithology combinations, and elastic parameters \u003csup\u003e43\u003c/sup\u003e. To achieve this, we employ the principles of sedimentology to select relevant well samples by assessing the similarity of seismic waveforms. By considering the spatial distribution distance and curve distribution characteristics of the samples, an initial model is established. Additionally, we propose the concept of 'phase cutoff frequency' to expand the frequency range, based on the analysis of elastic parameter curves within the same lithofacies. Statistical analysis of the longitudinal wave impedance of the sample wells is conducted to establish a prior probability function. The initial model is then matched and filtered with the seismic wave impedance volume to generate a likelihood function, gradually removing high-frequency components. Using Bayesian theory, the likelihood distribution is combined with the prior distribution to obtain the posterior probability distribution, which serves as the objective function. The samples selected based on waveform indication exhibit a strong spatial correlation, and the Metropolis-Hastings sampling algorithm is employed to sample the posterior probability distribution. The solution that maximizes the objective function is chosen as a feasible random realization, and the average of multiple feasible realizations is calculated as the expected output \u003csup\u003e43\u003c/sup\u003e. Therefore, this inversion method effectively utilizes the lateral variations of seismic waveforms, resulting in improved vertical resolution and reduced randomness, ultimately enabling more accurate simulation of the desired parameters.\u003c/p\u003e\u003cp\u003eThis calculation process relies on a comprehensive seismic interpretation, utilizing well-established well data and seismic information within the study area. The waveform indication simulation technique is employed to replicate the formation parameters of the target layer as per the procedure outlined in Fig.\u0026nbsp;3 \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo ensure clarity and flow, the following steps were taken in the study: Step 1: A seismic interpreted horizon-based isochronous framework model was established. Step 2: The predicted seismic waveforms were compared and analyzed with the waveforms near the wells. Wells with similar waveform characteristics were selected as valid samples. The curve structure on these sample wells was analyzed to establish an initial model. Step 3: The initial model was filtered using seismic mid-frequency impedance as a standard. High-frequency information that did not conform to the standard was removed. A likelihood function was constructed, and random simulations were performed repeatedly to ensure that the simulated results simultaneously conformed to the seismic mid-frequency impedance and the well curve structure characteristics. Step 4: The final simulation results were outputted. These results can be randomly realized or the mean of multiple realizations can be taken as the expected value.\u003c/p\u003e\u003cp\u003eIn order to accurately reflect the conditions of stratigraphic deposition, it is crucial to analyze the contact relationships between the different layers. If the layering features are clearly visible and the interpretation of horizons is consistently continuous, it is important to consider the stratigraphic characteristics when establishing the geological model. Therefore, the selection of stratigraphic contact relationships should be defined as parallel to the concept of 'equally divided'. By employing the aforementioned methods, it is possible to obtain an initial inversion model that provides a more precise representation of the structural morphology and internal structure of the target layer. The profile of the initial model is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe establishment of the initial model involves several steps. Firstly, well logging data is used to perform seismic synthetic record processing, which helps in obtaining accurate time-depth relationships and ensuring the geological horizon calibration is reasonable. This step is crucial for maintaining the accuracy of well time-depths during inversion. Secondly, a framework model of the stratigraphic structure is established based on seismic structural interpretation results. Geometric control structures between the strata are defined to create a detailed stratigraphic model. In this case, the stratigraphic contact relationship is selected as 'equally divided'. Finally, the intermediate parameter curves of the control points are combined with the detailed stratigraphic model. A three-dimensional spatial curve interpolation method is applied to generate the three-dimensional image of the inverted initial model.\u003c/p\u003e"},{"header":"Case study","content":"\u003cp\u003eDF1-1 structure is located in the northern area of the central depression of the Yinggehai Basin. It is a mid-level (HL Group 1) anticlinal closure that has developed on the background of the Neogene bottom sag. The porosity of the HL Group reservoirs in the DF area is mainly distributed between 15% and 25%, and the permeability generally ranges from (0.1 to 100)×10\u003csup\u003e− 3\u003c/sup\u003eµm\u003csup\u003e2\u003c/sup\u003e. The reservoirs primarily consist of mesoporous and medium to low-permeability reservoirs. However, there are significant variations in reservoir properties. In the DF13-2 area, the reservoir properties are relatively good, with porosity mainly distributed between 15% and 20%, and permeability mainly distributed between (1.0 to 100)×10\u003csup\u003e− 3\u003c/sup\u003eµm\u003csup\u003e2\u003c/sup\u003e. Moreover, there are also notable differences in reservoir properties among different wells within the same area. For instance, in the case of DF1-1-13, the well logging interpretation covers a total depth of 76.4m with 7 layers. This includes 29.8m of gas-bearing layers in 2 sub-layers, 13.3m of sub-gas layers in 2 sub-layers, and 33.3m of dry layers in 3 sub-layers. The comprehensive interpretation of the entire well amounts to 236.4m with 17 layers. This includes 110.8m of gas-bearing layers in 5 sub-layers, 92.3m of sub-gas layers in 10 sub-layers, and 33.3m of dry layers in 3 sub-layers.\u003c/p\u003e\u003cp\u003eBased on the established geological sweet spot model discussed in the previous section, the three-factor values were calculated individually using the logging curve data from well DF1-1-13 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Subsequently, the geological sweet spot index of the well was determined. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the generation of the three factor curves and the geological sweet spot index curve. The geological sweet spot index of well DF1-1-13 predominantly ranges between 0.3 and 0.7, exhibiting notable vertical variations.\u003c/p\u003e\u003cp\u003eThe geological sweet spot curves were analyzed for their distribution range using data from three wells (DF1-1-12, DF13-1-10, and DF13-1-7) within the DF11-2 block, where well DF1-1-13 is located. The analysis, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, reveals that the porosity factor values of the four wells range between 0.1 and 0.6, with the highest concentration between 0.2 and 0.4. Similarly, the mud factor values range from 0.2 to 0.7, with the majority falling between 0.3 and 0.6. The water content factor values show a distribution range of 0.1 to 0.7, with the majority falling between 0.2 and 0.6. Lastly, the geological compressibility values range from 0.2 to 0.6, with the majority falling between 0.3 and 0.5. Overall, the distribution trends of the porosity factor, gas content factor, mud factor, and geological compressibility curve of the four wells are consistent, with similar peak ranges. These findings meet the requirements of curve standardization and fulfill the standardization needs for data inversion.\u003c/p\u003e\u003cp\u003eFrom the extracted along-layer slices (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), The porosity factor values in the HL group are primarily distributed between 0.3–0.4, with a concentrated high-value area in the southern middle section of the study area. Along the layers, the gas content factor values in the HL group are mainly distributed between 0.24–0.3, with relatively lower values in the central and southern parts of the study area. However, the range of gas content factor values is small, resulting in insignificant numerical differences. The mud factor values in the HL group are mainly distributed between 0.3–0.6, with a localized high-value area in the southwestern part of the study area. The values of geological compressibility in the HL group are primarily distributed between 0.25–0.45, with a localized high-value area in the southwestern part of the study area.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA geological sweet spot model for tight sandstone reservoirs was established using factor analysis and the principle of normalization, based on the data of eight conventional well logging curves. The model quantitatively evaluated the vertical distribution of the reservoir's geological sweet spots and was verified to be highly accurate through well examples.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA three-dimensional geological sweet spot evaluation model is established by combining seismic-related data and logging data. This model is used to invert the key parameters of the geological sweet spots in the target layer and quantitatively evaluate the lateral distribution of the reservoir's geological sweet spots.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data used is confidential.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the Scientific and Technological Research Program of Chongqing Municipal Education Commission (GN:KJQN202201521); the Natural Science Foundation Project of Chongqing Science and Technology Bureau (GN:cstc2020jcyj-zdxmX0001) and the General project of Chongqing Natural Science Foundation, China (GN:cstc2021jcyj-msxmX0790), for their financial support.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Te frst draf of the manuscript was written by L.Z, manuscript review and editing were performed by J.X, J.L, C.W and H.Z. H.X. advised the students and corrected the manuscript. All authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eTe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJin, Z. J. \u0026amp; Zhang, F. Q. Status and major advancements in study of hydrocarbon migration. \u003cem\u003eOil \u0026amp; Gas Geology\u003c/em\u003e. \u003cstrong\u003e26\u003c/strong\u003e, 263-270, https://doi.org/10.3321/j.issn:0253-9985.2005.03.001 (2005).\u003c/li\u003e\n\u003cli\u003eZou, C. N.\u003cem\u003e et al.\u003c/em\u003e New advances in global unconventional oil and gas exploration and theoretical research. \u003cem\u003eChina Institute of Petroleum Exploration and Development\u003c/em\u003e. (2013).\u003c/li\u003e\n\u003cli\u003eZou, C. N.\u003cem\u003e et al.\u003c/em\u003e Geological concepts, characteristics, resource potential and key techniques of unconventional hydrocarbon: On unconventional petroleum geology \u003cem\u003ePetroleum Exploration and Development.\u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, 385-399,454, https://doi.org/10.11698/ped.2013.04.01 (2013).\u003c/li\u003e\n\u003cli\u003eZou, C. N. \u003cem\u003eet al.\u003c/em\u003e Types, characteristics, genesis and prospects of conventional and unconventional hydrocarbon accumulations: taking tight oil and tight gas in China as an instance. \u003cem\u003eActa Petrolei Sinica.\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e, 173-187, https://doi.org/10.7623/syxb201202001 (2012).\u003c/li\u003e\n\u003cli\u003eJia, C. Z., Zou, C. N., Li, J. Z., Li, D. H. \u0026amp; Zhen, M. Assessment criteria, main types, basic features and resource prospects of the tight oil in China. \u003cem\u003eActa Petrolei Sinica.\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e, 343-350, https://doi.org/10.7623/syxb201203001 (2012).\u003c/li\u003e\n\u003cli\u003ePrise, G. J., Stewart, D. R., Bird, T. M., Holland, B. \u0026amp; Wilson, W. W. Successful completion operations on Ravenspurn North Development. \u003cem\u003eSPE Offshore Europe\u003c/em\u003e. https://doi.org/10.2118/26744-MS (1993).\u003c/li\u003e\n\u003cli\u003eChorn, L., Yarus, J., Rosario-Davis, S. d. \u0026amp; Pitcher, J. Identification of Shale Sweet Spots Using Key Property Estimates from Log Analysis and Geostatistics. https://doi.org/10.1190/urtec2013-154 (2013).\u003c/li\u003e\n\u003cli\u003eHashmy, K., Abueita, S., Petroleum, A., Barnett, C. \u0026amp; Jonkers, J. Log-Based Identification of Sweet Spots for Effective Fracs in Shale Reservoirs. \u003cem\u003eCanadian Unconventional Resources Conference\u003c/em\u003e. https://doi.org/10.2118/149278-MS (2011).\u003c/li\u003e\n\u003cli\u003eZhou, D. H. \u0026amp; Jiao, F. Z. Evaluation and prediction of shale gas sweet spots: a case study in JurassicofJiannan area, Sichuan Basin. \u003cem\u003ePetroleum Geology \u0026amp; Experiment.\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e, 109-114, https://doi.org/10.3969/j.issn.1001-6112.2012.02.001 (2012).\u003c/li\u003e\n\u003cli\u003eZhu, R. K.\u003cem\u003e et al.\u003c/em\u003e Mechanism for generation and accumulation of continental tight oil in China. \u003cem\u003eOil \u0026amp; Gas Geology\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, 1168-1184, https://doi.org/10.11743/ogg20190602 (2019).\u003c/li\u003e\n\u003cli\u003eChen, F. L., Tong, M., Yan, L., Liu, L. F. \u0026amp; Wang, S. J. Sweetness evaluation method for \u0026ldquo;sweet spot\u0026rdquo; of tight oil reservoir. \u003cem\u003eSpecial Oil \u0026amp; Gas Reservoirs.\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 12-17, https://doi.org/10. 3969/j. issn. 1006－6535. 2017. 02. 003 (2017).\u003c/li\u003e\n\u003cli\u003eZou, C. N.\u003cem\u003e et al.\u003c/em\u003e Formation mechanism, geological characteristics and development strategy of nonmarine shale oil in China. \u003cem\u003ePetroleum Exploration and Development.\u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, 14-26 (2013c).\u003c/li\u003e\n\u003cli\u003eYang, Z.\u003cem\u003e et al.\u003c/em\u003e Formation, distribution and resource potential of the \u0026quot;sweet areas (sections)\u0026quot; of continental shale oil in China. \u003cem\u003eMarine \u0026amp; Petroleum Geology\u003c/em\u003e.\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e102\u003c/strong\u003e, 48-60, https://doi.org/10.1016/j.marpetgeo.2018.11.049 (2019).\u003c/li\u003e\n\u003cli\u003eDong, Y., Xu, D. S., Qian, G. B., Wang, X. H. \u0026amp; Dai, Y. J. Shale Oil \u0026ldquo;Sweet－Spot\u0026rdquo; Prediction in Jimusar Sag. \u003cem\u003eSpecial Oil \u0026amp; Gas Reservoirs.\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e, 54-59, https://doi.org/10.3969/j.issn.1006-6535.2020.03.009 (2020).\u003c/li\u003e\n\u003cli\u003ePan, R. F., Chen, M. L., Zhang, C. M. \u0026amp; Pan, J. Seismic prediction of Paleogene shale oil \u0026quot;sweet spots\u0026quot; and its influencing factor analysis in the Bonan sub-sag, Jiyang depression. \u003cem\u003eEarth Science Frontiers.\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 142-154, https://doi.org/10.13745/j.esf.sf.2018.5.26 (2018).\u003c/li\u003e\n\u003cli\u003eGao, Q. J.\u003cem\u003e et al.\u003c/em\u003e Well-seismic joint technology for quantitative evaluation of \u0026ldquo;sweet spot\u0026rdquo; in continental shale oil：A case study of Lower Es\u003csub\u003e3\u003c/sub\u003e Member of Luojia area in Jiyang Depression. \u003cem\u003ePetroleum Geology and Recovery Efficiency.\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 165-173, https://doi.org/10.13673/j.cnki.cn37-1359/te.2019.01.017 (2019).\u003c/li\u003e\n\u003cli\u003eGuo, X. G.\u003cem\u003e et al.\u003c/em\u003e Evaluation and application of key technologies of \u0026quot;sweet area\u0026quot; of shale oil in Junggar Basin: Case study of Permain Lucaogou Formation in Jimusar Depression. \u003cem\u003eNatural Gas Geoscience.\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 1168-1179, https://doi.org/10.11764/j.issn.1672-1926.2019.05.020 (2019).\u003c/li\u003e\n\u003cli\u003eZhang, P. F.\u003cem\u003e et al.\u003c/em\u003e Identification method of sweet spot zone in lacustrine shale oil reservoir and its application: A case study of the Shahejie Formation in Dongying Sag, Bohai Bay Basin. \u003cem\u003eOil \u0026amp; Gas Geology\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, 1339-1350, https://doi.org/10.11743/ogg20190618 (2019).\u003c/li\u003e\n\u003cli\u003eZhao. X. Z.\u003cem\u003e et al.\u003c/em\u003e Geological characteristics and exploration breakthrough of shale oil in Member 3 of Shahejie Formation of Qibei subsag, Qikou sag. \u003cem\u003eActa Petrolei Sinica.\u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e, 643-657, https://doi.org/10.7623/syxb202006001 (2020).\u003c/li\u003e\n\u003cli\u003eC.H. Sondergeld, K.E. Newsham, T. Comisky, M.C. Rice \u0026amp; C.S. Rai. Petrophysical Considerations in Evaluating and Producing Shale Gas Resources. \u003cem\u003eSPE Unconventional Gas Conference\u003c/em\u003e. https://doi.org/10.2118/131768-MS (2010).\u003c/li\u003e\n\u003cli\u003eFrantz Jr., J. H.\u003cem\u003e et al.\u003c/em\u003e Evaluating Barnett Shale Production Performance Using an Integrated Approach. \u003cem\u003eSPE Annual Technical Conference and Exhibition\u003c/em\u003e. https://doi.org/ 10.2118/96917-MS (2012).\u003c/li\u003e\n\u003cli\u003eLi, J. B.\u003cem\u003e et al.\u003c/em\u003e A new method for predicting sweet spots of shale oil using conventional well logs. \u003cem\u003eMarine \u0026amp; Petroleum Geology\u003c/em\u003e. \u003cstrong\u003e113\u003c/strong\u003e, 104097, https://doi.org/10.1016/j.marpetgeo.2019.104097 (2020).\u003c/li\u003e\n\u003cli\u003eYang, Z.\u003cem\u003e et al.\u003c/em\u003e Formation conditions and \u0026ldquo;sweet spot \u0026rdquo; evaluation of tight oil and shale oil. \u003cem\u003ePetroleum Exploration and Development.\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 555-565, https://doi.org/10.11698/PED.2015.05.02 (2015).\u003c/li\u003e\n\u003cli\u003eXie, X. N.\u003cem\u003e et al.\u003c/em\u003e Differential enrichment mechanism and key technology of shale gas in complex areas of south China. \u003cem\u003eGeoscience\u003c/em\u003e. \u003cstrong\u003e42\u003c/strong\u003e, 1045-1056, https://doi.org/10.3799/dqkx.2017.084 (2017).\u003c/li\u003e\n\u003cli\u003eWei, Y. B.\u003cem\u003e et al.\u003c/em\u003e Comprehensive evaluation method of sweet spot zone in lacustrine shale oil reservoir and its application: A case study of shale oil in lower 1st member of the Shahejie formation in the Raoyang sag. \u003cem\u003eJournal of China University of Mining \u0026amp; Technology.\u003c/em\u003e\u003cstrong\u003e50\u003c/strong\u003e, 813-824, https://doi.org/10.13247/j.cnki.jcumt.001223 (2021).\u003c/li\u003e\n\u003cli\u003eZhu, H. Y., Gong, D. \u0026amp; Zhang, B. Amulti-scale geology-engineering sweet spot evaluation method for tight sandstone gas reservoirs. \u003cem\u003eNatural Gas Industry.\u003c/em\u003e\u003cstrong\u003e43\u003c/strong\u003e, 76-86, https://doi.org/10.3787/j.issn.1000-0976.2023.06.007 (2023).\u003c/li\u003e\n\u003cli\u003eMi, H. G., Zhang, B., Zhu, G. H., Su, Y. \u0026amp; Zhang, H. F. Geoological characteristics and development potential analysis of Linxing tight sandstone gas reservoir. \u003cem\u003eSpecial Oil \u0026amp; Gas Reservoirs.\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 65-72, https://doi.org/10.3969/j.issn.1006-6535.2022.06.008 (2022).\u003c/li\u003e\n\u003cli\u003eLiu, Y. Integrated sweets spots evaluation technology for tight sandstone gas reservoirs in Zhongjiang Gas Field. \u003cem\u003eJournal of Southwest Petroleum University(Science \u0026amp; Technology Edition).\u003c/em\u003e\u003cstrong\u003e44\u003c/strong\u003e, 12-25, https://doi.org/10.11885/j.issn.1674-5086.2022.01.27.03 (2022).\u003c/li\u003e\n\u003cli\u003eLi, L. Z., Han, C., Wang, P. \u0026amp; Deng, C. H. Logging evaluation method of tight-gas-sandstone reservoirs in Northern Mountain Front. \u003cem\u003eTuha Oil \u0026amp; Gas.\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 257-262, https://doi.org/CNKI:SUN:THYQ.0.2010-02-017 (2010).\u003c/li\u003e\n\u003cli\u003eHui, W. Logging evaluation method of tight sandstone reservoir in Sichuan basin. \u003cem\u003ePetroleum Geology and Engineering.\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 80-83，148, https://doi.org/CNKI:SUN:SYHN.0.2015-02-022 (2015).\u003c/li\u003e\n\u003cli\u003eHan, C., Gao, X., Wang, J. X., Wang, S. Z. \u0026amp; Pan, H. F. Logging evaluation on tight sands reservoir in Tuha basin. \u003cem\u003eTuha Oil \u0026amp; Gas.\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 1-7, https://doi.org/CNKI:SUN:THYQ.0.2012-01-003 (2012).\u003c/li\u003e\n\u003cli\u003eDai, J. Q. \u0026amp; Lu, Z. X. Assessment of Hypercompact SandstoneReservoirs of the Upper Triassic Xujiahe Formation in West Sichuan. \u003cem\u003eActa Geologica Sichuan.\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 450-453, https://doi.org/10.3969/j.issn.1006-0995.2010.04.020 (2010).\u003c/li\u003e\n\u003cli\u003eTang, J., Zhang, C. G. \u0026amp; Cai, D. Y. Effectiveness evaluation of tight sandstone reservoir based on stoneley wave characteristic parameters. \u003cem\u003eJournal of Oil and Gas Technology.\u003c/em\u003e\u003cstrong\u003e35\u003c/strong\u003e, 79-85，77, https://doi.org/10.3969/j.issn.1000-9752.2013.06.015 (2013).\u003c/li\u003e\n\u003cli\u003eChen, B. X. \u0026amp; Xu, B. G. Logging evaluation technique for compact clastic rocks in West Sichuan. \u003cem\u003eJournal of Oil and Gas Technology.\u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e, 108-114，184, https://doi.org/10.3969/j.issn.1000-9752.2009.06.020 (2009).\u003c/li\u003e\n\u003cli\u003eZhang, S. Y. Logging evaluation technique for tight sandstone reservoir in Daniudi Gasfield. \u003cem\u003eGeophysical Prospecting for Petroleum.\u003c/em\u003e\u003cstrong\u003e49\u003c/strong\u003e, 415-420，420, https://doi.org/10.3969/j.issn.1000-1441.2010.04.013 (2010).\u003c/li\u003e\n\u003cli\u003eCheng, Z. G.\u003cem\u003e et al.\u003c/em\u003e Classification of petrophysical facies and gas evaluation in tight reservoir-case of re-evaluation of old wells in eastern Sulige. \u003cem\u003ePetroleum Geology and Recovery Efficiency.\u003c/em\u003e\u003cstrong\u003e20\u003c/strong\u003e, 23-25，27，32，112, https://doi.org/10.13673/j.cnki.cn37-1359/te.2013.05.006 (2013).\u003c/li\u003e\n\u003cli\u003eWen, L., Liu, A. P., Zhong, Z. C., Yuan, J. \u0026amp; Li, H. L. Method of evaluating upper triassic tight sandstone reservoirs in West Sichuan foreland basin. \u003cem\u003eNatural Gas Industry.\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 49-53,48, https://doi.org/CNKI:SUN:TRQG.0.2005-S1-013 (2005).\u003c/li\u003e\n\u003cli\u003eJiang, Y. Q.\u003cem\u003e et al.\u003c/em\u003e Characterization techniques and trends of the pore structure of tight reservoirs. \u003cem\u003eGeological Science and Technology Information.\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e, 63-70, https://doi.org/CNKI:SUN:DZKQ.0.2014-03-010 (2014).\u003c/li\u003e\n\u003cli\u003eYang, Z. M., Jiang, H. Q., Zhu, G. Y., Li, S. T. \u0026amp; Shan, W. W. Research on reservoir evaluation index for low-permeability water-bearing gas reservoir. \u003cem\u003eActa Petrolei Sinica.\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 252-256, https://doi.org/10.3321/j.issn:0253-2697.2008.02.017 (2008).\u003c/li\u003e\n\u003cli\u003eChen, Z. G. Analogy analysis of West Sichuan depression and Northern America sandstone gas rrservoirs. \u003cem\u003eJournal of Southwest Petroleum University(Science \u0026amp; Technology Edition).\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e, 71-76, https://doi.org/10.3863/j.issn.1674-5086.2012.01.011 (2012).\u003c/li\u003e\n\u003cli\u003eMeng, Z. Y.\u003cem\u003e et al.\u003c/em\u003e Combined mercury porosimetry to characterize the microscopic pore structure and pore size distribution of tight reservoirs: a case of Chang 6 reservoir in Wuqi area, Ordos Basin. \u003cem\u003eGeological Science and Technology Information.\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e, 208-216, https://doi.org/10.19509/j.cnki.dzkq.2019.0224 (2019).\u003c/li\u003e\n\u003cli\u003eYe, L. Y., Zhong, B., Xiong, W., Liu, H. X. \u0026amp; Hu, Z. M. An integrated evaluation method of Xujiahe low-permeability sandstone gas reservoirs in Middle Sichuan Basin. \u003cem\u003eNatural Gas Industry.\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 43-46，116-117, https://doi.org/10.3787/j.issn.1000-0976.2012.11.010 (2012).\u003c/li\u003e\n\u003cli\u003eHan, C. C., Lin, C. Y., Ren, L. H., Dong Chunmei \u0026amp; Wei Ting. Waveform-indication-based seismic inversion of carbonate reservoirs:A case study of the Lower-Middle Ordovician in Tahe oilfield,Tarim Basin. \u003cem\u003eOil \u0026amp; Gas Geology\u003c/em\u003e. \u003cstrong\u003e38\u003c/strong\u003e, 822-830, https://doi.org/10.11743/ogg20170419 (2017).\u003c/li\u003e\n\u003cli\u003eYue, L. J. \u0026amp; Qian, Y. L. Prediction of the narrow-thin reservoir based on seismic waveform indication inversion technology. \u003cem\u003ePetroleum Geology \u0026amp; Oilfield Development in Daqing.\u003c/em\u003e\u003cstrong\u003e39\u003c/strong\u003e, 135-140, https://doi.org/10.19597/j.issn.1000-3754.201909027 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3844170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3844170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tight sandstone oil and gas resources have emerged as a crucial element in the future energy landscape. The primary focus of geological evaluation for the development of tight sandstone reservoirs involves the comprehensive assessment and selection of oil layers with high potential, commonly referred to as 'sweet spots.' This study proposes a geological sweet spot evaluation model for tight sandstone reservoirs, which utilizes eight conventional well logging curve data as samples: crosswave time difference, compressional wave time difference, natural gamma, density, wellbore diameter, phase resistivity, amplitude resistivity, and average neutron porosity. The model applies factor analysis and normalization principles to quantitatively assess the vertical distribution of geological sweet spots within the reservoir. Validation of the model is conducted using exemplar wells. Additionally, the study integrates seismic data with well logging data and employs waveform indication simulation techniques to simulate the stratigraphic parameters of the reservoir. This approach facilitates the establishment of a three-dimensional geological sweet spot evaluation model, enabling the inversion of key parameters of geological sweet spots in the target stratum. Consequently, the model allows for a quantitative assessment of the lateral distribution of geological sweet spots in Block DF11-2.","manuscriptTitle":"Study on Three-Dimensional Geological Sweet Spot Evaluation of Tight Sandstone Reservoirs Based on Well Logging Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 12:59:30","doi":"10.21203/rs.3.rs-3844170/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"715d5b0a-8346-4fbe-8bdc-dd6671cf4e12","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-10T09:45:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 12:59:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3844170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3844170","identity":"rs-3844170","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}