{"paper_id":"30f04637-84a2-4f55-b38c-ea8177ca134d","body_text":"CAlstm-Nash：The nash multi-task learning based on The Convolutional Long Short-Term Memory neural network with Attention Mechanism for missing well-logging parameters prediction | 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 Research Article CAlstm-Nash：The nash multi-task learning based on The Convolutional Long Short-Term Memory neural network with Attention Mechanism for missing well-logging parameters prediction Huating Li, Yingtian Liu, Wen Feng, Yong Li, Junheng Peng, Mingwei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6713510/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 In reservoir evaluation, well-logging parameters such as porosity, permeability, and resistivity are essential for assessing the hydrocarbon storage capacity and producibility of reservoirs. These parameters provide critical insights into the sedimentary characteristics of different geological periods and the underlying subsurface conditions. Leveraging geophysical data to predict key reservoir properties plays an important role in enhancing the accuracy of reservoir descriptions and optimizing exploration and development efforts. To address this challenge, we propose a novel method for simultaneously predicting multiple missing well-logging parameters using known well-logging data based on an convolutional LSTM model with an Attention Mechanism (CAlstm). Specifically, to handle the missing parameters, we introduce Nash Multi-Task Learning (NashMTL) to resolve gradient conflicts among different tasks. Our method begins by developing a CAlstm neural network model, which incorporates a convolutional layer to extract features and generate task-specific outputs. A multi-head attention mechanism is then applied to capture contextual information from each position in the input sequence, and the results are passed to an LSTM layer to process the temporal features of the sequence. Subsequently, NashMTL is introduced to resolve gradient conflicts between different tasks, ensuring that the model effectively balances the impact of each task. The results show that after using Nash Multi-Task Learning, with the correlation coefficient (R2) as the model evaluation metric, for the dataset used in this study, the proposed model's prediction performance for Permeability, Resistivity, and Porosity on the test set improved by approximately 1.69%, 13.58%, and 2.97%, respectively, compared to the single-task model. Multitask learning Reservoir parameters Deep learning Geophysical well logging Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6713510\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":463737840,\"identity\":\"1899e272-dd7b-4ebc-9bc9-efcee175a035\",\"order_by\":0,\"name\":\"Huating Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chengdu University of Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huating\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":463737841,\"identity\":\"fcaa7286-c3bc-4d78-8d96-2c9327db2bd8\",\"order_by\":1,\"name\":\"Yingtian 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These parameters provide critical insights into the sedimentary characteristics of different geological periods and the underlying subsurface conditions. Leveraging geophysical data to predict key reservoir properties plays an important role in enhancing the accuracy of reservoir descriptions and optimizing exploration and development efforts. To address this challenge, we propose a novel method for simultaneously predicting multiple missing well-logging parameters using known well-logging data based on an convolutional LSTM model with an Attention Mechanism (CAlstm). Specifically, to handle the missing parameters, we introduce Nash Multi-Task Learning (NashMTL) to resolve gradient conflicts among different tasks. Our method begins by developing a CAlstm neural network model, which incorporates a convolutional layer to extract features and generate task-specific outputs. A multi-head attention mechanism is then applied to capture contextual information from each position in the input sequence, and the results are passed to an LSTM layer to process the temporal features of the sequence. Subsequently, NashMTL is introduced to resolve gradient conflicts between different tasks, ensuring that the model effectively balances the impact of each task. 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