{"paper_id":"09a97005-7c4e-4102-a28b-0d4b7db59c51","body_text":"Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale | 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 Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale Justin Mackey, Daniel J. Bain, Greg Lackey, James Gardiner, Djuna Gulliver, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3840288/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1,159 (95% CI: 1139–1178) metric tons per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median = 2.89 X 10 7 L/10-yr) and southwest (median = 5.56 x 10 7 L/10-yr) regions of the state, influencing estimates for ultimate lithium yields from wells in southwest [2.90 (95% CI: 2.80–2.99) mt/ 10-yr] and northeast [1.96 (CI: 1.86–2.07) mt/10-yr] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66–7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3–20.7). These estimates indicate lithium mass yields from Marcellus PW are substantial, though regional variability in chemistry and production may impact recovery efficiencies. Earth and environmental sciences/Environmental sciences Physical sciences/Energy science and technology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Lithium (Li) is a major battery component in electric vehicles (EV) and is part of a broader group of critical elements (minerals) with existing supply chain concerns. Moreover, Li is considered essential to the US economy due to domestic consumption in energy, manufacturing and defense. The Infrastructure Investment and Jobs Act 1 , commonly referred to as the Bipartisan Infrastructure Law, requires the raw materials used in EV battery components to be sourced domestically by 2030. As such, Li demand scenarios from net-zero and decarbonization initiatives could drive global demand of the critical metal up 400% 2 . These factors necessitate alternative domestic sources of Li to reliably enable the energy transition. Recent work has shown formation fluids that are co-produced with hydrocarbons during oil and gas operations, referred to as produced water (PW), has significant potential as an alternative source of Li 3 , 4 . Specifically, evidence suggests formation waters from Paleozoic stratigraphy of the Appalachian region have economically viable Li concentrations 5 – 7 . A promising domestic source of Li is PW from Marcellus Formation, a late Paleozoic (Middle-Devonian) aged unconventional natural gas field that underlies significant portions of central Appalachia (Fig. 1). Unconventional formations, such as the Marcellus, require substantial amounts of water to hydraulically fracture the formation to produce hydrocarbons. Figure 1. Map of study area showing the Marcellus shale extent, well locations used in decline curve analysis (DCA), PW samples used in this study, and previous USGS sample locations. Lithium (Li) concentration data was calculated using new data reported in this manuscript and existing data from the USGS National Produced Waters Database (Blondes et al., 2018) Moreover, the drilling boom and subsequent active wells have culminated in large volumes of PW being generated with limited options for beneficial reuse 8 , 9 . Currently, ~ 95% of the PW co-produced with natural gas from the Marcellus is recycled in ongoing fracking operations 8 , however this fluid is hypersaline, with total dissolved solids (TDS) concentrations exceeding 100,000 mg/L 10,11 and requires some treatment prior to reinjection 12 . Significantly, this fluid is enriched in Li relative to other formations of comparable TDS 13 , 14 . Marcellus Shale was deposited contemporaneous to Middle Devonian volcanism and contains interlayered beds of volcanic ash that, through diagenesis, partitioned Li from the volcanic ash into formation pore fluids making it a suitable target for Li extraction 14 – 17 . Reservoir properties, infrastructure and operational footprints have created two regional natural gas production hot spots, one in the northeast and another in the southwest of Pennsylvania 18 (Fig. 1). The Marcellus Shale varies compositionally and stratigraphically between these two regions, influencing the chemical complexity of its produced waters 11 , 19 , 20 . Thus, it is expected the Li extraction potential will vary between different areas across the shale. To date, comparative analysis of the Li resource potential of Paleozoic brines from global and domestic perspectives have highlighted the prospect of Li extraction from these waters, however these analyses do not consider specific, intra-basin influences on Li yields 3 , 4 , 14 . For example, the rate and quantity of PW generated by a given well can vary widely and these spatial variations introduce substantial uncertainty into basin scale Li mass-yield estimates 21 . Likewise, compositional variance in produced water chemistry can impact the method of Li recovery in Li extraction operations, where higher Mg 2+ /Li + mass ratios decrease absorbent and precipitation efficiencies during water treatment for Li removal 22 . Lastly, the volume of water produced from an unconventional well generally declines with time and will impact the ultimate recovery of Li from that location 23 , 24 . This study uses chemical and production compliance data reported to the Pennsylvania Department of Environmental Protection (PA DEP) to predict Li mass yields from the Middle-Devonian Marcellus Shale PW 25 , 26 . Furthermore, these compliance data are used to incorporate intra-regional variations of PW composition, production decline rates and volumes produced in estimating Li mass yields in Pennsylvania. 2. Results Herein, we report Monte Carlo estimates of Li mass yields from Pennsylvania’s Marcellus Shale PW. These results will quantify the total annual Li mass yield potential in Pennsylvania from PW, the amount of Li that can be generated from a single Marcellus well in either operating zone (NE PA or SW PA) and patterns of the variables that led to these calculations. Annual, mean PW volume generated in Pennsylvania from 2018—2022 was 8.76 X 10 9 L (STD; +/- 5.54 X 10 8 ). From this, the maximum likely estimation (MLE) for the total annual Marcellus Li yield is approximately 1,159 metric tons (mt) (95% CI: 1139–1178) (Fig. 2 ). Lithium and Mg concentrations and well water production volumes vary between the production regions but interactions between the differences result in negligible effect on the MLE Li yields. Produced waters sampled from wells in the NE have a broader distribution of Li concentrations (IQR, 139―267 mg/l; n = 422) with a median of 205 mg/L. Whereas, produced water Li concentrations in SW PA are lower and distributed more narrowly (IQR, 112―140 mg/l; n = 137) with a median concentration of 127 mg/L (Fig. 3 ). Conversely to Li, more PW is produced in SW PA wells than in NE PA. The median 10-year cumulative PW volume produced by a well in SW PA is over twice that of a NE PA well (4.68 × 10 7 liters and 2.43 × 10 7 , respectively; Fig. 4 ). Consequently, the 10-year cumulative Li production of a Marcellus well in the NE and SW producing zones (Fig. 5 ) vary by ~ 33%. The MLE calculations suggest SW and NE PA regional 10-year Li mass yields are 2.90 (95% CI: 2.80–2.99) mt and 1.86 (95% CI: 1.86–2.07) mt, respectively. Additionally, the data reveals significant heterogeneity in magnesium concentrations and Mg/Li mass ratios in PW generated between the two production zones. Median Mg concentrations in the NE PA are roughly half of those measured in the SW PA (NE PA; 1,000, SW PA; 2,300). Likewise, median Mg/Li ratios vary between the NE and SW PA are 5.4 (IQR, 2.66—7.26; n = 421) and 17.8 (IQR, 14.3—20.7 n = 137), respectively. Descriptive statistics of Li and Mg concentrations, Mg/Li ratios, PW volumes and 10-year Li mass yields are summarized in Table 1. Table 1. Distributions of Lithium (Li), Magnesium (Mg), Mg/Li ratios with simulation results for statewide, northeast (NE PA) and southwest (SW PA) Pennsylvania with 95% confidence intervals (CI). Mass is in metric tons (mt) n Median P25 P75 Lithium Mass Yield 95% CI Chemical Paramters NE Mg (mg/L) 421 1000 460 1690 - SW Mg (mg/L) 137 2300 1790 2570 - NE Mg/Li 422 5.39 2.66 7.26 - SW Mg/Li 137 17.8 14.3 20.7 - NE Li (mg/Li) 422 205 139 267 - SW Li (mg/Li) 137 127 112 140 - - - - - - PW Volume and Li Mass Yield Results - - - - - NE 10-year Cumulative PW Vol (L) 506 2.43 x 10^7 - - - SW 10-year Cumulative PW Vol (L) 722 4.68 x 10^7 - - - NE PA Li mt/10-year - - - - 1.96 1.86–2.07 SW PA Li mt/10-year - - - - 2.90 2.80–2.99 Annual Statewide Li Mass Yield (mt) 1159 1139–1178 3. Discussion State-wide MLE of PW resources in the Marcellus suggest this Li source could supply a substantial amount to the domestic markets, though existing PW reuse options need to be considered. Annual domestic Li consumption is estimated at 3,000 metric tons 27 . Astoundingly, statewide Li mass yield estimates suggest Marcellus Shale production wastewater from Pennsylvania could meet 38–40% of current domestic consumption, assuming Li extraction is more cost effective than competing uses for the water. Currently, 95% of the PW generated is reused in ongoing hydraulic fracturing operations and any volumetric offsets from increased treatment would likely be made up with freshwater sources 8 , 28 , 29 . Moreover, environmental, social considerations and regulatory structures have spawned investments in water management infrastructure to optimize for PW reuse 28 , 29 . Typically, PW is transported via a network of pipelines to a central facility where it is minimally treated to remove solids prior to reinjection at other well sites 12 , 28 . Li extraction from PW would be a more complex process and may increase the environmental footprint of water operations due to added transportation and solid wastes generated from PW treatment. Ultimately, our results show Li mass yields from Marcellus PW are substantial and the added valorization of this waste could offset the needed infrastructure and disposal costs. Regional variation in PW volumes and chemistry between the NE and SW producing zones likely will impact both the Li extraction method and the ultimate mass of Li generated. Specifically, this study shows that SW PA wells have slower PW decline rates (Fig. 4 ) and higher ultimate recovery potential (2.90 mt, 95% CI: 2.80–2.99; Fig. 5 .), compared to a NE PA well (1.96 mt, 95% CI: 1.86–2.07). However, SW PA wells only generate, on average, 26–38% more Li when considering differences in PW Li concentrations and the uncertainty of the calculations, despite producing approximately two times the PW volume. Further, extraction of Li from PW with high Mg/Li mass ratios (> 6), such as in SW PA, is less efficient and expensive relative to low Mg/Li extraction methods 30 . The low Mg/Li composition of NE produced waters are comparable to salar brines, such as the Atacama brines of Chile, which are favorable to more economical and sustainable evaporative and distillation Li recovery methods 30 , 31 . As a result, the higher Li yields from SW PA wells may be more costly to extract due to the lower concentrations and reduced treatment efficiencies due to the high Mg/Li nature of these waters. Another important consideration in the total Li yield of a reservoir is the well production decline rate. A typical Marcellus well has an 80% decline in production of water within it’s the first 2 years (SI 4.). Sustainable production of Li at volumes reported in this manuscript require continuous addition of new Marcellus wells to supplant older, less productive wells. Advances in artificial lift technologies could improve brine production metrics in older wells and should be a consideration in prolonging the life of this resource. The lift parameter in the model evaluated in this study is a baseline volume of produced water calculated from empirical data and assumed to be resulting from artificial lift installation. This study estimates that Marcellus Shale related Li yields have potential to make a significant contribution to US domestic consumption with a set of reasonable, conservative assumptions. Even if most likely estimates presented here are off by one or even two standard deviations, the potential production of Li would meet more than 30% of current US domestic consumption. Further, if the estimates are too low, this result becomes an even more promising incentive to properly manage Marcellus PW. The USGS estimate of roughly 96 trillion cubic feet of undiscovered gas in the Marcellus suggests the production lifetime of the formation will exceed several more decades 18 . Future production will likely be on the fringe of the current operational zones, as new territory is developed. Central PA is underdeveloped and has some of the of the highest Li concentrations included in our analysis (Fig. 1.). It seems clear that Marcellus Shale PW has the capacity to provide significant Li yields for the foreseeable future. 4. Methods 4.1 Lithium Concentration Data Produced waste-water chemical profiles reported to the PA DEP between 2012–2023 from unconventional wells targeting the Marcellus Shale were collected 25 . In total, 595 reports were considered from 515 wells. Chemical data were extracted from the PA DEP reports using optical character recognition and custom Python scripts. Two filters were applied to assure data quality: 1) Samples with a major cation/anion charge imbalance > ± 10% were removed; and 2) only brines (TDS > 35,000 mg/L) were considered to prevent inclusion of dilute flowback waters 32 – 34 . Lastly, regional PW profiles were sorted and stored based on location using ArcGIS Pro (NE; n = 422, SW; n = 137) 35 . Note that 35 reports were from wells located in the center of the state and not included in the regional analysis. 4.2 Regional Produced Water Volume Calculations Empirical decline curve analysis (DCA) is a widely used method to forecast the ultimate resource recovery from a hydrocarbon well 36 , 37 . This study employs DCA methods to forecast and evaluate the regional variability in PW volumes between Marcellus wells in the NE PA and SW PA operating zones, assessed over a decade of presumed continuous production. To do so, we mined Marcellus Shale PW volumes reported to the PA DEP Bureau of Oil and Gas (PA DEP, 2023) by six of the top 10 producers in the Pennsylvania from the years 2009—2022 26 . Top producers were selected based on quantity of natural gas produced, operational footprint (NE PA and SW PA) and continuity of at least one decade of operations. The total well count evaluated from the six operators’ data included in this study account for 42% of wells reporting PW volumes in 2022. Data processing and regional PW decline rate models for the NE and SW production zones were done in Python 3.9 using the Pandas, SciPy and NumPy packages 38 – 41 . First, monthly production volume data was parsed and verified to only include wells with the Marcellus Shale designated as the producing formation. Next, production volumes for each well were grouped by their associated API number, the first PW volume was used in the case of a duplicate. Then, well PW production timespans were normalized for each well by calculating the duration of time (months) between well installation (SPUD) and the date the volume was recorded. Non-duplicate, multiple reported volumes sharing a date for a unique API number were summed. The median SPUD normalized Marcellus PW DCA yielded an exponential curve fit that stabilized to a non-zero value approximately six years after the well’s SPUD date (Supplementary Figure S1 ). Generally, hydrocarbon well production declines through time, until a point where the bottom hole pressure of the well isn’t sufficient to economically produce hydrocarbons. At this point, operators install an artificial lift mechanism to lift the fluids (hydrocarbon and water) out of the well. A lift factor was included in the decline equation to account for this baseline production These calculations and variable descriptors are detailed in the SI. Initially, 4,798 wells reporting PW waste were evaluated in this DCA. However, a significant number of these wells had insufficient production volume data or reported volumes too noisy to generate accurate curve fits. In extreme cases, the model failed to converge on a fit. A series of quality control measures were applied to improve the success of the curve fits. First, curve fits were only carried out on wells having more than one reported volume and at least one measurement within the first two years from the SPUD date. Second, because Marcellus PW volume decline rates stabilize approximately 6 years from the SPUD of the well, only wells with reported volumes past 6 years from SPUD were considered (n = 2,561). Additional expulsion criteria were used to eliminate curve-fit parameter outliers generated from the DCA. These outlier fits generally arise from data gaps or inconsistencies in the production process rather than variability in the production. Including these fits in the Monte Carlo process artificially inflates the uncertainly. To minimize this inflation, we further filtered the data as follows: First, a goodness-of-fit filter was used to select curve fits with an r-squared (r 2 ) of 0.5 or greater. In general, curve fits falling below the 0.5 r 2 threshold were either positive, flat, vertical or otherwise not decreasing exponentially. Second, inter-quartile range (IQR) threshold analysis was used to identify and remove curve fits that over-estimated the initial production values (Qi) 42 . Outliers exceeding 1.5 of the IQR were removed. While wells with negative calculated lift factor (L) values were not removed, the negative values were converted to zero, as the negatives were considered a relic of the fit rather than actual negative production. After poor fit records were removed, wells with curve fit parameters that passed quality criteria were partitioned into region specific datasets (NE and SW PA) using ArcGIS Pro 35 . In total, 1,228 well decline curves met the quality criteria and fit parameters used in Monte Carlo simulations of production scenarios. Of these, 506 were in the NE and 722 in the SW producing zones of the Marcellus. 4.3 Monte Carlo Framework Monte Carlo (MC) simulations were used to both propagate and mitigate the uncertainty associated with using unrefined datasets to model Li mass yields on statewide and well-by-well scales. All variable “pulls” used in MC simulations were created using NumPy Random Number Generator (RNG) in the Spyder integrated developer environment using Python 3.9 programming language. All distributions generated and employed in our MC simulations were validated using descriptive statistics to ensure a match to the original dataset. A diagram of the data workflow is provided in Fig. 6. Table. 2 contains the original data sources and descriptions, distribution type, and RNG parameters (shape and scale) used in this study. Figure 6. Data process and workflow used in this study. Table 2. Distributions of original data, data sources and decline curve fit parameters. Scale and shape values used in NumPy are the mean and standard deviations of the log-transformed dataset. Variate Descriptor n= Distribution Distribution Type Numpy Parameters (scale, shape) Source Statewide Annual Mass Yield Parameters Lithium Concentration 593 Median: 174 (IQR, 125–247) mg/L lognormal (5.14, 0.51) Chemical analysis 26 residual waste chemical analysis reports 25 Annual Production Volumes 5 8.76 x 10 9 (STD = ± 5.53 x 10 8 ) liters normal Pennsylvania Oil and Gas Well Waste Report Portal 26 Regional 10-year Mass Yield Parameters NE PA NE PA Lithium Concentration 422 Median: 205 (IQR, 139–267) mg/L lognormal (5.3, 0.53) Chemical analysis 26 residual waste chemical analysis reports 25 Qi (Initial Production Rate) 506 Median = 2.69 x 10 6 (IQR, 8.11 x 10 5 – 1.37 x 10 7 ) lognormal (16, 1.26) Curve fit parameter modeled from residual waste volumes 26 D (Production Decline Rate) 506 Median = 0.11 (IQR, 0.054–0.22) lognormal (-1.9, 0.65) Curve fit parameter modeled from residual waste volumes 26 L (Lift Factor) 506 Median = 6.34 x 10 3 (IQR, 0–1.23 x 10 4 ) liters lognormal (8.9, 0.80) Curve fit parameter modeled from residual waste volumes 26 SW PA SW PA Lithium Concentration 135 Median: 127 (IQR, 112–140) mg/L lognormal (4.8, 0.26) Industry Collaborator provided residual waste chemical analysis. Qi (Initial Production Rate) 722 Median = 3.41 x 10 6 (IQR, 1.55 x 10 6 – 9.37 x 10 6 ) lognormal (16, 0.92) Curve fit parameter modeled from residual waste volumes 26 D (Production Decline Rate) 722 Median: 0.075 (IQR, 0.048–0.12) lognormal (-2.4, 0.51) Curve fit parameter modeled from residual waste volumes 26 L (Lift Factor) 722 Median = 1.21 x 10 4 (IQR, 0–2.98 x 10 4 ) liters lognormal (9.6, 0.94) Curve fit parameter modeled from residual waste volumes 26 Annual-statewide estimates of Li mass yields were evaluated using the most recent five years (2018—2022) of total annual Marcellus PW production data. Here, monthly reported volumes were summed for each of the calendar years. NumPy (RNG) was used to fit normal probability distribution functions (PDF) and generate random sample variates of the calculated annual PW volume and the Li concentration distributions described in section 2.1. These Monte Carlo samples of volume and chemistry (n = 25,000) were multiplied to derive the most likely estimate of Li mass yields per year from Marcellus operations in Pennsylvania. A Monte Carlo framework was also used to predict the cumulative PW production and associated Li mass yield for an individual Marcellus well in either the NE PA or SW PA operating zones over a ten-year period. To do this, decline-curve fits and Li concentration data were partitioned into NE PA and SW PA datasets based on the location of origin and used to create separate random sample variates for their respective regions. Given the lognormal distributions of the DCA fit parameters and Li concentrations, shape and scale parameters used to calculate a random distribution for each parameter were taken from the natural log transform of the distribution. Monte Carlo pulls (25,000) from these RNG generated fit parameter distributions were used to simulate a population of decline curves. Each decline curve was integrated over a 10-year timespan, providing a population of cumulative PW volumes for an individual well. This population of PW volumes were multiplied by a MC pull from a region-specific Li distribution to generate a population of Li mass yields from both NE and SW PA wells. Lastly a probability distribution function (PDF) was fit to the aggregated 10-year cumulative Li mass yields from these simulations and the value with the highest probability density was stored. Complete data processing, sampling, and modeling descriptions are included in the supplementary information. Declarations Disclaimer This project was funded by the U.S. Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Author Contributions The authors confirm contribution to the manuscript as follows: J. Mackey contributed the study conception and design, data collection, analysis and interpretation, and draft manuscript preparation; D. Bain contributed study conception and design, data analysis, results interpretation, draft manuscript preparation; G. Lackey contributed data collection and draft manuscript preparation; J. Gardiner contributed data collection and draft manuscript preparation, B. Kutchko contributed study conception, results interpretation and draft manuscript preparation; D. Gulliver contributed results interpretation, draft manuscript preparation. All authors have reviewed and approved the final version of the manuscript. Data Availability The datasets generated and/or analyzed during the current study are available on the National Energy Technology Laboratory’s Energy Data eXchange (EDX), https://edx.netl.doe.gov/dataset/lithium-geochemistry-and-regional-production-decline-curves-of-marcellus-shale-produced-water. 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Supplementary Files EstimatesoflithiummassyieldsfromproducedwatersourcedfromtheDevonianagedMarcellusShalesupplementalinformation.docx Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Mar, 2024 Reviews received at journal 16 Feb, 2024 Reviewers agreed at journal 07 Feb, 2024 Reviewers agreed at journal 24 Jan, 2024 Reviewers invited by journal 24 Jan, 2024 Editor assigned by journal 12 Jan, 2024 Editor invited by journal 11 Jan, 2024 Submission checks completed at journal 11 Jan, 2024 First submitted to journal 06 Jan, 2024 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. 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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-3840288\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":266847462,\"identity\":\"4e65937c-c7a8-4658-9e3a-67adbc96a0a6\",\"order_by\":0,\"name\":\"Justin Mackey\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"National Energy Technology Laboratory\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Justin\",\"middleName\":\"\",\"lastName\":\"Mackey\",\"suffix\":\"\"},{\"id\":266847463,\"identity\":\"664fc2e1-bd41-496a-a223-6a80fb3c1c52\",\"order_by\":1,\"name\":\"Daniel J. Bain\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Pittsburgh\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daniel\",\"middleName\":\"J.\",\"lastName\":\"Bain\",\"suffix\":\"\"},{\"id\":266847464,\"identity\":\"ba2de0e0-186c-4e0c-be04-3635c64cbbba\",\"order_by\":2,\"name\":\"Greg Lackey\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Energy Technology Laboratory\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Greg\",\"middleName\":\"\",\"lastName\":\"Lackey\",\"suffix\":\"\"},{\"id\":266847465,\"identity\":\"4eb47ce5-b9d3-4ea9-86d3-0e87a687641d\",\"order_by\":3,\"name\":\"James Gardiner\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Energy Technology Laboratory\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"James\",\"middleName\":\"\",\"lastName\":\"Gardiner\",\"suffix\":\"\"},{\"id\":266847466,\"identity\":\"18e53fb1-73df-447a-9e48-b1976218e01f\",\"order_by\":4,\"name\":\"Djuna Gulliver\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Energy Technology Laboratory\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Djuna\",\"middleName\":\"\",\"lastName\":\"Gulliver\",\"suffix\":\"\"},{\"id\":266847467,\"identity\":\"6caddec2-117f-4f3e-bd20-a28ee2d8c95f\",\"order_by\":5,\"name\":\"Barbara Kutchko\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Energy Technology Laboratory\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Barbara\",\"middleName\":\"\",\"lastName\":\"Kutchko\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-06 17:14:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3840288/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3840288/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41598-024-58887-x\",\"type\":\"published\",\"date\":\"2024-04-16T01:12:27+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":49636682,\"identity\":\"1916cab1-2109-4b32-9e78-9d4c44a6c278\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:38:04\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":174677,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMap of study area showing the Marcellus shale extent, well locations used in decline curve analysis (DCA), PW samples used in this study, and previous USGS sample locations. Lithium (Li) concentration data was calculated using new data reported in this manuscript and existing data from the USGS National Produced Waters Database (Blondes et al., 2018)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/5e760509f680cf004230bf63.png\"},{\"id\":49636387,\"identity\":\"376a0eae-26d4-4955-9e2b-1ddac0eeff40\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:30:05\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":29704,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHistogram showing distribution of estimated annual lithium (Li) mass yield from shale gas operations in Pennsylvania. A probability density function (PDF) fit to Monte Carlo simulations (n=25,000) shows the annual Li mass yield maximum likely estimate is 1159 (95% CI: 1139 – 1178) mt/year.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/007075fc40fbf5477bff5457.png\"},{\"id\":49636388,\"identity\":\"89ecd9c1-3a04-440d-8df6-1619896f0318\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:30:05\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":122279,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHistogram plot showing regional variance of Li (mg/L) concentrations in Marcellus produced water. NE PA Li distribution is broader and higher (median; 205 mg/L) than the SW PA (median; 127).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/ebebecf2757ea568bdfeb762.png\"},{\"id\":49636382,\"identity\":\"51882d54-9f24-4582-b101-058afb9babc9\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:30:04\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":52875,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProduction decline curve plots for curve fits with an R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026nbsp;≥ 0.5 for NE and SW PA. \\u0026nbsp;Y-axis is in liters and X-axis is time in years from the start of drilling. Median SW PA 10-year cumulative PW production is greater than NE due to a more gradual decline. Wells in the NE producing zone have a higher range of initial water production volumes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/0e5089b39c8cc299bcf1bc42.png\"},{\"id\":49636384,\"identity\":\"1258aa1a-7b23-4dd4-a772-cd2f25f4a373\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:30:04\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32385,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHistogram plot of Monte Carlo simulation results (n= 25,000) of estimated ultimate Li mass yield from a single Marcellus shale gas well over 10-years of assumed continuous production. Regional estimates on lithium yields from a SW PA well is marginally more (~33%) than its NE PA counterpart.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/b1390318bc38891e0a5e2ea8.png\"},{\"id\":49636386,\"identity\":\"ef2cd04b-5078-4f22-a74d-ae694785adda\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:30:05\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":119924,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eData process and workflow used in this study.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/3b445877e4c3059bac8d4288.png\"},{\"id\":54796052,\"identity\":\"6991aa3a-2ed3-4efa-997b-d85a5d3eeecd\",\"added_by\":\"auto\",\"created_at\":\"2024-04-17 01:12:33\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":868881,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/d2f1254f-621f-4876-afa7-f16383b47f44.pdf\"},{\"id\":49636385,\"identity\":\"5928809a-f9c1-4b59-b77e-d073d591e2ff\",\"added_by\":\"auto\",\"created_at\":\"2024-01-15 17:30:04\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":504888,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"EstimatesoflithiummassyieldsfromproducedwatersourcedfromtheDevonianagedMarcellusShalesupplementalinformation.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3840288/v1/883a2a6d044b9c0fadbe8846.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLithium (Li) is a major battery component in electric vehicles (EV) and is part of a broader group of critical elements (minerals) with existing supply chain concerns. Moreover, Li is considered essential to the US economy due to domestic consumption in energy, manufacturing and defense. The Infrastructure Investment and Jobs Act\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e, commonly referred to as the Bipartisan Infrastructure Law, requires the raw materials used in EV battery components to be sourced domestically by 2030. As such, Li demand scenarios from net-zero and decarbonization initiatives could drive global demand of the critical metal up 400%\\u003csup\\u003e2\\u003c/sup\\u003e. These factors necessitate alternative domestic sources of Li to reliably enable the energy transition. Recent work has shown formation fluids that are co-produced with hydrocarbons during oil and gas operations, referred to as produced water (PW), has significant potential as an alternative source of Li\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e. Specifically, evidence suggests formation waters from Paleozoic stratigraphy of the Appalachian region have economically viable Li concentrations\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eA promising domestic source of Li is PW from Marcellus Formation, a late Paleozoic (Middle-Devonian) aged unconventional natural gas field that underlies significant portions of central Appalachia (Fig.\\u0026nbsp;1). Unconventional formations, such as the Marcellus, require substantial amounts of water to hydraulically fracture the formation to produce hydrocarbons.\\u003c/p\\u003e \\u003cp\\u003eFigure 1. Map of study area showing the Marcellus shale extent, well locations used in decline curve analysis (DCA), PW samples used in this study, and previous USGS sample locations. Lithium (Li) concentration data was calculated using new data reported in this manuscript and existing data from the USGS National Produced Waters Database (Blondes et al., 2018)\\u003c/p\\u003e \\u003cp\\u003eMoreover, the drilling boom and subsequent active wells have culminated in large volumes of PW being generated with limited options for beneficial reuse\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e. Currently, ~\\u0026thinsp;95% of the PW co-produced with natural gas from the Marcellus is recycled in ongoing fracking operations\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e, however this fluid is hypersaline, with total dissolved solids (TDS) concentrations exceeding 100,000 mg/L\\u003csup\\u003e10,11\\u003c/sup\\u003e and requires some treatment prior to reinjection\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. Significantly, this fluid is enriched in Li relative to other formations of comparable TDS\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. Marcellus Shale was deposited contemporaneous to Middle Devonian volcanism and contains interlayered beds of volcanic ash that, through diagenesis, partitioned Li from the volcanic ash into formation pore fluids making it a suitable target for Li extraction\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR15 CR16\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. Reservoir properties, infrastructure and operational footprints have created two regional natural gas production hot spots, one in the northeast and another in the southwest of Pennsylvania\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;1). The Marcellus Shale varies compositionally and stratigraphically between these two regions, influencing the chemical complexity of its produced waters\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, it is expected the Li extraction potential will vary between different areas across the shale.\\u003c/p\\u003e \\u003cp\\u003eTo date, comparative analysis of the Li resource potential of Paleozoic brines from global and domestic perspectives have highlighted the prospect of Li extraction from these waters, however these analyses do not consider specific, intra-basin influences on Li yields\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. For example, the rate and quantity of PW generated by a given well can vary widely and these spatial variations introduce substantial uncertainty into basin scale Li mass-yield estimates\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. Likewise, compositional variance in produced water chemistry can impact the method of Li recovery in Li extraction operations, where higher Mg\\u003csup\\u003e2+\\u003c/sup\\u003e/Li\\u003csup\\u003e+\\u003c/sup\\u003e mass ratios decrease absorbent and precipitation efficiencies during water treatment for Li removal\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e. Lastly, the volume of water produced from an unconventional well generally declines with time and will impact the ultimate recovery of Li from that location\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e. This study uses chemical and production compliance data reported to the Pennsylvania Department of Environmental Protection (PA DEP) to predict Li mass yields from the Middle-Devonian Marcellus Shale PW\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. Furthermore, these compliance data are used to incorporate intra-regional variations of PW composition, production decline rates and volumes produced in estimating Li mass yields in Pennsylvania.\\u003c/p\\u003e\"},{\"header\":\"2. Results\",\"content\":\"\\u003cp\\u003eHerein, we report Monte Carlo estimates of Li mass yields from Pennsylvania\\u0026rsquo;s Marcellus Shale PW. These results will quantify the total annual Li mass yield potential in Pennsylvania from PW, the amount of Li that can be generated from a single Marcellus well in either operating zone (NE PA or SW PA) and patterns of the variables that led to these calculations. Annual, mean PW volume generated in Pennsylvania from 2018\\u0026mdash;2022 was 8.76 X 10\\u003csup\\u003e9\\u003c/sup\\u003eL (STD; +/- 5.54 X 10\\u003csup\\u003e8\\u003c/sup\\u003e). From this, the maximum likely estimation (MLE) for the total annual Marcellus Li yield is approximately 1,159 metric tons (mt) (95% CI: 1139\\u0026ndash;1178) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLithium and Mg concentrations and well water production volumes vary between the production regions but interactions between the differences result in negligible effect on the MLE Li yields. Produced waters sampled from wells in the NE have a broader distribution of Li concentrations (IQR, 139―267 mg/l; n\\u0026thinsp;=\\u0026thinsp;422) with a median of 205 mg/L. Whereas, produced water Li concentrations in SW PA are lower and distributed more narrowly (IQR, 112―140 mg/l; n\\u0026thinsp;=\\u0026thinsp;137) with a median concentration of 127 mg/L (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Conversely to Li, more PW is produced in SW PA wells than in NE PA. The median 10-year cumulative PW volume produced by a well in SW PA is over twice that of a NE PA well (4.68 \\u0026times; 10\\u003csup\\u003e7\\u003c/sup\\u003e liters and 2.43 \\u0026times; 10\\u003csup\\u003e7\\u003c/sup\\u003e, respectively; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Consequently, the 10-year cumulative Li production of a Marcellus well in the NE and SW producing zones (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) vary by ~\\u0026thinsp;33%. The MLE calculations suggest SW and NE PA regional 10-year Li mass yields are 2.90 (95% CI: 2.80\\u0026ndash;2.99) mt and 1.86 (95% CI: 1.86\\u0026ndash;2.07) mt, respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAdditionally, the data reveals significant heterogeneity in magnesium concentrations and Mg/Li mass ratios in PW generated between the two production zones. Median Mg concentrations in the NE PA are roughly half of those measured in the SW PA (NE PA; 1,000, SW PA; 2,300). Likewise, median Mg/Li ratios vary between the NE and SW PA are 5.4 (IQR, 2.66\\u0026mdash;7.26; n\\u0026thinsp;=\\u0026thinsp;421) and 17.8 (IQR, 14.3\\u0026mdash;20.7 n\\u0026thinsp;=\\u0026thinsp;137), respectively. Descriptive statistics of Li and Mg concentrations, Mg/Li ratios, PW volumes and 10-year Li mass yields are summarized in Table\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTable\\u0026nbsp;1.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c8\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eDistributions of Lithium (Li), Magnesium (Mg), Mg/Li ratios with simulation results for statewide, northeast (NE PA) and southwest (SW PA) Pennsylvania with 95% confidence intervals (CI). Mass is in metric tons (mt)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003en\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLithium Mass Yield\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e95% CI\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eChemical Paramters\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNE Mg (mg/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e421\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e460\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1690\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW Mg (mg/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2300\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1790\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2570\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNE Mg/Li\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e422\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW Mg/Li\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNE Li (mg/Li)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e422\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e205\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e267\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW Li (mg/Li)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e127\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e112\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e140\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePW Volume and Li Mass Yield Results\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNE 10-year Cumulative PW Vol (L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e506\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.43 x 10^7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW 10-year Cumulative PW Vol (L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e722\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.68 x 10^7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNE PA Li mt/10-year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.86\\u0026ndash;2.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW PA Li mt/10-year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.80\\u0026ndash;2.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnnual Statewide Li Mass Yield (mt)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1159\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1139\\u0026ndash;1178\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"3. Discussion\",\"content\":\"\\u003cp\\u003eState-wide MLE of PW resources in the Marcellus suggest this Li source could supply a substantial amount to the domestic markets, though existing PW reuse options need to be considered. Annual domestic Li consumption is estimated at 3,000 metric tons\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e. Astoundingly, statewide Li mass yield estimates suggest Marcellus Shale production wastewater from Pennsylvania could meet 38\\u0026ndash;40% of current domestic consumption, assuming Li extraction is more cost effective than competing uses for the water. Currently, 95% of the PW generated is reused in ongoing hydraulic fracturing operations and any volumetric offsets from increased treatment would likely be made up with freshwater sources\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Moreover, environmental, social considerations and regulatory structures have spawned investments in water management infrastructure to optimize for PW reuse\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Typically, PW is transported via a network of pipelines to a central facility where it is minimally treated to remove solids prior to reinjection at other well sites\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. Li extraction from PW would be a more complex process and may increase the environmental footprint of water operations due to added transportation and solid wastes generated from PW treatment. Ultimately, our results show Li mass yields from Marcellus PW are substantial and the added valorization of this waste could offset the needed infrastructure and disposal costs.\\u003c/p\\u003e \\u003cp\\u003eRegional variation in PW volumes and chemistry between the NE and SW producing zones likely will impact both the Li extraction method and the ultimate mass of Li generated. Specifically, this study shows that SW PA wells have slower PW decline rates (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) and higher ultimate recovery potential (2.90 mt, 95% CI: 2.80\\u0026ndash;2.99; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e.), compared to a NE PA well (1.96 mt, 95% CI: 1.86\\u0026ndash;2.07). However, SW PA wells only generate, on average, 26\\u0026ndash;38% more Li when considering differences in PW Li concentrations and the uncertainty of the calculations, despite producing approximately two times the PW volume. Further, extraction of Li from PW with high Mg/Li mass ratios (\\u0026gt;\\u0026thinsp;6), such as in SW PA, is less efficient and expensive relative to low Mg/Li extraction methods\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e. The low Mg/Li composition of NE produced waters are comparable to salar brines, such as the Atacama brines of Chile, which are favorable to more economical and sustainable evaporative and distillation Li recovery methods\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. As a result, the higher Li yields from SW PA wells may be more costly to extract due to the lower concentrations and reduced treatment efficiencies due to the high Mg/Li nature of these waters.\\u003c/p\\u003e \\u003cp\\u003eAnother important consideration in the total Li yield of a reservoir is the well production decline rate. A typical Marcellus well has an 80% decline in production of water within it\\u0026rsquo;s the first 2 years (SI 4.). Sustainable production of Li at volumes reported in this manuscript require continuous addition of new Marcellus wells to supplant older, less productive wells. Advances in artificial lift technologies could improve brine production metrics in older wells and should be a consideration in prolonging the life of this resource. The lift parameter in the model evaluated in this study is a baseline volume of produced water calculated from empirical data and assumed to be resulting from artificial lift installation.\\u003c/p\\u003e \\u003cp\\u003eThis study estimates that Marcellus Shale related Li yields have potential to make a significant contribution to US domestic consumption with a set of reasonable, conservative assumptions. Even if most likely estimates presented here are off by one or even two standard deviations, the potential production of Li would meet more than 30% of current US domestic consumption. Further, if the estimates are too low, this result becomes an even more promising incentive to properly manage Marcellus PW. The USGS estimate of roughly 96 trillion cubic feet of undiscovered gas in the Marcellus suggests the production lifetime of the formation will exceed several more decades\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e. Future production will likely be on the fringe of the current operational zones, as new territory is developed. Central PA is underdeveloped and has some of the of the highest Li concentrations included in our analysis (Fig.\\u0026nbsp;1.). It seems clear that Marcellus Shale PW has the capacity to provide significant Li yields for the foreseeable future.\\u003c/p\\u003e\"},{\"header\":\"4. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Lithium Concentration Data\\u003c/h2\\u003e \\u003cp\\u003eProduced waste-water chemical profiles reported to the PA DEP between 2012\\u0026ndash;2023 from unconventional wells targeting the Marcellus Shale were collected\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e. In total, 595 reports were considered from 515 wells. Chemical data were extracted from the PA DEP reports using optical character recognition and custom Python scripts. Two filters were applied to assure data quality: 1) Samples with a major cation/anion charge imbalance\\u0026thinsp;\\u0026gt;\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10% were removed; and 2) only brines (TDS\\u0026thinsp;\\u0026gt;\\u0026thinsp;35,000 mg/L) were considered to prevent inclusion of dilute flowback waters\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR33\\\" citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e. Lastly, regional PW profiles were sorted and stored based on location using ArcGIS Pro (NE; n\\u0026thinsp;=\\u0026thinsp;422, SW; n\\u0026thinsp;=\\u0026thinsp;137)\\u003csup\\u003e35\\u003c/sup\\u003e. Note that 35 reports were from wells located in the center of the state and not included in the regional analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Regional Produced Water Volume Calculations\\u003c/h2\\u003e \\u003cp\\u003eEmpirical decline curve analysis (DCA) is a widely used method to forecast the ultimate resource recovery from a hydrocarbon well\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. This study employs DCA methods to forecast and evaluate the regional variability in PW volumes between Marcellus wells in the NE PA and SW PA operating zones, assessed over a decade of presumed continuous production. To do so, we mined Marcellus Shale PW volumes reported to the PA DEP Bureau of Oil and Gas (PA DEP, 2023) by six of the top 10 producers in the Pennsylvania from the years 2009\\u0026mdash;2022\\u003csup\\u003e26\\u003c/sup\\u003e. Top producers were selected based on quantity of natural gas produced, operational footprint (NE PA and SW PA) and continuity of at least one decade of operations. The total well count evaluated from the six operators\\u0026rsquo; data included in this study account for 42% of wells reporting PW volumes in 2022.\\u003c/p\\u003e \\u003cp\\u003eData processing and regional PW decline rate models for the NE and SW production zones were done in Python 3.9 using the Pandas, SciPy and NumPy packages\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR39 CR40\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e. First, monthly production volume data was parsed and verified to only include wells with the Marcellus Shale designated as the producing formation. Next, production volumes for each well were grouped by their associated API number, the first PW volume was used in the case of a duplicate. Then, well PW production timespans were normalized for each well by calculating the duration of time (months) between well installation (SPUD) and the date the volume was recorded. Non-duplicate, multiple reported volumes sharing a date for a unique API number were summed. The median SPUD normalized Marcellus PW DCA yielded an exponential curve fit that stabilized to a non-zero value approximately six years after the well\\u0026rsquo;s SPUD date (Supplementary Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Generally, hydrocarbon well production declines through time, until a point where the bottom hole pressure of the well isn\\u0026rsquo;t sufficient to economically produce hydrocarbons. At this point, operators install an artificial lift mechanism to lift the fluids (hydrocarbon and water) out of the well. A lift factor was included in the decline equation to account for this baseline production These calculations and variable descriptors are detailed in the SI.\\u003c/p\\u003e \\u003cp\\u003eInitially, 4,798 wells reporting PW waste were evaluated in this DCA. However, a significant number of these wells had insufficient production volume data or reported volumes too noisy to generate accurate curve fits. In extreme cases, the model failed to converge on a fit. A series of quality control measures were applied to improve the success of the curve fits. First, curve fits were only carried out on wells having more than one reported volume and at least one measurement within the first two years from the SPUD date. Second, because Marcellus PW volume decline rates stabilize approximately 6 years from the SPUD of the well, only wells with reported volumes past 6 years from SPUD were considered (n\\u0026thinsp;=\\u0026thinsp;2,561).\\u003c/p\\u003e \\u003cp\\u003eAdditional expulsion criteria were used to eliminate curve-fit parameter outliers generated from the DCA. These outlier fits generally arise from data gaps or inconsistencies in the production process rather than variability in the production. Including these fits in the Monte Carlo process artificially inflates the uncertainly. To minimize this inflation, we further filtered the data as follows: First, a goodness-of-fit filter was used to select curve fits with an r-squared (r\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e) of 0.5 or greater. In general, curve fits falling below the 0.5 r\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e threshold were either positive, flat, vertical or otherwise not decreasing exponentially. Second, inter-quartile range (IQR) threshold analysis was used to identify and remove curve fits that over-estimated the initial production values (Qi)\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e. Outliers exceeding 1.5 of the IQR were removed. While wells with negative calculated lift factor (L) values were not removed, the negative values were converted to zero, as the negatives were considered a relic of the fit rather than actual negative production. After poor fit records were removed, wells with curve fit parameters that passed quality criteria were partitioned into region specific datasets (NE and SW PA) using ArcGIS Pro\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e. In total, 1,228 well decline curves met the quality criteria and fit parameters used in Monte Carlo simulations of production scenarios. Of these, 506 were in the NE and 722 in the SW producing zones of the Marcellus.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Monte Carlo Framework\\u003c/h2\\u003e \\u003cp\\u003eMonte Carlo (MC) simulations were used to both propagate and mitigate the uncertainty associated with using unrefined datasets to model Li mass yields on statewide and well-by-well scales. All variable \\u0026ldquo;pulls\\u0026rdquo; used in MC simulations were created using NumPy Random Number Generator (RNG) in the Spyder integrated developer environment using Python 3.9 programming language. All distributions generated and employed in our MC simulations were validated using descriptive statistics to ensure a match to the original dataset. A diagram of the data workflow is provided in Fig.\\u0026nbsp;6. Table. 2 contains the original data sources and descriptions, distribution type, and RNG parameters (shape and scale) used in this study.\\u003c/p\\u003e \\u003cp\\u003eFigure 6. Data process and workflow used in this study.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabb\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"12\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTable\\u0026nbsp;2.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c12\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eDistributions of original data, data sources and decline curve fit parameters. Scale and shape values used in NumPy are the mean and standard deviations of the log-transformed dataset.\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVariate Descriptor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003en=\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDistribution\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eDistribution Type\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNumpy Parameters (scale, shape)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSource\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eStatewide Annual Mass Yield Parameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLithium Concentration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e593\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian: 174 (IQR, 125\\u0026ndash;247) mg/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(5.14, 0.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eChemical analysis 26 residual waste chemical analysis reports\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnnual Production Volumes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.76 x 10\\u003csup\\u003e9\\u003c/sup\\u003e (STD\\u0026thinsp;=\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.53 x 10\\u003csup\\u003e8\\u003c/sup\\u003e) liters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003enormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c11\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003ePennsylvania Oil and Gas Well Waste Report Portal\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRegional 10-year Mass Yield Parameters\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNE PA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNE PA Lithium Concentration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e422\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian: 205 (IQR, 139\\u0026ndash;267) mg/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(5.3, 0.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eChemical analysis 26 residual waste chemical analysis reports\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQi (Initial Production Rate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e506\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian\\u0026thinsp;=\\u0026thinsp;2.69 x 10\\u003csup\\u003e6\\u003c/sup\\u003e (IQR, 8.11 x 10\\u003csup\\u003e5\\u003c/sup\\u003e \\u0026ndash; 1.37 x 10\\u003csup\\u003e7\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(16, 1.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eCurve fit parameter modeled from residual waste volumes\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eD (Production Decline Rate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e506\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian\\u0026thinsp;=\\u0026thinsp;0.11 (IQR, 0.054\\u0026ndash;0.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(-1.9, 0.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eCurve fit parameter modeled from residual waste volumes\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eL (Lift Factor)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e506\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian\\u0026thinsp;=\\u0026thinsp;6.34 x 10\\u003csup\\u003e3\\u003c/sup\\u003e (IQR, 0\\u0026ndash;1.23 x 10\\u003csup\\u003e4\\u003c/sup\\u003e) liters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(8.9, 0.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eCurve fit parameter modeled from residual waste volumes\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSW PA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSW PA Lithium Concentration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e135\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian: 127 (IQR, 112\\u0026ndash;140) mg/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(4.8, 0.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eIndustry Collaborator provided residual waste chemical analysis.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQi (Initial Production Rate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e722\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian\\u0026thinsp;=\\u0026thinsp;3.41 x 10\\u003csup\\u003e6\\u003c/sup\\u003e (IQR, 1.55 x 10\\u003csup\\u003e6\\u003c/sup\\u003e \\u0026ndash; 9.37 x 10\\u003csup\\u003e6\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(16, 0.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eCurve fit parameter modeled from residual waste volumes\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eD (Production Decline Rate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e722\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian: 0.075 (IQR, 0.048\\u0026ndash;0.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(-2.4, 0.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eCurve fit parameter modeled from residual waste volumes\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eL (Lift Factor)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e722\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedian\\u0026thinsp;=\\u0026thinsp;1.21 x 10\\u003csup\\u003e4\\u003c/sup\\u003e (IQR, 0\\u0026ndash;2.98 x 10\\u003csup\\u003e4\\u003c/sup\\u003e) liters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003elognormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(9.6, 0.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c12\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eCurve fit parameter modeled from residual waste volumes\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnnual-statewide estimates of Li mass yields were evaluated using the most recent five years (2018\\u0026mdash;2022) of total annual Marcellus PW production data. Here, monthly reported volumes were summed for each of the calendar years. NumPy (RNG) was used to fit normal probability distribution functions (PDF) and generate random sample variates of the calculated annual PW volume and the Li concentration distributions described in section 2.1. These Monte Carlo samples of volume and chemistry (n\\u0026thinsp;=\\u0026thinsp;25,000) were multiplied to derive the most likely estimate of Li mass yields per year from Marcellus operations in Pennsylvania.\\u003c/p\\u003e \\u003cp\\u003eA Monte Carlo framework was also used to predict the cumulative PW production and associated Li mass yield for an individual Marcellus well in either the NE PA or SW PA operating zones over a ten-year period. To do this, decline-curve fits and Li concentration data were partitioned into NE PA and SW PA datasets based on the location of origin and used to create separate random sample variates for their respective regions. Given the lognormal distributions of the DCA fit parameters and Li concentrations, shape and scale parameters used to calculate a random distribution for each parameter were taken from the natural log transform of the distribution.\\u003c/p\\u003e \\u003cp\\u003eMonte Carlo pulls (25,000) from these RNG generated fit parameter distributions were used to simulate a population of decline curves. Each decline curve was integrated over a 10-year timespan, providing a population of cumulative PW volumes for an individual well. This population of PW volumes were multiplied by a MC pull from a region-specific Li distribution to generate a population of Li mass yields from both NE and SW PA wells. Lastly a probability distribution function (PDF) was fit to the aggregated 10-year cumulative Li mass yields from these simulations and the value with the highest probability density was stored.\\u003c/p\\u003e \\u003cp\\u003eComplete data processing, sampling, and modeling descriptions are included in the supplementary information.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eDisclaimer\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis project was funded by the U.S. Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. \\u0026nbsp;Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors confirm contribution to the manuscript as follows: J. Mackey contributed the study conception and design, data collection, analysis and interpretation, and draft manuscript preparation; D. Bain contributed study conception and design, data analysis, results interpretation, draft manuscript preparation; G. Lackey contributed data collection and draft manuscript preparation; J. Gardiner contributed data collection and draft manuscript preparation, B. Kutchko contributed study conception, results interpretation and draft manuscript preparation; D. Gulliver contributed results interpretation, draft manuscript preparation. All authors have reviewed and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analyzed during the current study are available on the National Energy Technology Laboratory\\u0026rsquo;s Energy Data eXchange (EDX), https://edx.netl.doe.gov/dataset/lithium-geochemistry-and-regional-production-decline-curves-of-marcellus-shale-produced-water.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was performed in support of the U.S. Department of Energy\\u0026rsquo;s Fossil Energy and Carbon Management and executed through the National Energy Technology Laboratory (NETL) Research \\u0026amp; Innovation Center\\u0026rsquo;s Critical Minerals field work proposal. \\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eH.R.3684\\u0026ndash;117th Congress (2021\\u0026ndash;2022): Infrastructure Investment and Jobs Act | Congress.gov | Library of Congress. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.congress.gov/bill/117th-congress/house-bill/3684\\u003c/span\\u003e\\u003cspan address=\\\"https://www.congress.gov/bill/117th-congress/house-bill/3684\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThe Role of Critical Minerals in Clean Energy Transitions. Role Crit. Miner. Clean Energy Transitions (2021) doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1787/f262b91c-en\\u003c/span\\u003e\\u003cspan address=\\\"10.1787/f262b91c-en\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKumar, A., Fukuda, H., Hatton, T. A. \\u0026amp; Lienhard, J. H. Lithium Recovery from Oil and Gas Produced Water: A Need for a Growing Energy Industry. ACS Energy Lett. 4, 1471\\u0026ndash;1474 (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDugamin, E. J. M. \\u003cem\\u003eet al.\\u003c/em\\u003e Groundwater in sedimentary basins as potential lithium resource: a global prospective study. Sci. Rep. 11, 1\\u0026ndash;10 (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003e\\u003cem\\u003ePennsylvania Geol. Surv., 4th Ser. 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(John Wiley \\u0026amp; Sons, 2010).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3840288/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3840288/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eDecarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1,159 (95% CI: 1139\\u0026ndash;1178) metric tons per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median\\u0026thinsp;=\\u0026thinsp;2.89 X 10\\u003csup\\u003e7\\u003c/sup\\u003e L/10-yr) and southwest (median\\u0026thinsp;=\\u0026thinsp;5.56 x 10\\u003csup\\u003e7\\u003c/sup\\u003e L/10-yr) regions of the state, influencing estimates for ultimate lithium yields from wells in southwest [2.90 (95% CI: 2.80\\u0026ndash;2.99) mt/ 10-yr] and northeast [1.96 (CI: 1.86\\u0026ndash;2.07) mt/10-yr] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66\\u0026ndash;7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3\\u0026ndash;20.7). These estimates indicate lithium mass yields from Marcellus PW are substantial, though regional variability in chemistry and production may impact recovery efficiencies.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-15 17:29:59\",\"doi\":\"10.21203/rs.3.rs-3840288/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-03-05T04:15:28+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-02-16T07:48:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"8b25479d-d8de-45a5-8125-cf205a954fa0\",\"date\":\"2024-02-07T17:08:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"56bdfd66-fc6c-4582-ad29-3ca09e4cc16c\",\"date\":\"2024-01-24T22:56:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-01-24T21:16:25+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-01-12T06:32:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-01-11T17:52:51+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-01-11T17:41:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2024-01-06T17:11:05+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d8195419-c8dc-4396-af5a-54dcff86daed\",\"owner\":[],\"postedDate\":\"January 15th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":28122153,\"name\":\"Earth and environmental sciences/Environmental sciences\"},{\"id\":28122154,\"name\":\"Physical sciences/Energy science and technology\"}],\"tags\":[{\"value\":\"featured\",\"date\":\"2024-01-15 19:04:07\"}],\"updatedAt\":\"2024-04-17T01:12:27+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3840288\",\"link\":\"https://doi.org/10.1038/s41598-024-58887-x\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2024-04-16 01:12:27\",\"publishedOnDateReadable\":\"April 16th, 2024\"},\"versionCreatedAt\":\"2024-01-15 17:29:59\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-024-58887-x\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-024-58887-x\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3840288\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3840288\",\"identity\":\"rs-3840288\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}