{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:18:37Z","timestamp":1775186317722,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s12145-025-01938-2","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T17:00:40Z","timestamp":1748883640000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Application of machine learning model in shale TOC content prediction based on well log data: enhancing model interpretability by SHAP"],"prefix":"10.1007","volume":"18","author":[{"given":"Ruibin","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Sandong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Weixin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Detian","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"1938_CR1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.4236\/ijg.2019.104025","volume":"10","author":"O Agbadze","year":"2019","unstructured":"Agbadze O, Ye J, Cao Q, Bravo G (2019) Modeling of source rocks in Moliqing Basin of Yitong Graben, Northeast China. Int J Geosci 10:439\u2013453. https:\/\/doi.org\/10.4236\/ijg.2019.104025","journal-title":"Int J Geosci"},{"key":"1938_CR2","doi-asserted-by":"publisher","first-page":"104297","DOI":"10.1016\/j.coal.2023.104297","volume":"275","author":"A Alanazi","year":"2023","unstructured":"Alanazi A, Ibrahim AF, Bawazer S, Elkatatny S, Hoteit H (2023) Machine learning framework for estimating CO2 adsorption on coalbed for carbon capture, utilization, and storage applications. Int J Coal Geol 275:104297. https:\/\/doi.org\/10.1016\/j.coal.2023.104297","journal-title":"Int J Coal Geol"},{"key":"1938_CR3","doi-asserted-by":"publisher","first-page":"4055","DOI":"10.1007\/s12145-024-01337-z","volume":"17","author":"B Alizadeh","year":"2024","unstructured":"Alizadeh B, Rahimi M, Seyedali SM (2024) Total organic carbon (TOC) estimation using ensemble and artificial neural network methods; a case study from Kazhdumi formation, NW Persian gulf. Earth Sci Inform 17:4055\u20134066. https:\/\/doi.org\/10.1007\/s12145-024-01337-z","journal-title":"Earth Sci Inform"},{"issue":"2","key":"1938_CR4","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1021\/acs.energyfuels.6b02286","volume":"31","author":"H Aljamaan","year":"2017","unstructured":"Aljamaan H, Holmes R, Vishal V, Haghpanah R, Wilcox J, Kovscek AR (2017) CO2 storage and flow capacity measurements on idealized shales from dynamic breakthrough experiments. Energy Fuel 31(2):1193\u20131207. https:\/\/doi.org\/10.1021\/acs.energyfuels.6b02286","journal-title":"Energy Fuel"},{"key":"1938_CR5","doi-asserted-by":"publisher","unstructured":"Al-Mudhafer WJ (2014) Multinomial logistic regression for bayesian estimation of vertical facies modeling in heterogeneous sandstone reservoirs. OTC Asia OTC-24732-MS. https:\/\/doi.org\/10.4043\/24732-MS","DOI":"10.4043\/24732-MS"},{"key":"1938_CR6","doi-asserted-by":"publisher","first-page":"127118","DOI":"10.1016\/j.colsurfa.2021.127118","volume":"627","author":"A Al-Yaseri","year":"2021","unstructured":"Al-Yaseri A, Abdulelah H, Yekeen N, Ali M, Negash BM, Zhang Y (2021) Assessment of CO2\/shale interfacial tension. Colloids Surf A Physicochem Eng Asp 627:127118. https:\/\/doi.org\/10.1016\/j.colsurfa.2021.127118","journal-title":"Colloids Surf A Physicochem Eng Asp"},{"key":"1938_CR7","doi-asserted-by":"publisher","first-page":"104311","DOI":"10.1016\/j.jappgeo.2021.104311","volume":"188","author":"A Amosu","year":"2021","unstructured":"Amosu A, Imsalem M, Sun Y (2021) Effective machine learning identification of TOC-rich zones in the eagle ford shale. J Appl Geophys 188:104311. https:\/\/doi.org\/10.1016\/j.jappgeo.2021.104311","journal-title":"J Appl Geophys"},{"issue":"1","key":"1938_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"1938_CR9","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70\u201379. https:\/\/doi.org\/10.1016\/j.neucom.2017.11.077","journal-title":"Neurocomputing"},{"key":"1938_CR10","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/min8120559","volume":"8","author":"Z Chen","year":"2018","unstructured":"Chen Z, Jiang S, Wang H, Mei L, Miao H, Zou Y (2018) Lithology and U-Pb geochronology of basement of Cenozoic Yitong Basin in northeastern China: implication for basin architecture and new horizon of deep natural gas exploration. Minerals 8:559. https:\/\/doi.org\/10.3390\/min8120559","journal-title":"Minerals"},{"key":"1938_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2012.10.039","volume":"225","author":"P Cortez","year":"2013","unstructured":"Cortez P, Embrechts MJ (2013) Using sensitivity analysis and visualization techniques to open black box data mining models. Inf Sci 225:1\u201317. https:\/\/doi.org\/10.1016\/j.ins.2012.10.039","journal-title":"Inf Sci"},{"issue":"1","key":"1938_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327. https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Trans Inf Theory"},{"key":"1938_CR13","doi-asserted-by":"crossref","unstructured":"Fauziah CA, Al-Khdheeawi EA, Iglauer S, Barifcani A (2020) Influence of Total organic content on CO2-water-sandstone wettability and CO2 geo-storage capacity. SPE Europec:D011S002R004","DOI":"10.2118\/200564-MS"},{"key":"1938_CR14","doi-asserted-by":"publisher","first-page":"130985","DOI":"10.1016\/j.fuel.2024.130985","volume":"363","author":"P Han","year":"2024","unstructured":"Han P, Shen X, Shen B (2024) A simulation study on NOx reduction efficiency in SCR catalysts utilizing a modern C3-CNN algorithm. Fuel 363:130985. https:\/\/doi.org\/10.1016\/j.fuel.2024.130985","journal-title":"Fuel"},{"key":"1938_CR15","doi-asserted-by":"publisher","unstructured":"Handhal AM, Al-Abadi AM, Chafeet HE, Ismail MJ (2020) Prediction of total organic carbon at Rumaila oil field, southern Iraq using conventional well logs and machine learning algorithms. Mar Pet Geol 116. https:\/\/doi.org\/10.1016\/j.marpetgeo.2020.104347","DOI":"10.1016\/j.marpetgeo.2020.104347"},{"issue":"1","key":"1938_CR16","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/0146-6380(93)90078-P","volume":"20","author":"F Hao","year":"1993","unstructured":"Hao F, Chen J, Sun Y, Liu Y (1993) Application of organic facies studies to sedimentary basin analysis: a case study from the Yitong Graben, China. Org Geochem 20(1):27\u201342. https:\/\/doi.org\/10.1016\/0146-6380(93)90078-P","journal-title":"Org Geochem"},{"key":"1938_CR17","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.coal.2016.11.012","volume":"169","author":"B Hazra","year":"2017","unstructured":"Hazra B, Dutta S, Kumar S (2017) TOC calculation of organic matter rich sediments using rock-Eval pyrolysis: critical consideration and insights. Int J Coal Geol 169:106\u2013115. https:\/\/doi.org\/10.1016\/j.coal.2016.11.012","journal-title":"Int J Coal Geol"},{"key":"1938_CR18","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.marpetgeo.2015.12.006","volume":"70","author":"J He","year":"2016","unstructured":"He J, Ding W, Zhang J, Li A, Zhao W, Dai P (2016) Logging identification and characteristic analysis of marine\u2013continental transitional organic-rich shale in the carboniferous-Permian strata, Bohai Bay basin. Mar Petrol Geol 70:273\u2013293. https:\/\/doi.org\/10.1016\/j.marpetgeo.2015.12.006","journal-title":"Mar Petrol Geol"},{"key":"1938_CR19","doi-asserted-by":"publisher","first-page":"103545","DOI":"10.1016\/j.earscirev.2021.103545","volume":"214","author":"T Hu","year":"2021","unstructured":"Hu T, Pang X, Jiang F, Wang Q, Liu X, Wang Z, Jiang S, Wu G, Li C, Xu T, Li M, Yu J, Zhang C (2021) Movable oil content evaluation of lacustrine organic-rich shales: methods and a novel quantitative evaluation model. Earth-Sci Rev 214:103545. https:\/\/doi.org\/10.1016\/j.earscirev.2021.103545","journal-title":"Earth-Sci Rev"},{"key":"1938_CR20","doi-asserted-by":"publisher","first-page":"104318","DOI":"10.1016\/j.coal.2023.104318","volume":"276","author":"AF Ibrahim","year":"2023","unstructured":"Ibrahim AF (2023) Prediction of shale wettability using different machine learning techniques for the application of CO2 sequestration. Int J Coal Geol 276:104318. https:\/\/doi.org\/10.1016\/j.coal.2023.104318","journal-title":"Int J Coal Geol"},{"key":"1938_CR21","doi-asserted-by":"publisher","first-page":"105143","DOI":"10.1016\/j.jappgeo.2023.105143","volume":"216","author":"W Jia","year":"2023","unstructured":"Jia W, Zong Z, Qin D, Lan T (2023) A method for predicting the TOC in source rocks using a machine learning-based joint analysis of seismic multi-attributes. J Appl Geophys 216:105143. https:\/\/doi.org\/10.1016\/j.jappgeo.2023.105143","journal-title":"J Appl Geophys"},{"issue":"1","key":"1938_CR22","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.petrol.2008.12.012","volume":"65","author":"A Kadkhodaie-ilkhchiy","year":"2009","unstructured":"Kadkhodaie-ilkhchiy A, Rezaee R, Bonab H (2009) A committee neural network for prediction of normalized oil content from well log data : an example from south pars gas field, Persian gulf. J Pet Sci Eng 65(1):23\u201332. https:\/\/doi.org\/10.1016\/j.petrol.2008.12.012","journal-title":"J Pet Sci Eng"},{"key":"1938_CR23","doi-asserted-by":"publisher","first-page":"104386","DOI":"10.1016\/j.coal.2023.104386","volume":"280","author":"S Kalam","year":"2023","unstructured":"Kalam S, Arif M, Raza A, Lashari N, Mahmoud M (2023) Data-driven modeling to predict adsorption of hydrogen on shale kerogen: implication for underground hydrogen storage. Int J Coal Geol 280:104386. https:\/\/doi.org\/10.1016\/j.coal.2023.104386","journal-title":"Int J Coal Geol"},{"issue":"2","key":"1938_CR24","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.ijrmms.2004.08.005","volume":"42","author":"M Karakus","year":"2005","unstructured":"Karakus M, Kumral M, Kilic O (2005) Predicting elastic properties of intact rocks from index tests using multiple regression modelling. Int J Rock Mech Min Sci 42(2):323\u2013330. https:\/\/doi.org\/10.1016\/j.ijrmms.2004.08.005","journal-title":"Int J Rock Mech Min Sci"},{"key":"1938_CR25","doi-asserted-by":"publisher","first-page":"104913","DOI":"10.1016\/j.earscirev.2024.104913","volume":"258","author":"J Lai","year":"2024","unstructured":"Lai J, Zhao F, Xia Z, Su Y, Zhang C, Tian Y, Wang G, Qin Z (2024) Well log prediction of total organic carbon: a comprehensive review. Earth Sci Rev 258:104913. https:\/\/doi.org\/10.1016\/j.earscirev.2024.104913","journal-title":"Earth Sci Rev"},{"key":"1938_CR26","doi-asserted-by":"publisher","first-page":"121222","DOI":"10.1016\/j.envpol.2023.121222","volume":"323","author":"H Lee","year":"2023","unstructured":"Lee H, Park S, V-Minh Nguyen H, Shin H-S (2023) Proposal for a new customization process for a data-based water quality index using a random forest approach. Environ Pollut 323:121222. https:\/\/doi.org\/10.1016\/j.envpol.2023.121222","journal-title":"Environ Pollut"},{"issue":"9","key":"1938_CR27","doi-asserted-by":"publisher","first-page":"4008","DOI":"10.3390\/app11094008","volume":"11","author":"H-L Lee","year":"2021","unstructured":"Lee H-L, Kim J-S, Hong C-H, Cho D-K (2021) Ensemble learning approach for the prediction of quantitative rock damage using various acoustic emission parameters. Appl Sci 11(9):4008. https:\/\/doi.org\/10.3390\/app11094008","journal-title":"Appl Sci"},{"issue":"5","key":"1938_CR28","doi-asserted-by":"publisher","first-page":"765","DOI":"10.3390\/math8050765","volume":"8","author":"W Liang","year":"2020","unstructured":"Liang W, Luo S, Zhao G, Wu H (2020) Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics 8(5):765. https:\/\/doi.org\/10.3390\/math8050765","journal-title":"Mathematics"},{"key":"1938_CR29","doi-asserted-by":"publisher","first-page":"107464","DOI":"10.1016\/j.petrol.2020.107464","volume":"194","author":"B Liu","year":"2020","unstructured":"Liu B, Wang S, Ke X, Fu X, Liu X, Bai Y, Pan Z (2020) Mechanical characteristics and factors controlling brittleness of organic-rich continental shales. J Pet Sci Eng 194:107464. https:\/\/doi.org\/10.1016\/j.petrol.2020.107464","journal-title":"J Pet Sci Eng"},{"key":"1938_CR30","doi-asserted-by":"publisher","first-page":"109016","DOI":"10.1016\/j.petrol.2021.109016","volume":"206","author":"C Liu","year":"2021","unstructured":"Liu C, Zhao W, Sun L, Zhang Y, Chen X, Li J (2021a) An improved \u0394logR model for evaluating organic matter abundance. J Pet Sci Eng 206:109016. https:\/\/doi.org\/10.1016\/j.petrol.2021.109016","journal-title":"J Pet Sci Eng"},{"key":"1938_CR31","doi-asserted-by":"publisher","unstructured":"Liu C-y, Huang L, Zhao H-g, Wang J-q, Zhang L, Deng Y, Zhao J-f, Zhang D-d, Fan C-y (2019) Small-scale petroliferous basins in China: characteristics and hydrocarbon occurrence. AAPG Bull 103(9):2139\u20132175. https:\/\/doi.org\/10.1306\/0130191608217014","DOI":"10.1306\/0130191608217014"},{"key":"1938_CR32","doi-asserted-by":"crossref","unstructured":"Liu R, Zhang L, Wang X, Zhang X, Liu X, He X, Zhao X, Xiao D, Cao Z (2023) Application and comparison of machine learning methods for mud shale petrographic identification. Processes","DOI":"10.3390\/pr11072042"},{"key":"1938_CR33","doi-asserted-by":"publisher","unstructured":"Liu X, Tian Z, Chen C (2021b, 2021) Total organic carbon content prediction in lacustrine shale using extreme gradient boosting machine learning based on Bayesian optimization. Geofluids:6155663. https:\/\/doi.org\/10.1155\/2021\/6155663","DOI":"10.1155\/2021\/6155663"},{"issue":"6","key":"1938_CR34","doi-asserted-by":"publisher","first-page":"143e147","DOI":"10.1016\/j.ngib.2015.07.004","volume":"16","author":"J Lu","year":"2016","unstructured":"Lu J, Lu J, Wu Q, Jin W, Hao S (2016) A study and an application on logging evaluation method of TOC in shale oil and gas reservoir. Sci Technol Eng 16(6):143e147. https:\/\/doi.org\/10.1016\/j.ngib.2015.07.004","journal-title":"Sci Technol Eng"},{"key":"1938_CR35","unstructured":"Lundberg S, Lee S (2017) A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874"},{"key":"1938_CR36","doi-asserted-by":"publisher","first-page":"10658","DOI":"10.1038\/s41598-025-91224-4","volume":"15","author":"BS Mac\u00eado","year":"2025","unstructured":"Mac\u00eado BS, Wayo DDK, Campos D, Santis RBD, Martinho AD, Yaseen ZM, Saporetti CM, Goliatt L (2025) Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework. Sci Rep 15:10658. https:\/\/doi.org\/10.1038\/s41598-025-91224-4","journal-title":"Sci Rep"},{"key":"1938_CR37","doi-asserted-by":"publisher","first-page":"103244","DOI":"10.1016\/j.jngse.2020.103244","volume":"77","author":"F Male","year":"2020","unstructured":"Male F, Jensen JL, Lake LW (2020) Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches. J Nat Gas Sci Eng 77:103244. https:\/\/doi.org\/10.1016\/j.jngse.2020.103244","journal-title":"J Nat Gas Sci Eng"},{"key":"1938_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3997\/2214-4609.201700905","volume":"1","author":"EB McCreery","year":"2017","unstructured":"McCreery EB, Al-Mudhafar WJ (2017) Geostatistical classification of lithology using partitioning algorithms on well log data - a case study in Forest Hill oil field, East Texas Basin. EAGE Conf Exhib 1:1\u20135. https:\/\/doi.org\/10.3997\/2214-4609.201700905","journal-title":"EAGE Conf Exhib"},{"key":"1938_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.ocemod.2013.08.003","volume":"72","author":"L Mentaschi","year":"2013","unstructured":"Mentaschi L, Besio G, Cassola F, Mazzino A (2013) Problems in RMSE-based wave model validations. Ocean Model 72:53\u201358. https:\/\/doi.org\/10.1016\/j.ocemod.2013.08.003","journal-title":"Ocean Model"},{"key":"1938_CR40","doi-asserted-by":"publisher","first-page":"133e137","DOI":"10.1515\/geo-2017-0011","volume":"7","author":"X Nie","year":"2017","unstructured":"Nie X, Wan Y, Bie F (2017) Dual-shale-content method for total organic carbon content evaluation from wireline logs in organic shale. Open Geosci 7:133e137. https:\/\/doi.org\/10.1515\/geo-2017-0011","journal-title":"Open Geosci"},{"key":"1938_CR41","doi-asserted-by":"publisher","first-page":"173743","DOI":"10.1016\/j.scitotenv.2024.173743","volume":"943","author":"H Oh","year":"2024","unstructured":"Oh H, Park HY, Kim JI, Lee BJ, Choi JH, Hur J (2024) Enhancing machine learning models for total organic carbon prediction by integrating geospatial parameters in river watersheds. Sci Total Environ 943:173743. https:\/\/doi.org\/10.1016\/j.scitotenv.2024.173743","journal-title":"Sci Total Environ"},{"key":"1938_CR42","doi-asserted-by":"publisher","first-page":"100009","DOI":"10.1016\/j.jfueco.2021.100009","volume":"7","author":"C Onwumelu","year":"2021","unstructured":"Onwumelu C, Kolawole O, Nordeng S (2021) Maturation-induced modification of organic matter in shales: implications for geological CO2 storage. Fuel Commun 7:100009. https:\/\/doi.org\/10.1016\/j.jfueco.2021.100009","journal-title":"Fuel Commun"},{"key":"1938_CR43","doi-asserted-by":"publisher","first-page":"107374","DOI":"10.1016\/j.petrol.2020.107374","volume":"193","author":"B Pan","year":"2020","unstructured":"Pan B, Li Y, Zhang M, Wang X, Iglauer S (2020) Effect of total organic carbon (TOC) content on shale wettability at high pressure and high temperature conditions. J Pet Sci Eng 193:107374. https:\/\/doi.org\/10.1016\/j.petrol.2020.107374","journal-title":"J Pet Sci Eng"},{"key":"1938_CR44","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1016\/j.ijhydene.2023.12.298","volume":"56","author":"B Pan","year":"2024","unstructured":"Pan B, Song T, Yue M, Chen S, Zhang L, Edlmann K, Neil CW, Zhu W, Iglauer S (2024) Machine learning - based shale wettability prediction: implications for H2, CH4 and CO2 geo-storage. Int J Hydrog Energy 56:1384\u20131390. https:\/\/doi.org\/10.1016\/j.ijhydene.2023.12.298","journal-title":"Int J Hydrog Energy"},{"key":"1938_CR45","first-page":"1777","volume":"74","author":"Q Passey","year":"1990","unstructured":"Passey Q, Creaney S, Kulla J, Moretti F, Stroud J (1990) A practical model for organic richness from porosity and resistivity logs. AAPG Bull 74:1777\u20131794","journal-title":"AAPG Bull"},{"issue":"24","key":"1938_CR46","doi-asserted-by":"publisher","first-page":"14892","DOI":"10.1109\/JSEN.2020.3010134","volume":"20","author":"AS Pattanayak","year":"2020","unstructured":"Pattanayak AS, Pattnaik BS, Udgata SK, Panda AK (2020) Development of chemical oxygen on demand (COD) soft sensor using edge intelligence. IEEE Sensors J 20(24):14892\u201314902. https:\/\/doi.org\/10.1109\/JSEN.2020.3010134","journal-title":"IEEE Sensors J"},{"key":"1938_CR47","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s12145-024-01681-0","volume":"18","author":"M Rahimi","year":"2025","unstructured":"Rahimi M, Alizadeh B, Seyedali SM (2025) Source rock characterization using seismic data inversion and well log analysis; a case study from Kazhdumi formation, NW Persian Gulf. Earth Sci Inform 18:231. https:\/\/doi.org\/10.1007\/s12145-024-01681-0","journal-title":"Earth Sci Inform"},{"key":"1938_CR48","doi-asserted-by":"publisher","first-page":"109463","DOI":"10.1016\/j.petrol.2021.109463","volume":"208","author":"A Rostamian","year":"2022","unstructured":"Rostamian A, Heidaryan E, Ostadhassan M (2022) Evaluation of different machine learning frameworks to predict CNL-FDC-PEF logs via hyperparameters optimization and feature selection. J Pet Sci Eng 208:109463. https:\/\/doi.org\/10.1016\/j.petrol.2021.109463","journal-title":"J Pet Sci Eng"},{"key":"1938_CR49","doi-asserted-by":"publisher","first-page":"105783","DOI":"10.1016\/j.marpetgeo.2022.105783","volume":"143","author":"CM Saporetti","year":"2022","unstructured":"Saporetti CM, Fonseca DL, Oliveira LC, Pereira E, Goliatt L (2022) Hybrid machine learning models for estimating total organic carbon from mineral constituents in core samples of shale gas fields. Mar Pet Geol 143:105783. https:\/\/doi.org\/10.1016\/j.marpetgeo.2022.105783","journal-title":"Mar Pet Geol"},{"key":"1938_CR50","first-page":"1285","volume":"65","author":"JW Schmoker","year":"1981","unstructured":"Schmoker JW (1981) Determination of organic-matter content of Appalachian Devonian shales from gamma-ray logs. AAPG (Am Assoc Pet Geol) Bull 65:1285\u20131298","journal-title":"AAPG (Am Assoc Pet Geol) Bull"},{"key":"1938_CR51","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.petrol.2019.01.055","volume":"176","author":"MR Shalaby","year":"2019","unstructured":"Shalaby MR, Jumat N, Lai D, Malik O (2019) Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: case study from the Jurassic source rocks in shams field, NW Desert, Egypt. J Petrol Sci Eng 176:369\u2013380. https:\/\/doi.org\/10.1016\/j.petrol.2019.01.055","journal-title":"J Petrol Sci Eng"},{"issue":"6","key":"1938_CR52","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1007\/s13202-020-00906-4","volume":"10","author":"MR Shalaby","year":"2020","unstructured":"Shalaby MR, Malik OA, Lai D, Jumat N, Islam MA (2020) Thermal maturity and TOC prediction using machine learning techniques: case study from the cretaceous\u2013Paleocene source rock, Taranaki Basin, New Zealand. J Petrol Explor Prod Technol 10(6):2175\u20132193. https:\/\/doi.org\/10.1007\/s13202-020-00906-4","journal-title":"J Petrol Explor Prod Technol"},{"key":"1938_CR53","doi-asserted-by":"publisher","first-page":"26726","DOI":"10.3390\/s151026726","volume":"15","author":"X Shao","year":"2015","unstructured":"Shao X, Li H, Wang N, Zhang Q (2015) Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends. Sensors 15:26726\u201326742","journal-title":"Sensors"},{"key":"1938_CR54","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.jngse.2016.05.060","volume":"33","author":"X Shi","year":"2016","unstructured":"Shi X, Wang J, Liu G, Yang L, Ge X, Jiang S (2016) Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs. J Nat Gas Sci Eng 33:687\u2013702. https:\/\/doi.org\/10.1016\/j.jngse.2016.05.060","journal-title":"J Nat Gas Sci Eng"},{"key":"1938_CR55","doi-asserted-by":"publisher","unstructured":"Sun J, Dang W, Wang F, Nie H, Wei X, Li P, Zhang S, Feng Y, Li F (2023) Prediction of TOC content in organic-rich shale using machine learning algorithms: comparative study of random Forest, Support Vector Machine, and XGBoost. Energies 16(10). https:\/\/doi.org\/10.3390\/en16104159","DOI":"10.3390\/en16104159"},{"key":"1938_CR56","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.jngse.2015.07.008","volume":"26","author":"M Tan","year":"2015","unstructured":"Tan M, Song X, Yang X, Wu Q (2015) Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: a comparative study. J Nat Gas Sci Eng 26:792\u2013802. https:\/\/doi.org\/10.1016\/j.jngse.2015.07.008","journal-title":"J Nat Gas Sci Eng"},{"key":"1938_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jngse.2018.03.029","volume":"55","author":"P Wang","year":"2018","unstructured":"Wang P, Peng S, He TH (2018) A novel approach to total organic carbon content prediction in shale gas reservoirs with well logs data, Tonghua Basin, China. J Nat Gas Sci Eng 55:1\u201315. https:\/\/doi.org\/10.1016\/j.jngse.2018.03.029","journal-title":"J Nat Gas Sci Eng"},{"key":"1938_CR58","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.marpetgeo.2015.11.023","volume":"70","author":"P Wang","year":"2016","unstructured":"Wang P, Chen Z, Pang X, Hu K, Sun M, Chen X (2016) Revised models for determining TOC in shale play: example from Devonian Duvernay shale, Western Canada Sedimentary Basin. Mar Petrol Geol 70:304\u2013319. https:\/\/doi.org\/10.1016\/j.marpetgeo.2015.11.023","journal-title":"Mar Petrol Geol"},{"key":"1938_CR59","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.petrol.2019.01.096","volume":"176","author":"H Wang","year":"2019","unstructured":"Wang H, Wu W, Chen T, Dong X, Wang G (2019) An improved neural network for TOC, S1 and S2 estimation based on conventional well logs. J Pet Sci Eng 176:664\u2013678. https:\/\/doi.org\/10.1016\/j.petrol.2019.01.096","journal-title":"J Pet Sci Eng"},{"issue":"1","key":"1938_CR60","doi-asserted-by":"publisher","first-page":"103e112","DOI":"10.7523\/j.issn.2095-6134.2020.01.012","volume":"37","author":"H Wang","year":"2020","unstructured":"Wang H, Zhao G, Li L, Zhang W, Qi R, Liu J (2020) TOC prediction model for muddy source rocks based on convolutional neural network (CNN): a case study of the Hangjinqi area of the Ordos Basin. J University Chinese Acad Sci 37(1):103e112. https:\/\/doi.org\/10.7523\/j.issn.2095-6134.2020.01.012","journal-title":"J University Chinese Acad Sci"},{"key":"1938_CR61","doi-asserted-by":"publisher","unstructured":"Wang H, Lu S, Qiao L, Chen F, He X, Gao Y, Mei J (2022a) Unsupervised contrastive learning for few-shot TOC prediction and application. Int J Coal Geol 259:104046. https:\/\/doi.org\/10.1016\/j.coal.2022.104046","DOI":"10.1016\/j.coal.2022.104046"},{"key":"1938_CR62","doi-asserted-by":"crossref","unstructured":"Wang K, Zhang Z, Wu X, Zhang L (2022b) Multi-class object detection in tunnels from 3D point clouds: An auto-optimized lazy learning approach. Adv Eng Inform 52:101543. https:\/\/doi.org\/10.1016\/j.aei.2022.101543","DOI":"10.1016\/j.aei.2022.101543"},{"issue":"12","key":"1938_CR63","doi-asserted-by":"publisher","first-page":"11119","DOI":"10.1021\/acs.energyfuels.4c01423","volume":"38","author":"W Wei","year":"2024","unstructured":"Wei W, Lu P, Zhu C, Luo P, Mesdour R (2024) Advanced machine learning models for CO2 and H2S solubility in water and NaCl brine: implications for Geoenergy extraction and carbon storage. Energy Fuel 38(12):11119\u201311136. https:\/\/doi.org\/10.1021\/acs.energyfuels.4c01423","journal-title":"Energy Fuel"},{"key":"1938_CR64","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.physa.2018.10.060","volume":"517","author":"J Xiao","year":"2019","unstructured":"Xiao J (2019) SVM and KNN ensemble learning for traffic incident detection. Phys A 517:29\u201335. https:\/\/doi.org\/10.1016\/j.physa.2018.10.060","journal-title":"Phys A"},{"key":"1938_CR65","doi-asserted-by":"publisher","first-page":"28808","DOI":"10.1109\/ACCESS.2019.2955754","volume":"8","author":"W Xing","year":"2020","unstructured":"Xing W, Bei Y (2020) Medical health big data classification based on KNN classification algorithm. IEEE Access 8:28808\u201328819. https:\/\/doi.org\/10.1109\/ACCESS.2019.2955754","journal-title":"IEEE Access"},{"key":"1938_CR66","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.coal.2017.06.011","volume":"179","author":"H Yu","year":"2017","unstructured":"Yu H, Rezaee R, Wang Z, Han T, Zhang Y, Arif M, Johnson L (2017) A new method for TOC estimation in tight shale gas reservoirs. Int J Coal Geol 179:269\u2013277. https:\/\/doi.org\/10.1016\/j.coal.2017.06.011","journal-title":"Int J Coal Geol"},{"key":"1938_CR67","doi-asserted-by":"publisher","first-page":"104064","DOI":"10.1016\/j.jngse.2021.104064","volume":"93","author":"B Zeng","year":"2021","unstructured":"Zeng B, Li M, Zhu J, Wang X, Shi Y, Zhu Z, Guo H, Wang F (2021) Selective methods of TOC content estimation for organic-rich interbedded mudstone source rocks. J Nat Gas Sci Eng 93:104064. https:\/\/doi.org\/10.1016\/j.jngse.2021.104064","journal-title":"J Nat Gas Sci Eng"},{"key":"1938_CR68","doi-asserted-by":"publisher","first-page":"111271","DOI":"10.1016\/j.petrol.2022.111271","volume":"221","author":"H Zhang","year":"2023","unstructured":"Zhang H, Wu W, Wu H (2023) TOC prediction using a gradient boosting decision tree method: a case study of shale reservoirs in Qinshui Basin. Geoenergy Sci Eng 221:111271. https:\/\/doi.org\/10.1016\/j.petrol.2022.111271","journal-title":"Geoenergy Sci Eng"},{"issue":"9","key":"1938_CR69","doi-asserted-by":"publisher","first-page":"6697","DOI":"10.1007\/s12517-014-1744-9","volume":"8","author":"J Zhang","year":"2015","unstructured":"Zhang J, Li J, Liu S, Li C (2015) Sedimentology and sequence stratigraphy of the second member of Shuangyang formation, Y45 block, Moliqing oilfield, Yitong Basin, China. Arab J Geosci 8(9):6697\u20136707. https:\/\/doi.org\/10.1007\/s12517-014-1744-9","journal-title":"Arab J Geosci"},{"key":"1938_CR70","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.apgeochem.2017.10.014","volume":"91","author":"R Zhao","year":"2018","unstructured":"Zhao R, Shan X, Yi J, Du X, Liang Y, Zhang Y (2018) Geochemistry of HCO3-Na thermal water from the Gudian slope: insights into fluid origin, formation mechanism and circulation in the Yitong Basin, Northeast China. Appl Geochem 91:185\u2013196. https:\/\/doi.org\/10.1016\/j.apgeochem.2017.10.014","journal-title":"Appl Geochem"},{"issue":"2","key":"1938_CR71","doi-asserted-by":"publisher","first-page":"100098","DOI":"10.1016\/j.engeos.2022.03.001","volume":"4","author":"L Zhu","year":"2023","unstructured":"Zhu L, Zhou X, Liu W, Kong Z (2023) Total organic carbon content logging prediction based on machine learning: a brief review. Energy Geosci 4(2):100098. https:\/\/doi.org\/10.1016\/j.engeos.2022.03.001","journal-title":"Energy Geosci"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01938-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-01938-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01938-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T17:23:26Z","timestamp":1757179406000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-01938-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":71,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1938"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-01938-2","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"10 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"428"}}