{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:34:45Z","timestamp":1771745685755,"version":"3.50.1"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 42222209"],"award-info":[{"award-number":["No. 42222209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Scientific Research and Technological Development Programs of CNPC","award":["No. 2023ZZ0801"],"award-info":[{"award-number":["No. 2023ZZ0801"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01515-z","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T07:42:15Z","timestamp":1736408535000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Transitional shale reservoir quality evaluation based on Random Forest algorithm\u2014a case study of the Shanxi Formation, eastern Ordos Basin, China"],"prefix":"10.1007","volume":"18","author":[{"given":"Wanli","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingtao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiliang","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianquan","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingfang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"1515_CR1","doi-asserted-by":"publisher","unstructured":"Abdulraheem A, Sabakhy E, Ahmed M et al (2007) Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic. SPE Middle East Oil and Gas Show and Conference, Bahrain. SPE-105350. https:\/\/doi.org\/10.2523\/105350-MS","DOI":"10.2523\/105350-MS"},{"issue":"3","key":"1515_CR2","doi-asserted-by":"crossref","first-page":"T735","DOI":"10.1190\/INT-2020-0184.1","volume":"9","author":"A Adewale","year":"2021","unstructured":"Adewale A, Sun Y (2021) Identification of thermally mature total organic carbon-rich layers in shale formations using an effective machine-learning approach. Interpretation 9(3):T735\u2013T745","journal-title":"Interpretation"},{"issue":"03","key":"1515_CR3","doi-asserted-by":"crossref","first-page":"485","DOI":"10.2118\/126339-PA","volume":"13","author":"A Al-Anazi","year":"2011","unstructured":"Al-Anazi A, Gates I (2011) Support-vector regression for permeability prediction in a heterogeneous reservoir: a comparative study. SPE Reserv Eval Eng 13(03):485\u2013495","journal-title":"SPE Reserv Eval Eng"},{"key":"1515_CR4","doi-asserted-by":"crossref","first-page":"107365","DOI":"10.1016\/j.geomorph.2020.107365","volume":"369","author":"H Alifu","year":"2020","unstructured":"Alifu H, Vuillaume J, Johnson B et al (2020) Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1\/-2 (SAR\/optical), Landsat 8 (thermal) and digital elevation data. Geomorphology 369:107365","journal-title":"Geomorphology"},{"key":"1515_CR5","doi-asserted-by":"publisher","unstructured":"Al-Mudhafer W (2014) Using generalized linear regression of multiple attributes for modeling and prediction the formation permeability in sandstone reservoir. Offshore Technol Conf. OTC-25158-MS. Houston, Texas. https:\/\/doi.org\/10.4043\/25158-MS","DOI":"10.4043\/25158-MS"},{"issue":"4","key":"1515_CR6","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1007\/s13202-017-0360-0","volume":"7","author":"W Al-Mudhafar","year":"2017","unstructured":"Al-Mudhafar W (2017) Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms. J Pet Explor Prod Technol 7(4):1023\u20131033","journal-title":"J Pet Explor Prod Technol"},{"issue":"1","key":"1515_CR7","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11053-018-9370-y","volume":"28","author":"W Al-Mudhafar","year":"2019","unstructured":"Al-Mudhafar W (2019) Bayesian and LASSO regressions for comparative permeability modeling of sandstone reservoirs. Nat Resour Res 28(1):47\u201362","journal-title":"Nat Resour Res"},{"key":"1515_CR8","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11001-018-9370-7","volume":"40","author":"W Al-Mudhafar","year":"2019","unstructured":"Al-Mudhafar W (2019) Integrating lithofacies and well logging data into smooth generalized additive model for improved permeability estimation: Zubair formation, South Rumaila oil field. Mar Geophys Res 40:315\u2013332","journal-title":"Mar Geophys Res"},{"key":"1515_CR9","doi-asserted-by":"crossref","first-page":"105886","DOI":"10.1016\/j.marpetgeo.2022.105886","volume":"145","author":"W Al-Mudhafar","year":"2022","unstructured":"Al-Mudhafar W, Abbas M, Wood D (2022) Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs. Mar Petrol Geol 145:105886","journal-title":"Mar Petrol Geol"},{"key":"1515_CR10","doi-asserted-by":"publisher","unstructured":"Al-Mudhafar W, Bondarenko M Integrating K-means clustering analysis and generalized additive model for efficient reservoir characterization. In 77th EAGE, Conference (2015) and Exhibition 1, 1\u20136. European Association of Geoscientists & Engineers. https:\/\/doi.org\/10.3997\/2214-4609.201413024","DOI":"10.3997\/2214-4609.201413024"},{"issue":"4","key":"1515_CR11","first-page":"1030","volume":"40","author":"G Cai","year":"2022","unstructured":"Cai G, Jiang Y, Li X et al (2022) Comparison of characteristics of Transitional and Marine Organicrich Shale reservoirs. Acta Sedimentol Sin 40(4):1030\u20131042","journal-title":"Acta Sedimentol Sin"},{"issue":"3","key":"1515_CR12","first-page":"169","volume":"8","author":"T Cao","year":"2023","unstructured":"Cao T, Deng M, Xiao J et al (2023) Reservoir characteristics of marine\u2013continental transitional shale and gas-bearing mechanism: understanding based on comparison with marine shale reservoir. Nat Gas Geosci 8(3):169\u2013185","journal-title":"Nat Gas Geosci"},{"key":"1515_CR13","doi-asserted-by":"publisher","unstructured":"Chang Q, Ruan Z, Yu B et al (2024) Data-Driven classification and logging prediction of Mudrock lithofacies using machine learning: Shale Oil reservoirs in the Eocene Shahejie formation, Bonan Sag, Bohai Bay Basin, Eastern China. Minerals 14(4). https:\/\/doi.org\/10.3390\/min14040370","DOI":"10.3390\/min14040370"},{"issue":"4","key":"1515_CR14","first-page":"597","volume":"55","author":"C Chen","year":"2016","unstructured":"Chen C, Qu D, Wang M et al (2016) Prediction method of gas content in marine mud shale at JSB area in southeast Sichuan basin. Geophys Prospect Pet 55(4):597\u2013605","journal-title":"Geophys Prospect Pet"},{"key":"1515_CR15","doi-asserted-by":"crossref","first-page":"103625","DOI":"10.1016\/j.jngse.2020.103625","volume":"83","author":"Y Chen","year":"2020","unstructured":"Chen Y, Wang Y, Guo M et al (2020) Differential enrichment mechanism of organic matters in the marine-continental transitional shale in northeastern Ordos Basin, China: control of sedimentary environments. J Nat Gas Sci Eng 83:103625","journal-title":"J Nat Gas Sci Eng"},{"issue":"5","key":"1515_CR16","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1093\/jge\/gxab049","volume":"18","author":"Y Chen","year":"2021","unstructured":"Chen Y, Zhao L, Pan J et al (2021) Deep carbonate reservoir characterisation using multi-seismic attributes via machine learning with physical constraints. J Geophys Eng 18(5):761\u2013775","journal-title":"J Geophys Eng"},{"issue":"5","key":"1515_CR17","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/S1876-3804(22)60332-X","volume":"49","author":"B Cheng","year":"2022","unstructured":"Cheng B, Xu T, Luo S et al (2022) Method and practice of deep favorable shale reservoirs prediction based on machine learning. Petrol Explor Dev + 49(5):1056\u20131068","journal-title":"Petrol Explor Dev +"},{"key":"1515_CR18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.marpetgeo.2017.01.001","volume":"81","author":"Y Cui","year":"2017","unstructured":"Cui Y, Wang G, Stuart J et al (2017) Prediction of diagenetic facies using well logs\u2013A case study from the upper Triassic Yanchang Formation, Ordos Basin, China. Mar Petrol Geol 81:50\u201365","journal-title":"Mar Petrol Geol"},{"key":"1515_CR19","doi-asserted-by":"crossref","unstructured":"Dev A, Eden R (2019) Formation lithology classification using scalable gradient boosted decision trees. Comput Ch Eng 128:392\u2013404","DOI":"10.1016\/j.compchemeng.2019.06.001"},{"key":"1515_CR20","doi-asserted-by":"publisher","unstructured":"Dias P, Lunga D (2022) Embedding ethics and trustworthiness for sustainable AI in Earth sciences: Where do we begin? IGARSS 4639\u20134642. https:\/\/doi.org\/10.1109\/IGARSS46834.2022.9883030","DOI":"10.1109\/IGARSS46834.2022.9883030"},{"issue":"3","key":"1515_CR21","first-page":"19","volume":"26","author":"J Fu","year":"2021","unstructured":"Fu J, Dong G, Zhou X et al (2021) Research progress of petroleum geology and exploration technology in Ordos Basin. China Pet Explor 26(3):19\u201340","journal-title":"China Pet Explor"},{"issue":"8","key":"1515_CR22","first-page":"1289","volume":"34","author":"J Fu","year":"2023","unstructured":"Fu J, Zhao H, Dong G et al (2023) Discovery and prospect of oil and gas exploration in new areas of Ordos Basin. Nat Gas Geosci 34(8):1289\u20131304","journal-title":"Nat Gas Geosci"},{"key":"1515_CR23","doi-asserted-by":"crossref","first-page":"102978","DOI":"10.1016\/j.geothermics.2024.102978","volume":"119","author":"W Gao","year":"2024","unstructured":"Gao W, Zhao J (2024) Deep-time temperature field simulation of hot dry rock: a deep learning method in both time and space dimensions. Geothermics 119:102978","journal-title":"Geothermics"},{"issue":"1","key":"1515_CR24","first-page":"318","volume":"41","author":"Y Gu","year":"2023","unstructured":"Gu Y, Cai G, Li S et al (2023) Pore structure and controlling factors of different lithofacies in transitional shale: a case study of the shanxi formation shan23 submember, eastern ordos basin. Acta Sedimentol Sin 41(1):318\u2013332","journal-title":"Acta Sedimentol Sin"},{"key":"1515_CR25","doi-asserted-by":"publisher","unstructured":"Gu Y, Li X, Qi L et al (2022) Sedimentology and geochemistry of the lower permian shanxi formation shan23 submember transitional shale, eastern ordos basin, North China. Front Earth Sci 10. https:\/\/doi.org\/10.3389\/feart.2022.859845","DOI":"10.3389\/feart.2022.859845"},{"key":"1515_CR26","doi-asserted-by":"crossref","unstructured":"Hao R, Huang W, Bo J et al (2024) Fractal Characteristics and Main Controlling Factors of High-Quality Tight Sandstone Reservoirs in the Southeastern Ordos Basin. J Earth Sci 35(2):631\u2013641","DOI":"10.1007\/s12583-021-1514-z"},{"key":"1515_CR27","doi-asserted-by":"crossref","first-page":"104347","DOI":"10.1016\/j.marpetgeo.2020.104347","volume":"116","author":"A Handhal","year":"2020","unstructured":"Handhal A, Al-Abadi A, Chafeet H et al (2020) Prediction of total organic carbon at rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms. Mar Petrol Geol 116:104347","journal-title":"Mar Petrol Geol"},{"key":"1515_CR28","doi-asserted-by":"publisher","unstructured":"Ho T (1995) Random decision forest. In Proceedings of the 3rd International Conference on Document Analysis and Recognition .https:\/\/doi.org\/10.1109\/ICDAR.1995.598994","DOI":"10.1109\/ICDAR.1995.598994"},{"issue":"11","key":"1515_CR29","doi-asserted-by":"crossref","first-page":"e2021JB022476","DOI":"10.1029\/2021JB022476","volume":"126","author":"R Huang","year":"2021","unstructured":"Huang R, Liu S, Qi R et al (2021) Deep learning 3D sparse inversion of gravity data. JGR Solid Earth 126(11):e2021JB022476","journal-title":"JGR Solid Earth"},{"key":"1515_CR30","doi-asserted-by":"crossref","first-page":"106454","DOI":"10.1016\/j.marpetgeo.2023.106454","volume":"156","author":"Y Huang","year":"2023","unstructured":"Huang Y, Wang G, Zhang Y et al (2023) Logging evaluation of pore structure and reservoir quality in shale oil reservoir: the Fengcheng formation in Mahu Sag, Junggar Basin, China. Mar Petrol Geol 156:106454","journal-title":"Mar Petrol Geol"},{"key":"1515_CR31","doi-asserted-by":"crossref","first-page":"211721","DOI":"10.1016\/j.geoen.2023.211721","volume":"226","author":"D Jiang","year":"2023","unstructured":"Jiang D, Chen H, Xing J et al (2023) A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network. Geoenergy Sci Eng 226:211721","journal-title":"Geoenergy Sci Eng"},{"key":"1515_CR32","unstructured":"Jiang Y, Wen S, Cai G et al (2023b) Lithologic assemblage characteristics and shale gas exploration potential of transitional shale in the Ordos Basin. Nat Gas Ind 43(4):62\u201375"},{"key":"1515_CR33","doi-asserted-by":"crossref","unstructured":"Jiang F, Chen X, Wang P, et al (2024b) Genesis and Accumulation of Paleo-Oil Reservoir in Dabei Area, Kuqa Depression, Northwest China: Implications for Tight-Gas Accumulation. J Earth Sci 35(2): 655\u2013665.","DOI":"10.1007\/s12583-021-1562-4"},{"issue":"4","key":"1515_CR34","first-page":"11","volume":"43","author":"F Jiao","year":"2023","unstructured":"Jiao F, Wen S, Liu X et al (2023) Research progress in exploration theory and technology of transitional shale gas in the ordos Basin. Nat Gas Ind 43(4):11\u201323","journal-title":"Nat Gas Ind"},{"key":"1515_CR35","doi-asserted-by":"crossref","first-page":"105597","DOI":"10.1016\/j.marpetgeo.2022.105597","volume":"139","author":"M Kamali","year":"2022","unstructured":"Kamali M, Davoodi S, Ghorbani H et al (2022) Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Mar Petrol Geol 139:105597","journal-title":"Mar Petrol Geol"},{"issue":"8","key":"1515_CR36","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TKDE.2018.2861006","volume":"31","author":"A Karpatne","year":"2019","unstructured":"Karpatne A, Ebert-Uphoff I, Ravela S et al (2019) Machine learning for the geosciences: challenges and opportunities. IEEE Trans Knowl Data Eng 31(8):1544\u20131554","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"1515_CR37","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S1876-3804(20)60066-0","volume":"47","author":"L Kuang","year":"2020","unstructured":"Kuang L, Dong D, He W et al (2020) Geological characteristics and development potential of transitional shale gas in the east margin of the Ordos Basin. NW China. Petrol Explor Dev+ 47(3):471\u2013482","journal-title":"NW China. Petrol Explor Dev+"},{"key":"1515_CR38","doi-asserted-by":"crossref","first-page":"112294","DOI":"10.1016\/j.rse.2021.112294","volume":"255","author":"S Kuter","year":"2021","unstructured":"Kuter S (2021) Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression. Remote Sens Environ 255:112294","journal-title":"Remote Sens Environ"},{"key":"1515_CR39","doi-asserted-by":"publisher","unstructured":"Lacentre P, Carrica P (2003) A method to estimate permeability on uncored wells based on well logs and core data. SPE Latin American and Caribbean Petroleum Engineering Conference, Port-of-Spain, Trinidad and Tobago. SPE-81058. https:\/\/doi.org\/10.2523\/81058-MS","DOI":"10.2523\/81058-MS"},{"key":"1515_CR40","doi-asserted-by":"crossref","first-page":"106183","DOI":"10.1016\/j.petrol.2019.106183","volume":"181","author":"G Li","year":"2019","unstructured":"Li G, Qin Y, Wu M et al (2019) The pore structure of the transitional shale in the Taiyuan formation, Linxing area, Ordos Basin. J Petrol Sci Eng 181:106183","journal-title":"J Petrol Sci Eng"},{"key":"1515_CR41","doi-asserted-by":"crossref","unstructured":"Li K, Xi K, Cao Y et al (2021a) Chlorite authigenesis and its impact on reservoir quality in tight sandstone reservoirs of the Triassic Yanchang formation, southwestern Ordos basin, China. J Petrol Sci Eng 205:108843","DOI":"10.1016\/j.petrol.2021.108843"},{"key":"1515_CR42","doi-asserted-by":"crossref","unstructured":"Li K, Xi Y, Su Z et al (2021b) Research on reservoir lithology prediction method based on convolutional recurrent neural network. Comput Electr Eng 95:107404","DOI":"10.1016\/j.compeleceng.2021.107404"},{"key":"1515_CR43","doi-asserted-by":"crossref","first-page":"104939","DOI":"10.1016\/j.marpetgeo.2021.104939","volume":"126","author":"J Liu","year":"2021","unstructured":"Liu J, Liu J (2021) An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm-A case study of the Yanchang Formation, mid-eastern Ordos Basin, China. Mar Petrol Geol 126:104939","journal-title":"Mar Petrol Geol"},{"issue":"3","key":"1515_CR44","doi-asserted-by":"crossref","first-page":"SE41","DOI":"10.1190\/INT-2021-0173.1","volume":"10","author":"D Lubo-Robles","year":"2022","unstructured":"Lubo-Robles D, Devegowda D, Jayaram V et al (2022) Quantifying the sensitivity of seismic facies classification to seismic attribute selection: an explainable machine-learning study. Interpretation 10(3):SE41\u2013SE69","journal-title":"Interpretation"},{"issue":"1","key":"1515_CR45","first-page":"23","volume":"41","author":"B Ma","year":"2018","unstructured":"Ma B, Huang T, Zou D et al (2018) A seismic-based quantitative method to predict gas content of marine shales: examples from changning area, southern sichuan basin. Nat Gas Explor Dev 41(1):23\u201329","journal-title":"Nat Gas Explor Dev"},{"key":"1515_CR46","doi-asserted-by":"publisher","unstructured":"Maskey M, Ramachandran R, Gurung I et al (2022) Artificial intelligence vis-\u00e0-vis data systems. IGARSS 5081\u20135084https:\/\/doi.org\/10.1109\/IGARSS46834.2022.9883626","DOI":"10.1109\/IGARSS46834.2022.9883626"},{"issue":"3","key":"1515_CR47","doi-asserted-by":"crossref","first-page":"176","DOI":"10.2118\/84920-PA","volume":"6","author":"T Mathisen","year":"2003","unstructured":"Mathisen T, Lee S, Datta-Gupta A (2003) Improved permeability estimates in carbonate reservoirs using elec-trofacies characterization: a case study of the North Robertson Unit, West Texas. SPE Reserv Evaluation Eng 6(3):176\u2013184","journal-title":"SPE Reserv Evaluation Eng"},{"key":"1515_CR48","first-page":"1","volume":"1","author":"E McCreery","year":"2017","unstructured":"McCreery E (2017) Al-Mudhafar W (2017) Geostatistical classification of lithology using partitioning algorithms on well log data-a case study in forest hill oil field, East Texas Basin[C]\/\/79th EAGE Conference and Exhibition 2017. European Association of Geoscientists and Engineers 1:1\u20135","journal-title":"European Association of Geoscientists and Engineers"},{"key":"1515_CR49","doi-asserted-by":"publisher","unstructured":"Muhammad A, Peimin Z, Ren J et al (2024) Data-driven lithofacies prediction in complex tight sandstone reservoirs: a supervised workflow integrating clustering and classification models. Geomech Gophys Geo 10(1). https:\/\/doi.org\/10.1007\/s40948-024-00787-5","DOI":"10.1007\/s40948-024-00787-5"},{"key":"1515_CR50","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2015.07.019","volume":"168","author":"N Pastick","year":"2015","unstructured":"Pastick N, Jorgenson M, Wylie B et al (2015) Distribution of near-surface permafrost in Alaska: estimates of present and future conditions. Remote Sens Environ 168:301\u2013315","journal-title":"Remote Sens Environ"},{"issue":"2","key":"1515_CR51","doi-asserted-by":"crossref","first-page":"143","DOI":"10.2118\/84301-PA","volume":"8","author":"H Perez","year":"2005","unstructured":"Perez H, Datta-Gupta A, Mishra S (2005) The role of electrofacies, lithofacies, and hydraulic flow units in permeability predictions from well logs: a comparative analysis using classification trees. SPE Reserv Evaluation Eng 8(2):143\u2013155","journal-title":"SPE Reserv Evaluation Eng"},{"key":"1515_CR52","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1016\/j.marpetgeo.2019.01.011","volume":"102","author":"K Qian","year":"2019","unstructured":"Qian K, Ning J, Liu X et al (2019) A rock physics driven bayesian inversion for TOC in the Fuling Shale gas reservoir. Mar Petrol Geol 102:886\u2013898","journal-title":"Mar Petrol Geol"},{"key":"1515_CR53","doi-asserted-by":"crossref","first-page":"3726","DOI":"10.1016\/j.egyr.2021.06.056","volume":"7","author":"Z Qiu","year":"2021","unstructured":"Qiu Z, Song D, Zhang L et al (2021) The geochemical and pore characteristics of a typical marine\u2013continental-transitional gas shale: a case study of the Permian Shanxi Formation on the eastern margin of the Ordos Basin. Energy Rep 7:3726\u20133736","journal-title":"Energy Rep"},{"key":"1515_CR54","doi-asserted-by":"crossref","first-page":"106234","DOI":"10.1016\/j.marpetgeo.2023.106234","volume":"152","author":"A Ramdani","year":"2023","unstructured":"Ramdani A, Chandra V, Finkbeiner T et al (2023) Multi-scale geophysical characterization of microporous carbonate reservoirs utilizing machine learning techniques: an analog case study from an upper jubaila formation outcrop, Saudi Arabia. Mar Petrol Geol 152:106234","journal-title":"Mar Petrol Geol"},{"key":"1515_CR55","first-page":"106119","volume":"151","author":"M Rodrigues","year":"2023","unstructured":"Rodrigues M, Heather B, Matos M (2023) Seismic identification of carbonate reservoir sweet spots using unsupervised machine learning: a case study from Brazil deep water Aptian pre-salt data. Mar Petrol Geol 151:106119","journal-title":"Mar Petrol Geol"},{"key":"1515_CR56","doi-asserted-by":"crossref","first-page":"105783","DOI":"10.1016\/j.marpetgeo.2022.105783","volume":"143","author":"C Saporetti","year":"2022","unstructured":"Saporetti C, Fonseca D, Oliveira L et al (2022) Hybrid machine learning models for estimating total organic carbon from mineral constituents in core samples of shale gas fields. Mar Petrol Geol 143:105783","journal-title":"Mar Petrol Geol"},{"key":"1515_CR57","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.petrol.2019.01.055","volume":"176","author":"R Shalaby","year":"2019","unstructured":"Shalaby R, Jumat N, Lai D et al (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 Pet Sci Eng 176:369\u2013380","journal-title":"J Pet Sci Eng"},{"issue":"8","key":"1515_CR58","doi-asserted-by":"crossref","first-page":"3428","DOI":"10.3390\/app11083428","volume":"11","author":"H Sunwoo","year":"2021","unstructured":"Sunwoo H, Hyunjoong K (2021) Optimal feature set size in Random Forest Regression. Appl Sci 11(8):3428\u20133428","journal-title":"Appl Sci"},{"key":"1515_CR59","first-page":"1","volume":"1","author":"Z Tariq","year":"2021","unstructured":"Tariq Z, Aljawad M, Hasan A et al (2021) A systematic review of data science and machine learning applications to the oil and gas industry. J Pet Explor Prod Technol 1:1\u201336","journal-title":"J Pet Explor Prod Technol"},{"key":"1515_CR60","doi-asserted-by":"crossref","first-page":"105299","DOI":"10.1016\/j.marpetgeo.2021.105299","volume":"133","author":"S Wang","year":"2021","unstructured":"Wang S, Wang G, Huang L et al (2021) Logging evaluation of lamina structure and reservoir quality in shale oil reservoir of fengcheng formation in Mahu Sag, China. Mar Petrol Geol 133:105299","journal-title":"Mar Petrol Geol"},{"key":"1515_CR61","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.marpetgeo.2019.07.026","volume":"110","author":"A Wood","year":"2019","unstructured":"Wood A (2019) Lithofacies and stratigraphy prediction methodology exploiting an optimized nearest-neighbour algorithm to mine well-log data. Mar Petrol Geol 110:347\u2013367","journal-title":"Mar Petrol Geol"},{"key":"1515_CR62","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1007\/s12145-022-00829-0","volume":"3","author":"D Wood","year":"2022","unstructured":"Wood D (2022a) Carbonate\/siliciclastic lithofacies classification aided by well-log derivative, voatility and sequence boundary attributes combined with machine learning. Earth Sci Inf 3:1699\u20131721","journal-title":"Earth Sci Inf"},{"key":"1515_CR63","doi-asserted-by":"crossref","unstructured":"Wood D (2022b) Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis. Adv Geo-Energy Res 6(1):69\u201385","DOI":"10.46690\/ager.2022.01.06"},{"key":"1515_CR64","first-page":"132","volume":"3","author":"D Wood","year":"2022","unstructured":"Wood D (2022) Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence. Artif Intell Geosci 3:132\u2013147","journal-title":"Artif Intell Geosci"},{"key":"1515_CR65","doi-asserted-by":"crossref","unstructured":"Wu H, Xiong L, Ge Z et al (2019) Fine characterization and target window optimization of high-quality shale gas reservoirs in the Weiyuan area, Sichuan Basin. Nat Gas Ind B 6(5):463\u2013471","DOI":"10.1016\/j.ngib.2019.03.003"},{"key":"1515_CR66","unstructured":"Xiao Z, Wang J (2006) Image classification algorithm based on PCA and GMM. Computer Engineering and Design 11:1951\u20131953"},{"issue":"5","key":"1515_CR67","first-page":"1","volume":"37","author":"D Yan","year":"2013","unstructured":"Yan D, Huang W, Li A et al (2013) Preliminary analysis of marine-continental shale gas accumulation conditions and favorable areas in the upper paleozoic ordos basin. J Northeast Petroleum Univ 37(5):1\u20139","journal-title":"J Northeast Petroleum Univ"},{"key":"1515_CR68","doi-asserted-by":"crossref","first-page":"104282","DOI":"10.1016\/j.jngse.2021.104282","volume":"96","author":"X Yang","year":"2021","unstructured":"Yang X, Guo S (2021) Reservoirs characteristics and environments evolution of lower permian transitional shale in the Southern North China Basin: implications for shale gas exploration. J Nat Gas Sci Eng 96:104282","journal-title":"J Nat Gas Sci Eng"},{"key":"1515_CR69","first-page":"5258","volume":"Houston, Texas","author":"S Yerramilli","year":"2013","unstructured":"Yerramilli S, Yerramilli R, Vedanti N et al (2013) Integrated reservoir characterization of an unconventional reservoir using 3D seismic and well log data: a case study of Balol Field, India. SEG Annual Meeting Houston, Texas:5258","journal-title":"SEG Annual Meeting"},{"key":"1515_CR70","doi-asserted-by":"crossref","first-page":"106041","DOI":"10.1016\/j.marpetgeo.2022.106041","volume":"143","author":"Z Yu","year":"2023","unstructured":"Yu Z, Wang Z, Adenutsi C (2023) Genesis of authigenic clay minerals and their impacts on reservoir quality in tight conglomerate reservoirs of the triassic baikouquan formation in the Mahu Sag, Junggar Basin, Western China. Mar Petrol Geol 143:106041","journal-title":"Mar Petrol Geol"},{"key":"1515_CR71","doi-asserted-by":"crossref","first-page":"205365","DOI":"10.1016\/j.jgsce.2024.205365","volume":"127","author":"S Zhai","year":"2024","unstructured":"Zhai S, Geng S, Li C et al (2024) An improved convolutional neural network for predicting porous media permeability from rock thin sections. Gas Sci Eng 127:205365","journal-title":"Gas Sci Eng"},{"key":"1515_CR72","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s12517-017-3045-6","volume":"10","author":"J Zhang","year":"2017","unstructured":"Zhang J, Liu S, Li J et al (2017) Identification of sedimentary facies with well logs: an indirect approach with multinomial logistic regression and artificial neural network. Arab J Geosci 10:247","journal-title":"Arab J Geosci"},{"key":"1515_CR73","doi-asserted-by":"crossref","first-page":"110389","DOI":"10.1016\/j.palaeo.2021.110389","volume":"571","author":"L Zhang","year":"2021","unstructured":"Zhang L, Dong D, Qiu Z et al (2021) Sedimentology and geochemistry of Carboniferous-Permian Marine-continental transitional shales in the eastern Ordos Basin, North China. Palaeogeogr Palaeoclimatol Palaeoecol 571:110389","journal-title":"Palaeogeogr Palaeoclimatol Palaeoecol"},{"key":"1515_CR74","doi-asserted-by":"crossref","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","journal-title":"Geoenergy Sci Eng"},{"key":"1515_CR75","doi-asserted-by":"crossref","unstructured":"Zhang Q, Qiu Z, Zhang L et al (2022a) Characteristics and controlling factors of transitional shale gas reservoirs: an example from Permian Shanxi Formation, Daning-Jixian block, Ordos Basin, China. Nat Gas Geosci 7(3):147\u2013157","DOI":"10.1016\/j.jnggs.2022.06.004"},{"key":"1515_CR76","doi-asserted-by":"publisher","unstructured":"Zhang Q, Qiu Z, Zhao Q et al. (2022b) Composition effect on the Pore structure of Transitional Shale: a case study of the Permian Shanxi Formation in the Daning-Jixian Block at the Eastern Margin of the Ordos Basin. Front Earth Sci 9. https:\/\/doi.org\/10.3389\/feart.2021.802713","DOI":"10.3389\/feart.2021.802713"},{"issue":"9","key":"1515_CR77","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.3390\/en16093710","volume":"16","author":"Q Zhang","year":"2023","unstructured":"Zhang Q, Xiong W, Li X et al (2023) Discussion on Transitional Shale Gas Accumulation conditions from the Perspective of Source-Reservoir-Caprock Controlling Hydrocarbon: examples from Permian Shanxi Formation and Taiyuan Formation in the Eastern Margin of Ordos Basin, NW China. Energies 16(9):3710","journal-title":"Energies"},{"key":"1515_CR78","doi-asserted-by":"crossref","unstructured":"Zhang Y, Long M, Chen K et al (2023b) Skilful nowcasting of extreme precipitation with NowcastNet. Nature 619(7970):526\u2013532","DOI":"10.1038\/s41586-023-06184-4"},{"issue":"4","key":"1515_CR79","first-page":"27","volume":"37","author":"J Zhao","year":"2017","unstructured":"Zhao J, Shen C, Ren L et al (2017) Quantitative prediction of gas contents in different occurrence states of shale reservoirs: a case study of the Jiaoshiba Shale gasfield in the Sichuan basin. Nat Gas Ind 37(4):27\u201333","journal-title":"Nat Gas Ind"},{"key":"1515_CR80","doi-asserted-by":"crossref","first-page":"108815","DOI":"10.1016\/j.petrol.2021.108815","volume":"205","author":"B Zhao","year":"2021","unstructured":"Zhao B, Li R, Qin X et al (2021) Geochemical characteristics and mechanism of organic matter accumulation of marine-continental transitional shale of the lower permian Shanxi Formation, southeastern Ordos Basin, north China. J Petrol Sci Eng 205:108815","journal-title":"J Petrol Sci Eng"},{"key":"1515_CR81","doi-asserted-by":"crossref","first-page":"106419","DOI":"10.1016\/j.marpetgeo.2023.106419","volume":"156","author":"J Zhao","year":"2023","unstructured":"Zhao J, Fan Y, Ge X et al (2023) An intelligent identification method of interlayers in deep clastic rock\u2013An example of Donghe Sandstone in Hade Oilfield, Tarim Basin. Mar Petrol Geol 156:106419","journal-title":"Mar Petrol Geol"},{"issue":"5","key":"1515_CR82","first-page":"100691","volume":"5","author":"T Zhao","year":"2024","unstructured":"Zhao T, Wang S, Ouyang C et al (2024) Artificial intelligence for geoscience: Progress, challenges, and perspectives. Innovation 5(5):100691","journal-title":"Innovation"},{"issue":"6","key":"1515_CR83","first-page":"127","volume":"36","author":"X Zhou","year":"2017","unstructured":"Zhou X, Zhang Z, Zhang C et al (2017) Complex lithologic identification based on rough set-random forest algorism. Petroleum Geol Oilfield Dev Daqing 36(6):127\u2013133","journal-title":"Petroleum Geol Oilfield Dev Daqing"},{"key":"1515_CR84","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1007\/s11600-018-0180-8","volume":"66","author":"L Zhu","year":"2018","unstructured":"Zhu L, Zhang C, Zhang C et al (2018) Application of Multiboost-KELM algorithm to alleviate the collinearity of log curves for evaluating the abundance of organic matter in marine mud shale reservoirs: a case study in Sichuan Basin, China. Acta Geophys 66:983\u20131000","journal-title":"Acta Geophys"},{"key":"1515_CR85","doi-asserted-by":"crossref","unstructured":"Zou C, Yang Z, Zhang G, et al (2023) Theory, Technology and Practice of Unconventional Petroleum Geology. J Earth Sci 34(4): 951\u2013965","DOI":"10.1007\/s12583-023-2000-8"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01515-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01515-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01515-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:07:25Z","timestamp":1745654845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01515-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":85,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1515"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01515-z","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"26 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 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":"157"}}