[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127520-en":3,"doc-seo-127520-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127520,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Forecast of lacustrine shale lithofacies types in continental rift basins based on machine learning - case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China","Lacustrine shale in continental rift basins shows complex mineralogical compositions and microstructures, making lithofacies type prediction critical for shale oil reservoir quality. Traditional geophysical approaches cannot forecast lithofacies types reliably. Using the upper Es4 member of the Dongying Sag (Jiyang Depression, Bohai Bay Basin, China) as the study target, lithofacies types are forecast via SVM and XGBoost. Lithofacies are reclassified into 22 types from core and thin sections, and paleoenvironment parameters are integrated with well logging under two sample extraction modes.","TYPE Original Research PUBLISHED 20 April 2023  \nDOI 10.3389/feart.2023.1047981  \nOPEN ACCESS  \nEDITED BY  \nSid-Ali Ouadfeul, Sonatrach, Algeria  \nREVIEWED BY  \nChenyang Bai,  \nChina University of Geosciences, China Yong Niu,  \nShaoxing University, China Nan Xiao,  \nChangsha University of Science and Technology, China  \nWanju Yuan,  \nGeological Survey of Canada, Canada  \n*CORRESPONDENCE  \nLiqiang Zhang,  \n [zhanglq@upc.edu.cn](zhanglq@upc.edu.cn)  \nRECEIVED 19 September 2022  \nACCEPTED 04 April 2023  \nPUBLISHED 20 April 2023  \nCITATION  \nFang Z, Zhang L and Yan S (2023), Forecast of lacustrine shale lithofacies types in continental rift basins based on machine learning: A case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China.  \nFront. Earth Sci. 11:1047981 .  \ndoi: 10.3389/feart.2023.1047981  \nCOPYRIGHT  \n© 2023 Fang, Zhang and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nForecast of lacustrine shale lithofacies types in continental rift basins based on machine learning: A case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China  \nZhengwei Fang 1,2,3,4,5, Liqiang Zhang 1* and Shicui Yan 2  \n1School of Geosciences, China University of Petroleum (East China), Qingdao, China, 2Research Institute of Petroleum Exploration and Development, Shengli Oilfield, Dongying, China, 3Sinopec Key Laboratory of Shale Oil/Gas Exploration and Production, Shengli Oilfield Branch, Dongying, China, 4Shandong Provincial Key Laboratory of Unconventional Oil and Gas Exploration and Development, Dongying, China, 5Key Laboratory of Sedimentary Simulation and Reservoir Evaluation, Sinopec Shengli Oilfield, Dongying, China  \nLacustrine shale in continental rift basins is complex and features a variety of mineralogical compositions and microstructures. The lithofacies type of shale, mainly determined by mineralogical composition and microstructure, is the most critical factor controlling the quality of shale oil reservoirs. Conventional geophysical methods cannot accurately forecast lacustrine shale lithofacies types, thus restricting the progress of shale oil exploration and development. Considering the lacustrine shale in the upper Es4 member of the Dongying Sag in the Jiyang Depression, Bohai Bay Basin, China, as the research object, the lithofacies type was forecast based on two machine learning methods: support vector machine (SVM) and extreme gradient boosting (XGBoost) . To improve the forecast accuracy, we applied the following approaches: first, using core and thin section analyses of consecutively cored wells, the lithofacies were finelyreclassified into 22 types according to mineralogical composition and microstructure, and the vertical change of lithofacies types was obtained. Second, in addition to commonly used well logging data, paleoenvironment parameter data (Rb/Sr ratio, paleoclimate parameter; Sr %, paleosalinity parameter; Ti %, paleoprovenance parameter; Fe/Mn ratio, paleo-water depth parameter; P/Ti ratio, paleoproductivity parameter) were applied to the forecast. Third, two sample extraction modes, namely, curve shape-to-points and pointto-point, were used in the machine learning process. Finally, the lithofacies type forecast was carried out under six different conditions. In the condition of selecting the curved shape-to-point sample extraction mode and inputting both well logging and paleoenvironment parameter data, the SVM method achieved the highest average forecast accuracy for all lithofacies types, reaching 68%, as well as the highest average forecast accuracy for favorable lithofacies types at ","cbCaipfRHoCrMYRf","https://ap.wps.com/l/cbCaipfRHoCrMYRf","pdf",6083699,1,15,"English","en",105,"# Introduction\n## Shale oil and tectonic setting\n## Characteristics of lacustrine shale lithofacies\n# Methodology\n## Lithofacies reclassification based on core and thin sections\n## Machine learning models (SVM and XGBoost)\n## Input data: well logging and paleoenvironment parameters\n## Sample extraction modes and experimental conditions\n# Results\n## Forecast accuracy across lithofacies types\n## Effects of data combinations and extraction modes\n## Influence of learning sample quantity and overlap","[{\"question\":\"Why is forecasting lacustrine shale lithofacies types important for shale oil reservoirs?\",\"answer\":\"Lithofacies type, governed mainly by mineralogical composition and microstructure, controls shale oil reservoir quality. Accurate forecasting supports better exploration and development decisions.\"},{\"question\":\"What machine learning methods are used to forecast lithofacies types in this study?\",\"answer\":\"Support vector machine (SVM) and extreme gradient boosting (XGBoost) are used to forecast lacustrine shale lithofacies types.\"},{\"question\":\"Which factor combination produced the highest average forecast accuracy?\",\"answer\":\"Selecting the curve shape-to-point sample extraction mode and using both well logging data and paleoenvironment parameter data yields the highest average forecast accuracy for all lithofacies types, reaching 68%, and 98% for favorable lithofacies types.\"}]","Forecast of lacustrine shale lithofacies types in continental rift basins based on machine learning - case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China | PDF",1785939713,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecast-of-lacustrine-shale-lithofacies-types-in-continental-rift-basins-based-on-machine-learning-case-study-from-dongying-sag-jiyang-depression-bohai-bay-basin-china","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecast-of-lacustrine-shale-lithofacies-types-in-continental-rift-basins-based-on-machine-learning-case-study-from-dongying-sag-jiyang-depression-bohai-bay-basin-china/127520/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is forecasting lacustrine shale lithofacies types important for shale oil reservoirs?","Question",{"text":75,"@type":76},"Lithofacies type, governed mainly by mineralogical composition and microstructure, controls shale oil reservoir quality. Accurate forecasting supports better exploration and development decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning methods are used to forecast lithofacies types in this study?",{"text":80,"@type":76},"Support vector machine (SVM) and extreme gradient boosting (XGBoost) are used to forecast lacustrine shale lithofacies types.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factor combination produced the highest average forecast accuracy?",{"text":84,"@type":76},"Selecting the curve shape-to-point sample extraction mode and using both well logging data and paleoenvironment parameter data yields the highest average forecast accuracy for all lithofacies types, reaching 68%, and 98% for favorable lithofacies types.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]