Jiyuan GAO , Nuoyu WANG , Yuyang LI , Zhongxian CAI , Heng ZHANG , Lin JIANG , Yan WANG , Shilin WANG
{"title":"Structural classification of Ordovician paleokarst conduits and intelligent quantitative prediction of filling degree in Tahe Oilfield, NW China","authors":"Jiyuan GAO , Nuoyu WANG , Yuyang LI , Zhongxian CAI , Heng ZHANG , Lin JIANG , Yan WANG , Shilin WANG","doi":"10.1016/S1876-3804(26)60702-1","DOIUrl":null,"url":null,"abstract":"<div><div>Based on 3D seismic, logging, and core data from the Tahe Oilfield, Tarim Basin, this study carried out logging-based identification of cave filling facies in drilled Ordovician paleokarst conduits and quantitative calculation of filling degree, and analyzed the internal structure of paleokarst conduits. On this basis, a quantitative prediction of the filling degree of conduit networks in plan view was achieved by constructing a nonlinear relationship model. The results show that, according to differences in petrophysical fabric, filling facies in drilled caves can be classified into fractured host-rock facies, sandy-muddy cemented conglomeratic clastic facies, transported sandstone facies, chemical sedimentary filling facies and unfilled cave facies. Using a convolutional neural network algorithm, the filling degree of 156 drilled caves in the study area was quantitatively calculated, among which caves with a filling degree greater than 80% account for 39.7%, whereas those with a filling degree less than 20% account for only 16.0%. The genetic types of paleokarst conduits were divided into 7 categories: main-stream conduits, tributary conduits, outflow conduits, along-stream conduits, turnaround conduits, sinking-river conduits and labyrinthine conduits; and six conduit morphologies were identified: sinkholes, hall-shaped chambers, underflow loops, horizontal underflow passages, corridor passages and medium-dip connecting passages. Furthermore, a quantitative prediction method for conduit filling degree was established based on a backpropagation neural network, with geological controlling factors as input variables. The prediction results indicate that the filling within paleokarst conduits shows obvious spatial differentiation: the probability of filling is relatively high in underflow loop segments, zones of increased potential energy, and medium-dip passage segments, whereas the spaces above hall-shaped chambers, the upper parts of medium-dip connecting passages, and downstream outlets of conduits have relatively low filling probabilities. The latter should therefore be regarded as key potential targets for future fine-scale development of paleokarst conduit reservoirs.</div></div>","PeriodicalId":67426,"journal":{"name":"Petroleum Exploration and Development","volume":"53 2","pages":"Pages 427-443"},"PeriodicalIF":10.7000,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petroleum Exploration and Development","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876380426607021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/4/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Based on 3D seismic, logging, and core data from the Tahe Oilfield, Tarim Basin, this study carried out logging-based identification of cave filling facies in drilled Ordovician paleokarst conduits and quantitative calculation of filling degree, and analyzed the internal structure of paleokarst conduits. On this basis, a quantitative prediction of the filling degree of conduit networks in plan view was achieved by constructing a nonlinear relationship model. The results show that, according to differences in petrophysical fabric, filling facies in drilled caves can be classified into fractured host-rock facies, sandy-muddy cemented conglomeratic clastic facies, transported sandstone facies, chemical sedimentary filling facies and unfilled cave facies. Using a convolutional neural network algorithm, the filling degree of 156 drilled caves in the study area was quantitatively calculated, among which caves with a filling degree greater than 80% account for 39.7%, whereas those with a filling degree less than 20% account for only 16.0%. The genetic types of paleokarst conduits were divided into 7 categories: main-stream conduits, tributary conduits, outflow conduits, along-stream conduits, turnaround conduits, sinking-river conduits and labyrinthine conduits; and six conduit morphologies were identified: sinkholes, hall-shaped chambers, underflow loops, horizontal underflow passages, corridor passages and medium-dip connecting passages. Furthermore, a quantitative prediction method for conduit filling degree was established based on a backpropagation neural network, with geological controlling factors as input variables. The prediction results indicate that the filling within paleokarst conduits shows obvious spatial differentiation: the probability of filling is relatively high in underflow loop segments, zones of increased potential energy, and medium-dip passage segments, whereas the spaces above hall-shaped chambers, the upper parts of medium-dip connecting passages, and downstream outlets of conduits have relatively low filling probabilities. The latter should therefore be regarded as key potential targets for future fine-scale development of paleokarst conduit reservoirs.