Structural classification of Ordovician paleokarst conduits and intelligent quantitative prediction of filling degree in Tahe Oilfield, NW China

IF 10.7 Q1 ENERGY & FUELS
Petroleum Exploration and Development Pub Date : 2026-04-01 Epub Date: 2026-04-28 DOI:10.1016/S1876-3804(26)60702-1
Jiyuan GAO , Nuoyu WANG , Yuyang LI , Zhongxian CAI , Heng ZHANG , Lin JIANG , Yan WANG , Shilin WANG
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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.
塔河油田奥陶系古岩溶管道结构分类及充填度智能定量预测
以塔里木盆地塔河油田三维地震、测井、岩心资料为基础,利用测井资料对钻探的奥陶系古岩溶管道溶洞充填相进行了识别,定量计算了充填程度,分析了古岩溶管道内部结构。在此基础上,通过建立非线性关系模型,实现了平面上管网充填程度的定量预测。结果表明,根据岩石物性组构的不同,钻孔岩洞充填相可分为破碎性寄主岩相、砂泥胶结砾岩相、输运砂岩相、化学沉积充填相和非充填岩洞相。利用卷积神经网络算法对研究区156个钻孔洞体充填度进行了定量计算,其中充填度大于80%的洞体占39.7%,充填度小于20%的洞体仅占16.0%。将古岩溶导管的成因类型划分为干流导管、支流导管、流出导管、顺流导管、回转导管、沉河导管和迷宫式导管7类;确定了6种管道形态:天坑、厅室、底流环、水平底流通道、走廊通道和中倾角连接通道。在此基础上,以地质控制因素为输入变量,建立了基于反向传播神经网络的管道充填度定量预测方法。预测结果表明,古岩溶导管内充填具有明显的空间分异特征,下流环段、势能增大带和中倾通道段充填概率较高,而厅室上方、中倾连接通道上部和导管下游出口空间充填概率较低。因此,后者应被视为未来古岩溶导管储层精细开发的关键潜在目标。
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CiteScore
11.50
自引率
0.00%
发文量
473
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