塔河油田奥陶系古岩溶暗河结构划分与充填程度智能定量预测OA

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 host-rock facies within caves,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 passages.On this basis,a backpropagation neural-network-based quantitative prediction method for conduit filling degree was established using geological controlling factors.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.