基于BP神经网络预测轮古油田奥陶系碳酸盐岩油藏洞穴充填程度  

Prediction of Cave Filling Degree in Ordovician Carbonate Reservoirs Based on BP Neural Network in Lungu Oilfield,Tarim Basin

作　　者：于聪灵 蔡忠贤[1,2] 杨海军[3] 朱永峰[3] 王慧[1,2] YU Congling;CAI Zhongxian;YANG Haijun;ZHU Yongfeng;WANG Hui(China University of Geosciences,School of Resources,Hubei 430074,China;China University of Geosciences,MOE Key Laboratory of Tectonics and Petroleum Resources,Wuhan,Hubei 430074,China;;Research Institute of Exploration and Development,Tarim Oilfield Company,PetroChina,Korla,Xinjiang 841000,China)

机构地区：[1]中国地质大学,资源学院,武汉430074 [2]中国地质大学,构造与油气资源教育部重点实验室,武汉430074 [3]中国石油塔里木油田分公司勘探开发研究院,新疆库尔勒841000

出　　处：《新疆石油地质》2018年第5期614-621,共8页Xinjiang Petroleum Geology

基　　金：国家973项目(2005CB422105)

摘　　要：为了预测碳酸盐岩油藏洞穴型储集层的充填性,利用神经网络能够实现输入目标与输出目标之间的非线性映射,基于暗河网络、古地貌、古水系和暗河出入口的精细刻画,对暗河类型进行划分;利用测井、三维地震以及岩心资料描述单井钻遇洞穴的充填程度,结合单井钻遇洞穴的充填程度与过井暗河的类型之间的对应关系,对轮古油田暗河充填程度进行预测。通过对该地区暗河解剖,认为暗河充填程度的控制因素为暗河级别、暗河类型、洞道样式、与暗河出入口的关系、与厅堂洞的关系和是否垮塌6项,将其作为输入参数,建立暗河充填程度预测模型。预测结果表明,利用BP神经网络对暗河充填程度预测的相对误差在10%左右,可将该方法应用于碳酸盐岩油藏洞穴型储集层的评价。In order to predict the filling performance of caves in carbonate reservoirs,using the feature that the BP neural network can realize the linear mapping between input and output targets,and based on the detailed characterization of underground river network,ancient landform,ancient drainage system and passageways of underground river,the paper classifies the underground rivers,uses the data of logging,3D seismic and cores to describe the cave filling degree penetrated by a single well,and predicts the filling degree of the underground river in Lungu oilfield by combining with the correspondence relationship between the cave filling degree and through-well underground river.The dissection of the underground rivers in the study area shows that there are 6 factors which can control the filling degree of the under-ground river such as the level and type of underground river,cave type,the relationship with the underground river passageways,the relationship with hall?like caves and cave collapse.Using the 6 factors as the input parameters,a prediction model is established for the under-ground river filling degree.The prediction results show that the relative error is about 10%by using the BP neural network to predict the un?derground river filling degree.The method can be applied in the evaluation of caved carbonate reservoirs.

关 键 词：塔里木盆地 轮古油田 奥陶系 碳酸盐岩油藏 洞穴充填预测 BP神经网络 

分 类 号：TE112.23[石油与天然气工程—油气勘探]