石油地球物理勘探2024，Vol.59Issue(3)：381-391,11.DOI:10.13810/j.cnki.issn.1000-7210.2024.03.001
利用机器学习与改进岩石物理模型预测页岩油层系横波速度
Shear wave velocity prediction of shale oil formations based on machine learning and improved rock physics model
摘要
Abstract
Conventional shear wave(S-wave)velocity prediction methods include empirical formulas and rock physics model methods.The former is suitable for reservoirs with relatively simple rock mineral compositions,and it is affected by areas and some other factors.Therefore,it is difficult to be widely applied for different for-mations and has low prediction accuracy.The latter requires selecting appropriate rock physics models based on different situations,so as to achieve the expected goals.Most machine learning methods for S-wave velocity prediction aredriven by pure data,and the quality and quantity of the dataset directly determine the accuracy of the S-wave velocity prediction model,which are in lack of sufficient physical insights.Therefore,based on the deep neural network(DNN)methods,this paper assumes that the mathematical form of wave propagation equa-tions for the reservoir in the study area is known,but the elastic parameters are unknown and are learned through a DNN training on the basis of well logging data,so as to establish the wave propagation equations of the target layer.The corresponding compressional wave(P-wave)and S-wave velocities are obtained with the plane wave analysis method to connect the neural networks and the theoretical model.In addition,to address the shortcomings of the conventional Xu-White model,an improved rock physicsmodel for S-wave velocity pre-diction is proposed by considering the pore aspect ratio varying with depth.By using the adequate well logging data in the study area,the established DNN model and the improved rock physics model for S-wave velocity prediction are used to predict the S-wave velocity,and the results are compared with the conventional Xu-White model.It shows that both the DNN model and the improved rock physics model can help obtain high-precision S-wave velocity prediction results,and the former has better prediction performances.关键词
深度神经网络/岩石物理模型/页岩油层系/储层参数/横波速度/孔隙纵横比Key words
deep neural network/rock physics model/shale oil formations/reservoir parameters/S-wave velocity/pore aspect ratio分类
天文与地球科学引用本文复制引用
方志坚,巴晶,熊繁升,杨志芳,晏信飞,阮传同..利用机器学习与改进岩石物理模型预测页岩油层系横波速度[J].石油地球物理勘探,2024,59(3):381-391,11.基金项目
本项研究受国家自然科学基金项目"页岩油储层多尺度岩石物理模型及参数预测方法研究"(42174161)和"基于微观孔隙结构特征构建致密砂岩衰减岩石物理模型"(41974123)、中国石油天然气集团有限公司科技项目"油气藏精细描述与剩余油分布地球物理预测方法"(2023ZZ0504)、江苏省科技计划青年基金项目"基于多尺度衰减岩石物理模型的页岩油储层孔裂隙特征和黏土含量定量预测研究"(BK20220995)联合资助. （42174161）
