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

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国家科技期刊平台
NETL
NSTL
万方数据

中文摘要：传统的横波速度预测方法包括经验公式法和岩石物理模型法.前者适用于岩石矿物组分相对单一的储层,且受区域限制等因素的影响,不具有普适性,预测精度较低.后者需要根据不同的实际情况选择合适的岩石物理模型,才能达到预期的目的.大多数机器学习横波速度预测方法基于纯数据驱动,数据集的质量和数量将直接决定横波预测模型精度,并缺乏充分的物理内涵.为此,基于深度神经网络(DNN)的方法,假设研究区储层波传播方程的数学形式已知,通过测井数据训练DNN得到未知的弹性参数,以确立目的层的波传播方程.利用平面波分析法得到相应的纵波、横波速度,实现神经网络与理论模型的结合.此外,针对传统Xu-White模型的不足,考虑随深度变化的孔隙纵横比,提出了改进横波速度预测岩石物理模型.利用研究区较丰富的测井数据,分别采用构建的DNN模型和改进横波速度预测岩石物理模型预测横波速度,并与传统的Xu-White模型预测结果进行对比、分析.结果表明,由DNN模型和改进岩石物理模型均可获得较高精度的横波速度预测结果,且前者的预测效果更好.

外文标题：Shear wave velocity prediction of shale oil formations based on machine learning and improved rock physics model

外文摘要：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.

外文关键词：

deep neural networkrock physics modelshale oil formationsreservoir parametersS-wave velocitypore aspect ratio

作者：

方志坚、巴晶、熊繁升、杨志芳、晏信飞、阮传同

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作者单位：

河海大学地球科学与工程学院,江苏南京 211100

北京雁栖湖应用数学研究院,北京 101408

中国石油天然气股份有限公司勘探开发研究院,北京 100083

中国石油天然气股份有限公司地球物理重点实验室,北京 100083

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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

基金：

国家自然科学基金&&中国石油天然气集团有限公司科技项目江苏省科技计划青年项目

项目编号：

42174161419741232023ZZ0504BK20220995

出版年：

2024

DOI：

10.13810/j.cnki.issn.1000-7210.2024.03.001

石油地球物理勘探

东方地球物理勘探有限责任公司

石油地球物理勘探

CSTPCD北大核心

影响因子：1.766

ISSN：1000-7210

年,卷(期)：2024.59(3)