An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

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外文摘要：Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the re-sults for each pore pressure prediction.The CGP-NN model has the best generalization when the physics-related metric λ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

外文关键词：

Pore pressure predictionSeismic data1D convolution pyramid poolingAdaptive physics-informed loss functionHigh generalization capability

作者：

Xin Zhang、Yun-Hu Lu、Yan Jin、Mian Chen、Bo Zhou

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作者单位：

State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum(Beijing),Beijing 102249,China

College of Petroleum Engineering,China University of Petroleum(Beijing),Beijing 102249,China

State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum(Beijing),Beijing,102249,China

College of Petroleum Engineering,China University of Petroleum(Beijing),Beijing,102249,China

College of Artificial Intelligence,China University of Petroleum(Beijing),Beijing,102249,China

PetroChina Tarim Oilfield Company,Korla,Xinjiang 841000,China

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基金：

国家自然科学基金一般项目国家自然科学基金一般项目国家重点研发计划

项目编号：

52074314U19B6003-052019YFA0708303-05

出版年：

2024

DOI：

10.1016/j.petsci.2023.11.006

石油科学(英文版)

中国石油大学（北京）

石油科学(英文版)

影响因子：0.88

ISSN：1672-5107

年,卷(期)：2024.21(2)