Journal of Geophysics and Engineering
人工智能（AI）：岩石物理 专题合集
本合集收录 Journal of Geophysics and Engineering 在 人工智能（AI）：岩石物理 方向发表的 3 篇文章，附完整引用格式、英文摘要与全文网址，复制网址到浏览器打开即可阅读全文。
目录 · 共 3 篇
01Research on the conductivity mechanism and saturation evaluation method of altered igneous rock reservoirs based on numerical simulation02Identification of diagenetic facies based on data difference enhancement (DDE) machine learning03Fusion of finite element and machine learning methods to predict rock shear strength parameters
01Research on the conductivity mechanism and saturation evaluation method of altered igneous rock reservoirs based on numerical simulation
Authors：Xinru Wang, Qinghui Wang, Baozhi Pan, Yuhang Guo, Lihua Zhang, Yan Li, Ruhan A, Xiuwen Mo
Citation：Xinru Wang, Qinghui Wang, Baozhi Pan, Yuhang Guo, Lihua Zhang, Yan Li, Ruhan A, Xiuwen Mo, Research on the conductivity mechanism and saturation evaluation method of altered igneous rock reservoirs based on numerical simulation, Journal of Geophysics and Engineering, Volume 23, Issue 5, October 2026, Pages 1748–1767, https://doi.org/10.1093/jge/gxag079
Abstract（英文原文，上下滑动查看）
Altered igneous reservoirs are difficult to evaluate using conventional electrical models because of their complex mineral composition and mixed conduction mechanisms. To improve the understanding of electrical features in these reservoirs, this study combines laboratory experiments with digital rock simulation to analyze the conductivity mechanism of altered igneous rocks and to establish applicable conductivity and saturation models. X-ray diffraction, resistivity measurements, and CT scanning were used to characterize the mineral composition and pore structure of the samples and to reconstruct 3D digital cores. On this basis, digital rock models with different clay contents, wettability conditions, and water saturations were generated. Considering the alteration characteristics of igneous rocks, mathematical morphology and the lattice Boltzmann method were used to simulate pore-scale fluid occurrence, and the model reliability was verified by experimental results and fractal exponent analysis. Finite element simulations and laboratory data were then integrated to investigate the effects of clay content, wettability, and salinity on electrical properties. The results show that clay content increases the nonlinearity of the I–Sw relationship, whereas salinity weakens it. Clay minerals also affect the saturation exponent mainly by altering wettability. Based on these results, new conductivity and saturation models were developed, and a Bayesian-optimized bidirectional long short-term memory network was introduced to predict clay-related parameters. Compared with conventional shaly sandstone models, the proposed method gives better predictions for altered igneous rocks. The study provides a practical basis for logging evaluation and petrophysical interpretation of complex altered igneous reservoirs.https://doi.org/10.1093/jge/gxag079
02Identification of diagenetic facies based on data difference enhancement (DDE) machine learning
Authors：Peng Zhu, Qin Xu, Tong Ma, Caihua Xu, Ying Feng, Tairan Ye, Youyi Bi
Citation：Peng Zhu, Qin Xu, Tong Ma, Caihua Xu, Ying Feng, Tairan Ye, Youyi Bi, Identification of diagenetic facies based on data difference enhancement (DDE) machine learning, Journal of Geophysics and Engineering, Volume 22, Issue 4, August 2025, Pages 1074–1084, https://doi.org/10.1093/jge/gxaf066
Abstract（英文原文，上下滑动查看）
Accurate identification of diagenetic facies is crucial for reservoir characterization, as it directly determines the evaluation of petrophysical properties, pore structure types, and reservoir quality, thereby playing a pivotal role in predicting high-quality hydrocarbon-bearing zones. However, conventional identification approaches are often limited by subjective interpretation, lack of standardized criteria, and low operational efficiency. Meanwhile, existing intelligent classification techniques frequently fail to adequately discriminate subtle but critical data variations, leading to suboptimal classification accuracy. To address these challenges, this paper develops a novel method for diagenetic facies identification based on data difference enhancement (DDE). The proposed methodology consists of three key steps: first, high-dimensional well-logging data are projected into a lower-dimensional feature space using the t-SNE algorithm to improve computational efficiency while preserving nonlinear relationships. Subsequently, the k-means clustering algorithm partitions the processed dataset into distinct groups, thereby amplifying intra-cluster data homogeneity and inter-cluster separability. Next, an ensemble learning architecture is constructed using the stacking algorithm, where cluster-specific meta-classifiers are individually optimized to enhance model robustness. During application, unclassified samples are assigned to their nearest cluster based on Euclidean distance metrics, followed by targeted prediction using the corresponding meta-classifier. The data from the second member of the Upper Triassic Xujiahe Formation are employed for model evaluation, and the results show that the DDE machine learning method significantly outperforms conventional machine learning algorithms, including k-nearest neighbours, support vector machines, and random forests, achieving an accuracy rate of 86.4%. This workflow enables efficient and reliable diagenetic facies classification using standard well-logging curves, offering both theoretical insights and practical tools for reservoir quality prediction in hydrocarbon exploration.https://doi.org/10.1093/jge/gxaf066
03Fusion of finite element and machine learning methods to predict rock shear strength parameters
Authors：Defu Zhu, Biaobiao Yu, Deyu Wang, Yujiang Zhang
Citation：Defu Zhu, Biaobiao Yu, Deyu Wang, Yujiang Zhang, Fusion of finite element and machine learning methods to predict rock shear strength parameters, Journal of Geophysics and Engineering, Volume 21, Issue 4, August 2024, Pages 1183–1193, https://doi.org/10.1093/jge/gxae064
Abstract（英文原文，上下滑动查看）
The trial-and-error method for calibrating rock mechanics parameters has the disadvantages of complexity, being time-consuming, and difficulty in ensuring accuracy. Harnessing the repeatability and scalability intrinsic to numerical simulation calculations and amalgamating them with the data-driven attributes of machine learning methods, this study uses the finite element analysis software RS2 to establish 252 sets of sandstone sample data. The recursive feature elimination and cross-validation method is employed for feature selection. The shear strength parameters of sandstone are predicted using machine learning models optimized by the particle swarm optimization (PSO) algorithm, including the backpropagation neural network, Bayesian ridge regression, support vector regression (SVR), and light gradient boosting machine. The predicted value of cohesion is proposed as the input feature to predict the friction angle. The results indicate that the optimal input characteristics for predicting cohesion are elastic modulus, Poisson's ratio, peak stress, and peak strain, while the optimal input characteristics for predicting friction angle are peak stress and cohesion. The PSO-SVR model demonstrates the best performance. The maximum error between the predicted values of cohesion and friction angle and the calculated results of RSData program are 3.5% and 4.31%, respectively. The finite element calculation is in good agreement with the stress–strain curve obtained in the laboratory. The sensitivity analysis indicates that SVR's prediction performance for cohesion and friction angle tends to be stable when the sample size is ＞25. These results offer a valuable reference for accurately predicting rock mechanics parameters.https://doi.org/10.1093/jge/gxae064
Journal of Geophysics and Engineering (JGE)是由中石化石油物探技术研究院有限公司主办，英国帝国理工大学（Imperial College London）协办，牛津大学出版社(Oxford University Press)合作出版的国际英文SCI收录期刊。
JGE主要登载地球物理与油气藏工程方面的高质量文章，内容涉及固体地球物理、油气地球物理、矿产勘探地球物理、煤矿矿山地球物理、水文工程环境地球物理、岩石物理、测井技术、钻井技术、考古、遥感、CCUS、地热、氢能源、GPR、仪器和传感器设计及相关的地球物理等。
JCR 2025影响因子：2.0
JCR 分区：Q3
收录：SCIE, Scopus, DOAJ
Editors-in-Chief：
Yanghua Wang Principal of Resource Geophysics Academy, Imperial College London, UK
Xuhui Xu Director of SINOPEC Geophysical Research Institute Co., Ltd., Nanjing, China
官方网址：https://academic.oup.com/jge
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