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Study on Rock Fracture Mechanism Using Well logging data and Minimum Energy Consumption Principle: A Case Study of Mesozoic Clastic Rocks in Chengdao-Zhuanghai Area, Jiyang Depression

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PROCESSES 2025年第5期13卷 1614-1614页

作者： Ma, Shilong Yang, Shaochun Wu, Yanjia Huang, Dongmou Zhang, Yifan China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China China Univ Petr East China State Key Lab Deep Oil & Gas Qingdao 266580 Peoples R China Shengli Oilfield Sinopec Offshore Prod Plant Dongying 257237 Peoples R China

In the Chengdao-Zhuanghai area, there are few core samples of Mesozoic clastic rocks but abundant logging data. It is difficult to establish a fracture model of clastic rocks directly based on core samples and relevant tests. In this study, triaxial compression tests are conducted on Mesozoic clastic rock samples to reveal the failure mechanism of clastic rocks. A statistical model based on logging data is utilized to calculate dynamic rock mechanical parameters, and theoretical relationships between static and dynamic mechanical parameters are derived. A failure model for clastic rocks is established using logging data and the minimum energy consumption principle by applying the principle of minimum energy consumption and adopting the unified energy yield criterion of rocks as the energy consumption constraint. This research study shows that a linear relationship exists between the static and dynamic mechanical parameters of Mesozoic clastic rocks, and the correlation coefficient can reach 85%. The core aspect of clastic rock failure is energy dissipation. As confining pressure increases, more energy must be dissipated during the failure of clastic rocks. Upon failure, the releasable elastic energy accumulated within the clastic rocks clearly reflects the confining pressure effect. A higher initial confining pressure leads to a greater release of elastic energy and results in a more severe failure degree. The developed rock failure model effectively represents the nonlinear mechanical behavior of Mesozoic clastic rocks in the Chengdao-Zhuanghai area under complex stress conditions. It is suitable for investigating the fracture distribution of Mesozoic clastic rocks and addresses the challenge of understanding the failure mechanism of these rocks in the Chengdao-Zhuanghai region.

关键词： Chengdao-Zhuanghai area Mesozoic clastic rock logging data principle of minimum energy consumption rock fracture mechanism

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Estimation of tensile and uniaxial compressive strength of carbonate rocks from well-logging data: artificial intelligence approach

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第1期14卷 317-329页

作者： Ibrahim, Ahmed Farid Hiba, Moaz Elkatatny, Salaheldin Ali, Abdulwahab King Fahd Univ Petr & Minerals Petr Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Integrat Petr Res Dhahran 31261 Saudi Arabia

The uniaxial compressive strength (UCS) and tensile strength (T0) are crucial parameters in field development and excavation projects. Traditional lab-based methods for directly measuring these properties face practical challenges. Therefore, non-destructive techniques like machine learning have gained traction as innovative tools for predicting these parameters. This study leverages machine learning methods, specifically random forest (RF) and decision tree (DT), to forecast UCS and T0 using real well-logging data sourced from a Middle East reservoir. The dataset comprises 2600 data points for model development and over 600 points for validation. Sensitivity analysis identified gamma-ray, compressional time (DTC), and bulk density (ROHB) as key factors influencing the prediction. Model accuracy was assessed using the correlation coefficient (R) and the absolute average percentage error (AAPE) against actual parameter profiles. For UCS prediction, both RF and DT achieved R values of 0.97, with AAPE values at 0.65% for RF and 0.78% for DT. In T0 prediction, RF yielded R values of 0.99, outperforming DT's 0.93, while AAPE stood at 0.28% for RF and 1.4% for DT. These outcomes underscore the effectiveness of both models in predicting strength parameters from well-logging data, with RF demonstrating superior performance. These models offer the industry an economical and rapid tool for accurately and reliably estimating strength parameters from well-logging data.

关键词： Failure parameters logging data Machine learning Uniaxial compressive strength Tensile strength

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A novel approach for reconstructing paleo-overpressure through basin simulations constrained by logging data a case study of Tight gas in Sulige Gas field, Ordos Basin

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CARBONATES AND EVAPORITES 2025年第1期40卷 1-15页

作者： Zhang, Tao Zhang, Jingong Liu, Xiaopeng Cao, Qian Liang, Bing Ma, Tengfei Northwest Univ Dept Geol State Key Lab Continental Dynam Xian 710069 Peoples R China PetroChina Changqing Oilfield Co Res Inst Explorat & Dev Xian 710018 Peoples R China China Natl Petr Corp Bur Geophys Prospecting Inc Baoding 072750 Peoples R China PipeChina Grp Beijing 100020 Peoples R China

The application of the basin simulation method in reconstructing the pressure evolution process is frequently constrained by the limited availability of measured data. In order to establish a valid relation between logging data and paleo-pressure, this paper proposes a novel approach to constrain the pressure reconstructed by basin simulation method. The Eaton index N corresponding to the paleo-pressure of each period is calculated using inversion method, based on measured paleo-pressure by fluid inclusion PVTx method and logging data, in accordance with the Eaton formula. Subsequently, this value is utilized to calculate the paleo-pressure of other wells. The paleo-pressure results are compared with those reconstructed by basin simulation method to validate the reliability of the findings. The findings demonstrate a strong concurrence between the paleo-pressure calculated using the mean value of index N for each period and the reconstructed paleo-pressure obtained through basin simulation methodology, with an average error margin of less than +/- 5%. Therefore, utilizing log data to constrain the paleo-pressure reconstructed by basin simulation method is a viable approach due to its ease of accessibility, strong continuity, and broad applicability and potential for application. This is particularly advantageous in regions where acquiring paleo-pressure data poses challenges. The paleo-dynamic conditions exert a significant influence on the distribution of gas and water in tight sandstone gas reservoirs, providing valuable insights for identifying favorable areas.

关键词： Basin simulation method Paleo-pressure reconstruction logging data Eaton index N Tight gas Permian System Ordos Basin

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Identification of Coal Structure Based on Geophysical logging data in Zhengzhuang Block, Southern Qinshui Basin, China: Investigation by Convolutional Neural Networks

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NATURAL RESOURCES RESEARCH 2025年 1-17页

作者： Wan, Tingrui Ren, Pengfei Chen, Chuanbang Ping, Hanghang Chengdu Univ Technol Coll Energy Coll Modern Shale Gas Ind Chengdu 610059 Peoples R China Chengdu Univ Technol State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610059 Peoples R China

Coalbed methane (CBM) is a significant, widely distributed, and clean energy source, playing a crucial role in addressing the increasing global energy demand. Coal structure, a key parameter in the development of CBM, influences the main length and complexity of coal seams during the hydraulic fracturing process. Identifying coal structures through geophysical logging has become a hot topic due to the high cost and time-consuming nature of coal cores. This paper takes the No. 3 coal from the Zhengzhuang Block in the Qinshui Basin as a case study and proposes a coal structure identification method using acoustic time difference (AC), caliper log (CAL), natural gamma (GR), density (DEN), and deep lateral resistivity (LLD) as input features in a convolutional neural network (CNN) model. The study demonstrated that AC, CAL, and LLD increased with coal damage, while GR and DEN decreased. The trained CNN model achieved an accuracy of 97.8% on the training dataset and 92.3% on the testing dataset. Further validation through blind well tests on wells 64 and 65 yielded accuracies of 94.5% and 92.3%, respectively, demonstrating the CNN model's strong generalization capability. The planar and sectional features of coal seams in the study area were analyzed: the undeformed coal was thicker in the southwest part of the study area, the thickness of cataclastic and granulated coal decreased gradually along the southwest direction, and the coal seams showed inhomogeneity in the vertical direction. This study provides an efficient and accurate approach for coal structure classification, offering a valuable tool for CBM exploration and development.

关键词： Coalbed methane Coal structure logging data Convolutional neural network (CNN) Qinshui Basin

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logging data Completion Based on an MC-GAN-BiLSTM Model

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IEEE ACCESS 2022年 10卷 1810-1822页

作者： Liang Guo Luo Renze Li Xingyu Tuo Juanjuan Lei, Canru Zhou Yang Southwest Petr Univ Sch Earth Sci & Technol State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610500 Peoples R China Southwest Petr Univ Sch Earth Sci & Technol Chengdu 610500 Peoples R China

Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose an MC-GAN-BiLSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the logging data depth domain. We complete the logging data missing values through an encoding-decoding structure that includes the Bi-LSTM. In addition, the generator module also uses multiscale convolution to fully extract the logging data features. We use logging data random missing values and consecutive missing values to simulate a field data acquisition environment and threshold control to simulate a laboratory processing environment for experiments. The experimental results show that the coefficient of determination (R-2) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), sequence to sequence (seq2seq) and generative adversarial interpolation network (GAIN) models.

关键词： data models Neural networks Logic gates Generative adversarial networks Support vector machines Recurrent neural networks Predictive models logging data missing value GAN LSTM GAIN GAN-LSTM

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Lithology identification technology of logging data based on deep learning model

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EARTH SCIENCE INFORMATICS 2023年第3期16卷 2545页

作者： Zhang, XiaLin Wen, JinJun Sun, Qing Wang, ZhenJiang Zhang, LuYi Liang, Peng China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Minist Nat Resources Engn Technol Innovat Ctr Mineral Resources Explora Guiyang 550081 Guizhou Peoples R China

Traditional machine learning models have mainly been used to study geological logging data of a single sample point, ignoring the fact that logging data has a strong spatial correlation. In this study, we use convolutional neural network to extract single-point features, structural features, and multidimensional features from logging data and compare the identification effects of lithology identification models based on the three features. The identification model based on the multidimensional feature extraction achieves 77.94% correctness in the test set, which is the best result among the identification models based on CNN and the three machine learning models. Based on this feature extraction model, the feature fusion modules in U-net and feature pyramid are added respectively to build two feature fusion models to combine the features extracted from different convolutional layers and improve the effectiveness of the model. The model also introduces attention mechanism to improve the role of useful features in the model training process. The identification accuracy of the two feature fusion models, U-CNN and P-CNN, reached 79.67% and 80.02% on the test set, respectively, which verified the effectiveness of the feature fusion models for lithology identification in the study area.

关键词： Lithology identification Convolutional neural network Feature fusion logging data

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Novel Physics-Informed Machine Learning Approach to Estimate Safe Mud Window Using Petrophysical logging data

Novel Physics-Informed Machine Learning Approach to Estimate...

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2024 International Petroleum Technology Conference, IPTC 2024

作者： Gowida, A. Elkatatny, S. College of Petroleum Engineering & Geosciences King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (纸本)9781959025184

The allowable limits of mud weights for drilling O&G wells, known as the safe mud window (SMW), play a crucial role in preventing wellbore instability issues and loss of circulation. The SMW consists of the minimum mud weight for shear failure (MWBO) and the maximum mud weight for tensile failure (MWBD), which are determined by the principal stresses of the formation, including the maximum (Shmax) and minimum (Shmin) horizontal stresses. Measuring these stresses accurately can be achieved through field tests or approximated using physics-based equations. However, obtaining the necessary in-situ geomechanical parameters for these equations, such as static Poisson's ratio and static elastic modulus, is not always feasible for all wells. Furthermore, the existing machine learning models rely on expensive and destructive tests. To address these challenges, this study investigated the feasibility of utilizing machine learning (ML) algorithms to predict these parameters in a time- and cost-effective manner. New ML-based models employing artificial neural networks (ANN) were developed to predict the SMW limits (MWBO and MWBD) using petrophysical well-log data as inputs. A comprehensive dataset consisting of field test data and petrophysical logging data was collected and extensively analyzed to train the models. The predictions generated by the developed ANN-based models exhibited a high degree of accuracy, with a mean absolute average error (MAPE) of less than 0.30% when compared to the actual output values. Thes developed models were validated using an unseen dataset, demonstrating remarkable agreement with the actual stress gradient and SMW limit values. The prediction accuracy exceeded 95%, and the MAPE was as low as 0.59%. The statistical analysis of the results confirmed the robustness of the developed equations in accurately predicting the SMW limits, provided that the logging data are available. The originality of this research lies in its ability to efficiently an

关键词： artificial neural network logging data machine learning safe mud window

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Quantitative logging data clustering with hidden Markov model to assist log unit classification

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EARTH PLANETS AND SPACE 2022年第1期74卷 1-21页

作者： Yabe, Suguru Hamada, Yohei Fukuchi, Rina Nomura, Shunichi Shigematsu, Norio Kiguchi, Tsutomu Ueki, Kenta Geol Survey Japan Natl Inst Adv Ind Sci & Technol Tsukuba Cent 71-1-1 Higashi Tsukuba Ibaraki 3058567 Japan Japan Agcy Marine Earth Sci & Technol Kochi Inst Core Sample Res Inst Extra Cutting Edge Sci & Technol Avant Garde 200 Monobe Otsu Nankoku Kochi 7838502 Japan Naruto Univ Educ 748 NakajimaNaruto Cho Naruto 7728502 Japan Waseda Univ Grad Sch Accountancy Shinjuku Ku 1-6-1 Nishiwaseda Tokyo 1698050 Japan Japan Agcy Marine Earth Sci & Technol Res Inst Marine Geodynam 2-15 Natsushima Cho Yokosuka Kanagawa 2370061 Japan

Revealing subsurface structures is a fundamental task in geophysical and geological studies. logging data are usually acquired through drilling projects, which constrain the subsurface structure, and together with the description of drill core samples, are used to distinguish geological units. Clustering is useful for interpreting logging data and making log unit classification and is usually performed by manual inspection of the data. However, the validity of clustering results with such subjective criteria may be questionable. This study proposed the application of a statistical clustering method, the hidden Markov model, to conduct unsupervised clustering of logging data. As logging data are aligned along the drilled hole, they and the geological structure hidden behind such sequential datasets can be regarded as observables and hidden states in the hidden Markov model. When log unit classification is manually conducted, depth dependency of logging data is usually focused. Therefore, we included depth information as observables to explicitly represent depth dependency of logging data. The model was applied to the following geological settings: the accretionary prism at the Nankai Trough, the onshore fault zone at the Kii Peninsula (southwest Japan), and the forearc basin at the Japan Trench. The optimum number of clusters were searched using a quantitative index. The clustering results using the hidden Markov model were consistent with previously reported classifications or lithological descriptions;however, our method allowed a more detailed division of logging data, which is useful to interpret geological structures, such as a fault or a fault zone. Therefore, the use of the hidden Markov model enabled us to clarify assumptions quantitatively and conduct clustering consistently for the entire depth range, even for different geological sites. The proposed method is expected to have wider applicability and extensibility for other types of data, including geochemi

关键词： Hidden Markov model logging data Clustering Unit classification

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Lithology identification of logging data based on improved neighborhood rough set and AdaBoost

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EARTH SCIENCE INFORMATICS 2022年第2期15卷 1201-1213页

作者： Zhang, Xialin Sun, Qing He, Kunyang Wang, Zhenjiang Wang, Jin China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China State Key Lab Biogeol & Environm Geol Wuhan 430074 Hubei Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Minist Nat Resources Engn Technol Innovat Ctr Mineral Resources Explor Guiyang 550081 Guizhou Peoples R China

Traditional lithology identification left the problems of low accuracy, recognition efficiency and generalization ability. Facing the logging data with outliers, unbalance and high complexity, we propose a lithology identification method based on an improved neighborhood rough set and AdaBoost. On the basis of the classical neighborhood rough set, the selection of the neighborhood radius and the running time are optimized. The redundant information in logging data is then effectively eliminated. Thus more sensitive logging curves are selected without changing the physical meaning of logging attributes. Then the selected data are input into the AdaBoost model to construct a lithology identification model. About 54,000 samples from 5 boreholes are tested in the study area. The accuracy of classification on the test set is about 98.42%. Compared with BP neural network and random forest algorithm, the proposed method owns advantages in recognition accuracy and generalization ability. It can provide help for complex lithology recognition in the study area.

关键词： Neighborhood rough set AdaBoost logging data Lithology identification

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Prediction of cohesion and friction angle from well-logging data using decision tree and random forest

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ARABIAN JOURNAL OF GEOSCIENCES 2022年第1期15卷 1-11页

作者： Hiba, Moaz Ibrahim, Ahmed Farid Elkatatny, Salaheldin Ali, Abdulwahab King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Dept Petr Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Integrat Petr Res Dhahran 31261 Saudi Arabia

Rock mechanical parameters are essential for drilling operation optimization, reservoir health monitoring, and hydrocarbon production. Consequently, achieving high economic revenue from the reservoir is dependent upon an accurate prediction of these parameters. Cohesion and internal friction angle are two important parameters that represent the shear strength of the rock. Several works have been done to predict these two parameters empirically from lab measurement or wireline logging data. In this study, two machine learning (ML) techniques, decision tree (DT) and random forest (RF), were used to build a predictive model using actual well logging data of two wells. The input data include compressional time (DTC), bulk density (ROHB), and neutron porosity (NPHI), as the sensitivity analysis showed that friction angle and cohesion are more sensitive to this input than the other logging parameters. The first well data set was used to train and test the model, while another set of 635 data points from different well was used for the model validation. The model accuracy was measured using the correlation coefficient (R), and the absolute average percentage error (AAPE), in addition to the failure parameter profiles. The result indicated the high accuracy of the two models in the prediction as the DT model recorded an R-value greater than 0.98 and 0.96 and an AAPE less than 2% and 3% for friction angle and cohesion, respectively, while the RF model showed its outperformance with R values 0.99 and 0.97 and AAPE values less than 1% and 2.5% for the two parameters respectively. The validation set confirmed the accuracy of the two models in generating a complete accurate profile of the friction angle and cohesion, as the DT model's R values were calculated at 0.96 and 0.95 and AAPE of 1.6% and 3.3% for friction angle and cohesion, respectively. On the other hand, the RF model showed its better accuracy with R values of 0.97 and 0.96, while the AAPE values were 1.2% and 2.5% f

关键词： Failure parameters logging data Decision tree Random forest Friction angle Cohesion

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