Artificial Intelligence in Geosciences最新文献_第4页

Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals 利用TransUNet增强微地震事件检测：一种深度学习方法，用于同时拾取p波和s波首次到达

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100129

Kun Chen , Meng Li , Xiaolian Li , Guangzhi Cui , Jia Tian , JiaLe Li , RuoYao Mu , JunJie Zhu

{"title":"Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals","authors":"Kun Chen , Meng Li , Xiaolian Li , Guangzhi Cui , Jia Tian , JiaLe Li , RuoYao Mu , JunJie Zhu","doi":"10.1016/j.aiig.2025.100129","DOIUrl":"10.1016/j.aiig.2025.100129","url":null,"abstract":"<div><div>Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance when dealing with complex and noisy data. In this study, we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network. Our model integrates the advantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously. We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam. The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data. The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the TransUNet achieves the optimal balance in its architecture and inference speed. With relatively low inference time and network complexity, it operates effectively in high-precision microseismic phase pickings. This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reservoir monitoring applications.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models 基于计算机视觉技术和一对一模型的碳酸盐岩薄片自动分类

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100117

Elisangela L. Faria , Rayan Barbosa , Juliana M. Coelho , Thais F. Matos , Bernardo C.C. Santos , J.L. Gonzalez , Clécio R. Bom , Márcio P. de Albuquerque , P.J. Russano , Marcelo P. de Albuquerque

{"title":"Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models","authors":"Elisangela L. Faria , Rayan Barbosa , Juliana M. Coelho , Thais F. Matos , Bernardo C.C. Santos , J.L. Gonzalez , Clécio R. Bom , Márcio P. de Albuquerque , P.J. Russano , Marcelo P. de Albuquerque","doi":"10.1016/j.aiig.2025.100117","DOIUrl":"10.1016/j.aiig.2025.100117","url":null,"abstract":"<div><div>Convolutional neural networks have been widely used for analyzing image data in industry, especially in the oil and gas area. Brazil has an extensive hydrocarbon reserve on its coast and has also benefited from these neural network models. Image data from petrographic thin section can be essential to provide information about reservoir quality, highlighting important features such as carbonate lithology. However, the automatic identification of lithology in reservoir rocks is still a significant challenge, mainly due to the heterogeneity that is part of the lithologies of the Brazilian pre-salt. Within this context, this work presents an approach using one-class or specialist models to identify four classes of lithology present in reservoir rocks in the Brazilian pre-salt. The proposed methodology had the challenge of dealing with a small number of images for training the neural networks, in addition to the complexity involved in the analyzed data. An auto-machine learning tool called AutoKeras was used to define the hyperparameters of the implemented models. The results found were satisfactory and presented an accuracy greater than 70% for image samples belonging to other wells not seen during the model building, which increases the applicability of the implemented model. Finally, a comparison was made between the proposed methodology and multiple-class models, demonstrating the superiority of one-class models.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale 利用环境数据和卫星图像进行作物产量预测的一种新的综合神经符号方法

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100125

Khadija Meghraoui , Teeradaj Racharak , Kenza Ait El Kadi , Saloua Bensiali , Imane Sebari

{"title":"A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale","authors":"Khadija Meghraoui , Teeradaj Racharak , Kenza Ait El Kadi , Saloua Bensiali , Imane Sebari","doi":"10.1016/j.aiig.2025.100125","DOIUrl":"10.1016/j.aiig.2025.100125","url":null,"abstract":"<div><div>Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping 变异函数建模优化使用遗传算法和机器学习线性回归：应用顺序高斯模拟映射

Artificial Intelligence in Geosciences Pub Date : 2025-05-26 DOI: 10.1016/j.aiig.2025.100124

André William Boroh , Alpha Baster Kenfack Fokem , Martin Luther Mfenjou , Firmin Dimitry Hamat , Fritz Mbounja Besseme

{"title":"Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping","authors":"André William Boroh , Alpha Baster Kenfack Fokem , Martin Luther Mfenjou , Firmin Dimitry Hamat , Fritz Mbounja Besseme","doi":"10.1016/j.aiig.2025.100124","DOIUrl":"10.1016/j.aiig.2025.100124","url":null,"abstract":"<div><div>The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy and efficiency of geostatistical analysis, particularly in mineral exploration. The study combines GA and machine learning to optimise variogram parameters, including range, sill, and nugget, by minimising the root mean square error (RMSE) and maximising the coefficient of determination (R<sup>2</sup>). The experimental variograms were computed and modelled using theoretical models, followed by optimisation via evolutionary algorithms. The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon, covering 141 data points. Sequential Gaussian Simulations (SGS) were employed for predictive mapping to validate simulated results against true values. Key findings show variograms with ranges between 24.71 km and 49.77 km, optimised RMSE and R<sup>2</sup> values of 11.21 mGal<sup>2</sup> and 0.969, respectively, after 42 generations of GA optimisation. Predictive mapping using SGS demonstrated that simulated values closely matched true values, with the simulated mean at 21.75 mGal compared to the true mean of 25.16 mGal, and variances of 465.70 mGal<sup>2</sup> and 555.28 mGal<sup>2</sup>, respectively. The results confirmed spatial variability and anisotropies in the N170-N210 directions, consistent with prior studies. This work presents a novel integration of GA and machine learning for variogram modelling, offering an automated, efficient approach to parameter estimation. The methodology significantly enhances predictive geostatistical models, contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soil liquefaction assessment using machine learning 利用机器学习进行土壤液化评估

Artificial Intelligence in Geosciences Pub Date : 2025-05-20 DOI: 10.1016/j.aiig.2025.100122

Gamze Maden Muftuoglu , Kaveh Dehghanian

{"title":"Soil liquefaction assessment using machine learning","authors":"Gamze Maden Muftuoglu , Kaveh Dehghanian","doi":"10.1016/j.aiig.2025.100122","DOIUrl":"10.1016/j.aiig.2025.100122","url":null,"abstract":"<div><div>Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature importance methods to Random Forest (RF), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms. Feature importance methods consist of permutation and Shapley Additive exPlanations (SHAP) importances along with the used model's built-in feature importance method if it exists. These suggested approaches incorporate an extensive dataset of geotechnical parameters, historical liquefaction events, and soil properties. The feature set comprises 18 parameters that are gathered from 161 field cases. Algorithms are used to determine the optimum performance feature set. Compared to other approaches, the study assesses how well these algorithms predict soil liquefaction potential. Early findings show that the algorithms perform well, demonstrating their capacity to identify non-linear connections and improve prediction accuracy. Among the feature set, <em>σ</em><sup><em>,</em></sup><sub><em>v</em></sub> (<em>psf</em>), MSF, <em>CSR</em><sub><em>σ,</em></sub> <sub><em>v</em></sub>, FC%, V<sub>s∗,40f</sub> <sub>t</sub>(f ps) and <em>N</em><sub>1<em>,</em>60<em>,CS</em></sub> are the ones that have the highest deterministic power on the result. The study's contribution is that, in the absence of extensive data for liquefaction assessment, the proposed method estimates the liquefaction potential using five parameters with promising accuracy.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters 基于钻井和岩石物理参数，利用增强机器学习来预测钻进速度（ROP）

Artificial Intelligence in Geosciences Pub Date : 2025-05-12 DOI: 10.1016/j.aiig.2025.100121

Raed H. Allawi , Watheq J. Al-Mudhafar , Mohammed A. Abbas , David A. Wood

{"title":"Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters","authors":"Raed H. Allawi , Watheq J. Al-Mudhafar , Mohammed A. Abbas , David A. Wood","doi":"10.1016/j.aiig.2025.100121","DOIUrl":"10.1016/j.aiig.2025.100121","url":null,"abstract":"<div><div>Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF), gradient boosting modeling (GBM), extreme gradient boosting (XGBoost), and adaptive boosting (Adaboost) models to generate ROP predictions. The models use well data from a 3200-m segment across the stratigraphic column (Dibdibba to Zubair formations) of the large West Qurna oil field in Southern Iraq, penetrating 19 formations and four oil reservoirs. The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies. The ROP predictive models were developed using 14 operational parameters: TVD, weight on bit (WOB), torque, effective circulating density (ECD), drilling rotation per minute (RPM), flow rate, standpipe pressure (SPP), bit size, total RPM, D exponent, gamma ray (GR), density, neutron, caliper, and discrete lithology distribution. Training and validation of the ROP models involves data compiled from three development wells. Applying Random subsampling, the compiled dataset was split into 85 % for training and 15 % for validation and testing. The test subgroup's measured and predicted ROP mismatch was assessed using root mean square error (RMSE) and coefficient of correlation (R<sup>2</sup>). The RF, GBM, and XGBoost models provide ROP predictions versus depth with low errors. Models with cross-validation that integrate data from three wells deliver more accurate ROP predictions than datasets from single well. The input variables' influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LatentPINNs: Generative physics-informed neural networks via a latent representation learning latentpinn：基于潜在表征学习的生成物理信息神经网络

Artificial Intelligence in Geosciences Pub Date : 2025-05-09 DOI: 10.1016/j.aiig.2025.100115

Mohammad H. Taufik, Tariq Alkhalifah

{"title":"LatentPINNs: Generative physics-informed neural networks via a latent representation learning","authors":"Mohammad H. Taufik, Tariq Alkhalifah","doi":"10.1016/j.aiig.2025.100115","DOIUrl":"10.1016/j.aiig.2025.100115","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs are hampered by the relatively slow convergence and the need to perform additional, potentially expensive training for new PDE parameters. To solve this limitation, we introduce LatentPINN, a framework that utilizes latent representations of the PDE parameters as additional (to the coordinates) inputs into PINNs and allows for training over the distribution of these parameters. Motivated by the recent progress on generative models, we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions. We use a two-stage training scheme in which, in the first stage, we learn the latent representations for the distribution of PDE parameters. In the second stage, we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters. Considering their importance in capturing evolving interfaces and fronts in various fields, we test the approach on a class of level set equations given, for example, by the nonlinear Eikonal equation. We share results corresponding to three Eikonal parameters (velocity models) sets. The proposed method performs well on new phase velocity models without the need for any additional training.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield 基于机器学习和生产数据的层间识别和连通性分析：以M油田为例

Artificial Intelligence in Geosciences Pub Date : 2025-05-09 DOI: 10.1016/j.aiig.2025.100119

Xiaoshuai Wu , Yuanliang Zhao , Jianpeng Zhao , Shichen Shuai , Bing Yu , Junqing Rong , Hui Chen

{"title":"Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield","authors":"Xiaoshuai Wu , Yuanliang Zhao , Jianpeng Zhao , Shichen Shuai , Bing Yu , Junqing Rong , Hui Chen","doi":"10.1016/j.aiig.2025.100119","DOIUrl":"10.1016/j.aiig.2025.100119","url":null,"abstract":"<div><div>Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development. However, the traditional method of identifying interlayers has some limitations: (1) Due to the existence of overlaps in the cross plot for different categories of interlayers, it is difficult to establish a determined model to classify the type of interlayer; (2) Traditional identification methods only use two or three logging curves to identify the types of interlayers, making it difficult to fully utilize the information of the logging curves, the recognition accuracy will be greatly reduced; (3) For a large number of complex logging data, interlayer identification is time-consuming and labor-intensive. Based on the existing well area data such as logging data and core data, this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CⅢ sandstone group in the M oilfield. Through the comparison of various classifiers, it is found that the decision tree method has the best applicability and the highest accuracy in the study area. Based on single well identification of interlayers, the continuity of well interval interlayers in the study area is analyzed according to the horizontal well. Finally, the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital core reconstruction of tight carbonate rocks based on SliceGAN 基于SliceGAN的致密碳酸盐岩数字岩心重建

Artificial Intelligence in Geosciences Pub Date : 2025-04-22 DOI: 10.1016/j.aiig.2025.100116

Ying Zhou , Taiping Zhao , Wenjing Zhang , Feiqi Teng , Xin Nie

{"title":"Digital core reconstruction of tight carbonate rocks based on SliceGAN","authors":"Ying Zhou , Taiping Zhao , Wenjing Zhang , Feiqi Teng , Xin Nie","doi":"10.1016/j.aiig.2025.100116","DOIUrl":"10.1016/j.aiig.2025.100116","url":null,"abstract":"<div><div>The pore structures of the Majiagou Formation in the Ordos Basin are complex, featuring micro- and nano-scale intra-crystalline and inter-crystalline pores that significantly impact hydrocarbon storage and flow. Precisely characterizing the rock internal structures is crucial for reservoir exploration and development. However, it is difficult to accurately characterize the pore structure of rock using traditional imaging methods to meet the simulation requirements. In this context, this study focuses on high-resolution 3D digital core reconstruction using the SliceGAN model. Specifically, the Modular Automated Processing System (MAPS) image and Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) image were combined to divide MAPS into three categories: pore, dolomite, and calcite. Then, through the SliceGAN algorithm, the 3D digital core was reconstructed. To evaluate the reconstruction, the auto-correlation function, two-point probability function, porosity, mineral content, and specific surface area were employed. The results show that the SliceGAN can effectively capture the micro-features in the core, and the internal structure of the generated core was consistent with that of the original core. This study provided a new sight for reconstructing cores with complex pore structures and strong heterogeneity and innovatively supports tight carbonate reservoir characterization and evaluation.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An intelligent recognition method of deep shale gas reservoir laminaset based on laminaset clustering and R-L-M algorithm 基于层状集聚类和R-L-M算法的深层页岩气藏层状集智能识别方法

Artificial Intelligence in Geosciences Pub Date : 2025-04-07 DOI: 10.1016/j.aiig.2025.100113

Yu Zeng , Fuqiang Lai , Haijie Zhang , Yi Jiang , Junwei Pu , Tongtong Luo , Xiaoxia Zhao

{"title":"An intelligent recognition method of deep shale gas reservoir laminaset based on laminaset clustering and R-L-M algorithm","authors":"Yu Zeng , Fuqiang Lai , Haijie Zhang , Yi Jiang , Junwei Pu , Tongtong Luo , Xiaoxia Zhao","doi":"10.1016/j.aiig.2025.100113","DOIUrl":"10.1016/j.aiig.2025.100113","url":null,"abstract":"<div><div>Lamina structures, as typical sedimentary features in shale formations, determine both the quality of shale reservoirs and fracturing effects. In this study, through electric imaging logging, based on core scanning photos, thin sections, and other data from the Wufeng-Longmaxi Formation shale reservoirs in the western Sichuan Block, the characteristics and classification scheme of deep shale gas reservoir laminaset were clarified. In addition, with core scale electrical images, the electrical imaging logging response characteristics of different types of laminaset were identified. Based on electrical imaging logging images, a laminaset clustering algorithm was designed to segment the laminaset and then Levenberg-Marquardt (L-M) algorithm was improved by introducing a random forest to obtain the R-L-M algorithm, which was used to extract key parameters of laminaset such as attitude, type, density, and thickness. The average accuracy, recall rate, and F1 score of laminaset recognition results of this algorithm were 14.82 % higher than those of a well-known international commercial software (T). This method was used to evaluate the Longmaxi Formation shale gas reservoir in the western Sichuan Block. The development density of clay-siliceous (organic-lean) laminaset from the Longyi 1–4 small layer to the lower Wufeng Formation firstly decreased and then increased and the minimum value was found in Longyi 1-1 small layer. In contrast, the development density of siliceous-clay laminaset (organic-rich) first increased and then gradually decreased and the maximum value was found in Longyi 1-1 small layer. The clay-siliceous laminaset (organic matters-contained) and the calcareous-clay laminaset (organic matters-contained) showed a stable developmental trend.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0