Artificial Intelligence in Geosciences最新文献

{"title":"Earthquake location and magnitude estimation using seismic arrival times pattern and gradient boosted decision trees","authors":"Saeed SoltaniMoghadam,&nbsp;Anooshiravan Ansari,&nbsp;Leila Etemadsaeed,&nbsp;Mohammad Tatar,&nbsp;Meysam Mahmoodabadi","doi":"10.1016/j.aiig.2025.100149","DOIUrl":"10.1016/j.aiig.2025.100149","url":null,"abstract":"<div><div>We present a machine learning approach for earthquake location and magnitude estimation based on seismic arrival time patterns, using Histogram-Based Gradient Boosting for its high accuracy and computational efficiency. The model is first evaluated using a synthetic earthquake bulletin that simulates realistic network geometry, station-event distributions, and incorporates a 3D velocity model for accurate travel-time computation. Input features include P and S arrival times and amplitudes, while targets consist of location, origin time, magnitude, and uncertainty measures (horizontal and depth errors, azimuthal gap). Model performance is evaluated using <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, Mean Absolute Error (MAE), and Median Absolute Error (MEDAE), demonstrating high accuracy across datasets with varying levels of completeness. Finally, we validate the model using real-world data from the Ahar-Varzaghan 2012 aftershock sequence in NW Iran. The model accurately recovers key spatial patterns of seismicity despite significant missing data, and the results align with previous high-resolution studies. These findings confirm that the proposed method generalizes well beyond synthetic settings and offers a fast, robust alternative for operational seismic networks and rapid hazard assessment.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100149"},"PeriodicalIF":4.2,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842609","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}

{"title":"Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence","authors":"Evet Naturinda ,&nbsp;Fortunate Kemigyisha ,&nbsp;Anthony Gidudu ,&nbsp;Isa Kabenge ,&nbsp;Emmanuel Omia ,&nbsp;Jackline Aboth","doi":"10.1016/j.aiig.2025.100147","DOIUrl":"10.1016/j.aiig.2025.100147","url":null,"abstract":"<div><div>Greenhouse gases (GHGs) from agriculture in Africa are among the world's fastest-growing emissions, with the livestock sector as the primary contributor. However, the methods for quantifying these emissions rely on manual and outdated data collection and processing approaches. Therefore, there is a need to develop more accurate and efficient methods of quantifying GHGs from livestock. This research developed a remote sensing and Artificial Intelligence (AI) based approach to quantify GHG emissions from cattle in the Kisombwa Ranching Scheme in Mubende District, central Uganda.</div><div>We trained a deep learning algorithm, You Only Look Once (YOLO) v4, to detect cattle from the Unmanned Aerial Vehicle (UAV) images of the study area and applied the Simple Online Real-time Tracker (SORT) algorithm for automated counting. Methane (CH₄₎ and Nitrous Oxide (N₂O) emissions from manure management and enteric fermentation were estimated using the number of cattle and the Tier 1 guidelines from the Intergovernmental Panel on Climate Change (IPCC). The total estimated emissions were 321,121.34 kg carbon dioxide equivalent (CO₂eq) per year, with CH₄ at 282,282.96 kg CO₂eq per year (88 %) and N₂O at 38,838.38 kg CO₂eq per year (12 %). Enteric fermentation contributed the highest emissions, about 99 % of the total CH₄ emissions and 87 % of the total GHGs.</div><div>The proposed remote sensing and AI-driven method achieved an average F1 score of 88.9 %, average precision of 97 %, and average recall of 82.9 % on the testing set of images. Therefore, these research findings demonstrate that remote sensing and AI are a more potent and efficient approach to upscale quantifying and reporting animal population and livestock GHG emissions for sustainable agriculture and climate change mitigation.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702139","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}

{"title":"Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts","authors":"Iver Martinsen ,&nbsp;Steffen Aagaard Sørensen ,&nbsp;Samuel Ortega ,&nbsp;Fred Godtliebsen ,&nbsp;Miguel Tejedor ,&nbsp;Eirik Myrvoll-Nilsen","doi":"10.1016/j.aiig.2025.100145","DOIUrl":"10.1016/j.aiig.2025.100145","url":null,"abstract":"<div><div>Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed. They are important indicators in many analyses, are used in climate change research, monitoring marine environments, evolutionary studies, and are also frequently used in the oil and gas industry. Although some research has focused on automating the classification of foraminifera images, few have addressed the uncertainty in these classifications. Although foraminifera classification is not a safety-critical task, estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived. Uncertainty estimation in deep learning has gained significant attention and many methods have been developed. However, evaluating the performance of these methods in practical settings remains a challenge. To create a benchmark for uncertainty estimation in the classification of foraminifera, we administered a multiple choice questionnaire containing classification tasks to four senior geologists. By analyzing their responses, we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains. These uncertainty estimates served as a baseline for comparison when training neural networks in classification. We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications. The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark, to see how the methods performed individually and how the methods aligned with humans. Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance. Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694746","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}

{"title":"Explaining machine learning models trained to predict Copernicus DEM errors in different land cover environments","authors":"Michael Meadows,&nbsp;Karin Reinke,&nbsp;Simon Jones","doi":"10.1016/j.aiig.2025.100141","DOIUrl":"10.1016/j.aiig.2025.100141","url":null,"abstract":"<div><div>Machine learning models are increasingly used to correct the vertical biases (mainly due to vegetation and buildings) in global Digital Elevation Models (DEMs), for downstream applications which need “bare earth” elevations. The predictive accuracy of these models has improved significantly as more flexible model architectures are developed and new explanatory datasets produced, leading to the recent release of three model-corrected DEMs (FABDEM, DiluviumDEM and FathomDEM). However, there has been relatively little focus so far on explaining or interrogating these models, especially important in this context given their downstream impact on many other applications (including natural hazard simulations). In this study we train five separate models (by land cover environment) to correct vertical biases in the Copernicus DEM and then explain them using SHapley Additive exPlanation (SHAP) values. Comparing the models, we find significant variation in terms of the specific input variables selected and their relative importance, suggesting that an ensemble of models (specialising by land cover) is likely preferable to a general model applied everywhere. Visualising the patterns learned by the models (using SHAP dependence plots) provides further insights, building confidence in some cases (where patterns are consistent with domain knowledge and past studies) and highlighting potentially problematic variables in others (such as proxy relationships which may not apply in new application sites). Our results have implications for future DEM error prediction studies, particularly in evaluating a very wide range of potential input variables (160 candidates) drawn from topographic, multispectral, Synthetic Aperture Radar, vegetation, climate and urbanisation datasets.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663596","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}

{"title":"Machine learning assisted estimation of total solids content of drilling fluids","authors":"B.T. Gunel ,&nbsp;Y.D. Pak ,&nbsp;A.Ö. Herekeli ,&nbsp;S. Gül ,&nbsp;B. Kulga ,&nbsp;E. Artun","doi":"10.1016/j.aiig.2025.100138","DOIUrl":"10.1016/j.aiig.2025.100138","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations. In particular, maintaining the optimal levels of solids content is essential for achieving the most effective fluid performance. Proper management of solids content also reduces the risk of tool failures. Traditional solids content analysis methods, such as retort analysis, require substantial human intervention and time, which can lead to inaccuracies, time-management issues, and increased operational risks. In contrast to human-intensive methods, machine learning may offer a viable alternative for solids content estimation due to its pattern-recognition capability. In this study, a large set of laboratory reports of drilling-fluid analyses from 130 oil wells around the world were compiled to construct a comprehensive data set. The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms. Several machine learning algorithms of diverse classes, namely linear (linear regression, ridge regression, and ElasticNet regression), kernel-based (support vector machine) and ensemble tree-based (gradient boosting, XGBoost, and random forests) algorithms, were trained and tuned to estimate solids content from other readily available drilling fluid properties. Input variables were kept consistent across all models for interpretation and comparison purposes. In the final stage, different evaluation metrics were employed to evaluate and compare the performance of different classes of machine learning models. Among all algorithms tested, random forests algorithm was found to be the best predictive model resulting in consistently high accuracy. Further optimization of the random forests model resulted in a mean absolute percentage error (MAPE) of 3.9% and 9.6% and R&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; of 0.99 and 0.93 for the training and testing sets, respectively. Analysis of residuals, their histograms and Q-Q normality plots showed Gaussian distributions with residuals that are scattered around a mean of zero within error ranges of &lt;span&gt;&lt;math&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;1% and &lt;span&gt;&lt;math&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;4%, for training and testing, respectively. The selected model was further validated by applying the rheological measurements from mud samples taken from an offshore well from the Gulf of Mexico. The model was able to estimate total solids content in those four mud samples with an average absolute error of 1.08% of total solids content. The model was then used to develop a web-based graphical-user-interface (GUI) application, which can be practically used at the rig site by engineers to optimize drilling fluid programs. The proposed model can complement automation workflows that are designed to measure fundamental rheological properties in real time during drilling operations. While a st","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581266","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}

{"title":"Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning","authors":"Oriyomi Raheem ,&nbsp;Misael M. Morales ,&nbsp;Wen Pan ,&nbsp;Carlos Torres-Verdín","doi":"10.1016/j.aiig.2025.100144","DOIUrl":"10.1016/j.aiig.2025.100144","url":null,"abstract":"<div><div>Capillary pressure plays a crucial role in determining the spatial distribution of oil and gas, particularly in medium-to-low permeability reservoirs, where it is closely linked to the rock's pore structure and wettability. In these environments, pore structure is the primary factor influencing capillary pressure, with different pore types affecting fluid transport through varying degrees of hydrocarbon saturation. One of the main challenges in characterizing pore structure is how to use data from core plugs to establish a relationship with microscopic pore and throat properties, enabling more accurate predictions of capillary pressure. While special core analysis laboratory experiments are effective, they are time-consuming and expensive. In contrast, nuclear magnetic resonance (NMR) measurements, which provide information on pore body size distribution, are faster and can be leveraged to estimate capillary pressure using machine learning algorithms. Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).</div><div>Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. Although porescale imaging and network modeling techniques can compute capillary pressure from micro-CT rock images (Øren and Bakke, 2003; Valvatne and Blunt, 2004), these approaches are time-consuming, limited to small sample volumes, and not yet practical for routine reservoir evaluation. In this study, we introduce rock classification techniques and implement a data-driven machine learning (ML) method to estimate saturation-dependent capillary pressure from core petrophysical properties. The new model integrates cumulative NMR data and densely resampled core measurements as training data, with prediction errors quantified throughout the process. To approach the common condition of sparsely sampled training data, we transformed the prediction problem into an overdetermined one by applying composite fitting to both capillary pressure and pore throat size distribution, and Gaussian cumulative distribution fitting to the NMR <span><math><mrow><msub><mi>T</mi><mn>2</mn></msub></mrow></math></span> measurements, generating evenly sampled data points. Using these preprocessed input features, we performed classification based on the natural logarithm of the permeability-to-porosity ratio <span><math><mrow><mo>(</mo><mrow><mi>ln</mi><mrow><mo>(</mo><mrow><mi>k</mi><mo>/</mo><mi>ϕ</mi></mrow><mo>)</mo></mrow></mrow><mo>)</mo></mrow></math></span> to cluster distinct rock types. For each rock class, we applied regression techniques—such as random forest (RF), k-nearest neighbors (k-NN), extreme gradient boosting (XGB), and artificial neural networks (ANN)—to estimate the logarithm of capillary pressure. The methods were tested on blind core samples, and performance comparisons among different estimation methods ","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633212","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}

{"title":"Deep learning approaches for estimating maximum wall deflection in excavations with inconsistent clay stratigraphy","authors":"Vinh V. Le ,&nbsp;HongGiang Nguyen ,&nbsp;Nguyen Huu Ngu","doi":"10.1016/j.aiig.2025.100140","DOIUrl":"10.1016/j.aiig.2025.100140","url":null,"abstract":"<div><div>This paper presents a deep learning architecture combined with exploratory data analysis to estimate maximum wall deflection in deep excavations. Six major geotechnical parameters were studied. Statistical methods, such as pair plots and Pearson correlation, highlighted excavation depth (correlation coefficient = 0.82) as the most significant factor. For method prediction, five deep learning models (CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM) were built. The CNN-BiLSTM model excelled in training performance (R² = 0.98, RMSE = 0.02), while BiLSTM reached superior testing results (R² = 0.85, RMSE = 0.06), suggesting greater generalization ability. Based on the feature importance analysis from model weights, excavation depth, stiffness ratio, and bracing spacing were ranked as the highest contributors. This point verified a lack of prediction bias on residual plots and high model agreement with measured values on Taylor diagrams (correlation coefficient 0.92). The effectiveness of integrated techniques was reliably assured for predicting wall deformation. This approach facilitates more accurate and efficient geotechnical design and provides engineers with improved tools for risk evaluation and decision-making in deep excavation projects.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581267","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}

{"title":"Comparison of processing speed of NRS-ANN hybrid and ANN models for oil production rate estimation of reservoir under waterflooding","authors":"Paul Theophily Nsulangi ,&nbsp;Werneld Egno Ngongi ,&nbsp;John Mbogo Kafuku ,&nbsp;Guan Zhen Liang","doi":"10.1016/j.aiig.2025.100139","DOIUrl":"10.1016/j.aiig.2025.100139","url":null,"abstract":"<div><div>This study compared the predictive performance and processing speed of an artificial neural network (ANN) and a hybrid of a numerical reservoir simulation (NRS) and artificial neural network (NRS-ANN) models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery. The historical input variables: reservoir pressure, reservoir pore volume containing hydrocarbons, reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models. To create the NRS-ANN hybrid models, 314 data sets extracted from the NRS model, which included reservoir pressure, reservoir pore volume containing hydrocarbons, reservoir pore volume containing water and reservoir water injection rate were used. The output of the models was the historical oil production rate (HOPR in m³ per day) recorded from the ZH86 reservoir block. Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions (2, 4 and 6), each at 1000 epochs. A comparative analysis indicated that, for all 25 models, the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance. ANN models achieved an average of R² and MAE of 0.8433 and 8.0964 m³/day values, respectively, while NRS-ANN hybrid models achieved an average of R² and MAE of 0.7828 and 8.2484 m³/day values, respectively. In addition, ANN models achieved a processing speed of 49 epochs/sec, 32 epochs/sec, and 24 epochs/sec after 2, 4, and 6 replicates, respectively. Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec, 23 epochs/sec and 20 epochs/sec. In addition, the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy. The ANN optimal model achieved a speed of 336.44 epochs/sec, compared to the NRS-ANN hybrid optimal model, which achieved a speed of 52.16 epochs/sec. The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m³/day and 5.3855 m³/day in the validation dataset compared with the hybrid ANS optimal model, which achieved 13.6821 m³/day and 9.2047 m³/day, respectively. The study also showed that the ANN optimal model consistently achieved higher R² values: 0.9472, 0.9284 and 0.9316 in the training, test and validation data sets. Whereas the NRS-ANN hybrid optimal yielded lower R² values of 0.8030, 0.8622 and 0.7776 for the training, testing and validation datasets. The study showed that ANN models are a more effective and reliable tool, as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653505","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}

{"title":"Interpretable machine learning models for evaluating strength of ternary geopolymers","authors":"Junfei Zhang ,&nbsp;Huisheng Cheng ,&nbsp;Ninghui Sun ,&nbsp;Zehui Huo ,&nbsp;Junlin Chen","doi":"10.1016/j.aiig.2025.100128","DOIUrl":"10.1016/j.aiig.2025.100128","url":null,"abstract":"<div><div>Ternary geopolymers incorporating multiple solid wastes such as steel slag (SS), fly ash (FA), and granulated blast furnace slag (GBFS) are considered environmentally friendly and exhibit enhanced performance. However, the mechanisms governing strength development and the design of optimal mixtures are not fully understood due to the complexity of their components. This study presents the development of four machine learning models—Artificial Neural Network (ANN), Support Vector Regression (SVR), Extremely Randomized Tree (ERT), and Gradient Boosting Regression (GBR)—for predicting the unconfined compressive strength (UCS) of ternary geopolymers. The models were trained using a dataset comprising 120 mixtures derived from laboratory tests. Shapley Additive Explanations analysis was employed to interpret the machine learning models and elucidate the influence of different components on the properties of ternary geopolymers. The results indicate that ANN exhibits the highest predictive accuracy for UCS (R = 0.949). Furthermore, the UCS of ternary geopolymers is most sensitive to the content of GBFS. This study provides valuable insights for optimizing the mix proportions in ternary blended geopolymer mixtures.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523230","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}

{"title":"On the application of machine learning algorithms in predicting the permeability of oil reservoirs","authors":"Andrey V. Soromotin ,&nbsp;Dmitriy A. Martyushev ,&nbsp;João Luiz Junho Pereira","doi":"10.1016/j.aiig.2025.100126","DOIUrl":"10.1016/j.aiig.2025.100126","url":null,"abstract":"<div><div>Permeability is one of the main oil reservoir characteristics. It affects potential oil production, well-completion technologies, the choice of enhanced oil recovery methods, and more. The methods used to determine and predict reservoir permeability have serious shortcomings. This article aims to refine and adapt machine learning techniques using historical data from hydrocarbon field development to evaluate and predict parameters such as the skin factor and permeability of the remote reservoir zone. The article analyzes data from 4045 wells tests in oil fields in the Perm Krai (Russia). An evaluation of the performance of different Machine Learning (ML) algorithms in the prediction of the well permeability is performed. Three different real datasets are used to train more than 20 machine learning regressors, whose hyperparameters are optimized using Bayesian Optimization (BO). The resulting models demonstrate significantly better predictive performance compared to traditional methods and the best ML model found is one that never was applied before to this problem. The permeability prediction model is characterized by a high R² adjusted value of 0.799. A promising approach is the integration of machine learning methods and the use of pressure recovery curves to estimate permeability in real-time. The work is unique for its approach to predicting pressure recovery curves during well operation without stopping wells, providing primary data for interpretation. These innovations are exclusive and can improve the accuracy of permeability forecasts. It also reduces well downtime associated with traditional well-testing procedures. The proposed methods pave the way for more efficient and cost-effective reservoir development, ultimately supporting better decision-making and resource optimization in oil production.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330611","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}