Predicting carbonate formation permeability using machine learning

Tran, Huy Dat; Kasha, Ahmed; Sakhaee‐Pour, A.; Hussein, Ibnelwaleed A.

doi:10.1016/j.petrol.2020.107581

Cited by 16 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Models can be trained on large datasets, including both physical experiments and simulated data and have also been used to identify key factors that control increased permeability after acidizing, such as mineralogy, porosity, and other parameters, and their interactions. These insights can help to better understand the mechanisms controlling permeability and to design more effective strategies for enhancing or mitigating permeability in subsurface reservoirs 25,27,43 . this study utilizes genetic programming and machine learning models such as artificial neural networks, XGBoost, and random forest.…”

Section: Computational Techniques Data Preparingmentioning

confidence: 99%

“…The challenges associated with limited sample size and indirect measurements in predicting carbonate formation permeability are overcome through the use of machine learning. The proposed correlations show promising results, with an average R square score surpassing 0.96 27 . Talebkeikhah et al found SVM and DT models to be most accurate compared to traditional methods.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability

Dargi,

Khamehchi,

Mahdavi Kalatehno

2023

Sci Rep

View full text Add to dashboard Cite

Formation damage poses a widespread challenge in the oil and gas industry, leading to diminished permeability, flow rates, and overall well productivity. Acidizing is a commonly employed technique aimed at mitigating damage and enhancing permeability. In this study, to predict the permeability after acidizing in oil and gas reservoirs, three machine learning models, namely artificial neural networks, random forest, and XGBoost, along with genetic programming were used to estimate permeability changes after acidizing. These models are utilized to estimate permeability changes following acidizing operations. Training of the models involved a dataset comprising 218 acidizing operations conducted in diverse reservoirs across Iran. The input parameters, namely permeability, porosity, skin factor, calcite mineral fraction, acid injection rate, and injected acid volume, were optimized through the use of a genetic algorithm. Statistical and graphical analysis of the results demonstrates that genetic programming outperformed the other machine learning techniques, yielding superior performance with R square and RMSE values of 0.82 and 17.65, respectively. Nevertheless, the other models also exhibited commendable performance, surpassing an R square value of 0.73. The post-acidizing permeability data obtained from core flooding experiments conducted on carbonate and sandstone cores was utilized to validate the models. The genetic programming model demonstrates an average error of 21.1%. The evaluation of post-acidizing permeability using genetic programming, in comparison with the results obtained from the core-flood test, revealed errors of 22.95% and 32.4% for carbonate and sandstone cores, respectively. Furthermore, a comparison between the calculated post-acidizing permeability derived from the GP model and previous studies indicated errors within the range of 8.6–26.59%. The findings highlight the potential of genetic programming and machine learning algorithms in accurately predicting post-acidizing permeability, thereby aiding in acidizing design, effectiveness assessment, and ultimately enhancing oil and gas production rates.

show abstract

Section: Computational Techniques Data Preparingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability

Dargi,

Khamehchi,

Mahdavi Kalatehno

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The results show that this AE-CNN approach outperforms traditional CNN and lattice Boltzmann method (LBM) approaches, with an average R 2 value of 0.896 and low mean-square errors, showing substantial improvements in prediction accuracy from low-resolution porous media images. Tran et al 54 used both ANN and multiple regression to investigate the indirect correlation between pore throat radius, permeability, and porosity of carbonate samples. Compared to multiple regression, ANN exhibited superior performance with a higher correlation factor in predicting permeability.…”

Section: Introductionmentioning

confidence: 99%

Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India

Yalamanchi,

Datta Gupta

2024

Sci Rep

View full text Add to dashboard Cite

Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flow characteristics within these formations. The goal of the study was to use machine learning techniques for pore structure analysis and estimation of permeability in carbonate reservoirs. We implemented these algorithms by examining 2D scanning electron microscope (SEM) images of carbonate samples from the Jaisalmer sub-basin captured at various magnifications. In the initial stage of the analysis, various binarization algorithms were applied to determine carbonate sample porosity. Among these algorithms, the MaxEntropy algorithm gave a porosity value closely aligned with those obtained through petrography analysis. We employed the watershed algorithm to find the pore network parameters of carbonate samples at various magnifications. We observed that changes in magnification affected pore network parameters, resulting in a reduction in pore size distribution, throat radius, and grain size. Subsequently, we employed the numerical lattice Boltzmann method (LBM) to estimate the permeability of carbonate samples and compared to values derived from well logs. We employed machine learning (ML) algorithms, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM), to predict the permeability of carbonate samples. The input features for these models were the pore network parameters, while the LBM permeability values served as the output. We examine the prediction performance of these methods against the measured LBM permeability by conducting the error analysis and the coefficient of determination ($${R}^{2}$$ R 2 ) calculation. Our findings revealed that the ANN models outperformed the SVM models. Specifically, the ANN model displayed an impressive R2 value of 0.892, along with root mean square error (RMSE), mean squared error (MSE) and, mean absolute error (MAE) values of 1.927, 3.716 and 1.580, respectively. In contrast, the SVM model yielded an R2 value of 0.849, with RMSE, MSE and, MAE values of 2.324, 5.401 and, 2.166 respectively, when assessed on testing data of measured permeability. This study found that ANN is more dependable, robust, and precise than SVM in forecasting carbonate sample permeability.

show abstract

Porosity prediction using bagging ensemble machine learning in CCUS reservoirs. A case study: Darling Basin, Australia

Sandunil,

Bennour,

Sivakumar

et al. 2024

Earth Sci Inform

View full text Add to dashboard Cite

Predicting carbonate formation permeability using machine learning

Cited by 16 publications

References 42 publications

Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability

Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability

Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India

Porosity prediction using bagging ensemble machine learning in CCUS reservoirs. A case study: Darling Basin, Australia

Contact Info

Product

Resources

About