Evaluating reservoir permeability from core data: Leveraging boosting techniques and ANN for heterogeneous reservoirs

Characterizing reservoir rock is aided by an understanding of how the permeability changes dynamically within formations. There are currently only nine research articles that focus on permeability prediction using a small set of input parameters that are easily, affordably, and frequently derived from laboratory core analysis. The majority of machine learning models applied to permeability determination are connected to well logs. This work investigates and implements four novel approaches for permeability prediction from standard core analysis data. These approaches include hybrid stacking and three boosting techniques: AdaBoost, gradient boosting, and extreme gradient boosting (XGB). While boosting enhances any regressor or classifier by being computationally efficient in large-scale datasets, stacking increases prediction accuracy by mixing the output from several base models. The dataset comprises measures of porosity ( ), grain density ( ), water saturation ( ), oil saturation ( ), depth, and absolute permeability ( ) for 197 core plugs from the sedimentary basin of Jeanne d'Arc. According to the results, boosting strategies with a root mean squared error (RMSE) of less than 32.24 and a coefficient of determination (R 2 ) of more than 0.95 are good enough and meet the study's objectives. With an RMSE of 23.45 to 30.16 and an R 2 of 0.92 to 0.95, hybrid stacking—which combines AdaBoost, gradient boosting, XGB, and artificial neural networks (ANN)— offers a bit less accuracy than boosting models. Gradient Boosting is shown to provide the maximum precision, with an RMSE of 18.23 and an R 2 of 0.98. The ANN has also high prediction accuracy, with an R2 of 0.97 and an RMSE of 26.41. The boosting strategies in permeability prediction from routine core data are quite accurate, as shown by the comparison of the suggested methodology with 20 earlier utilized models identified in 9 literatures. XGB, Gradient Boosting, AdaBoost, and Stacking models are explored in this study, marking the first instance of their application in predicting permeability from routine core analysis. Additionally, previously utilized algorithms, such as ANN, have also been re-evaluated to predict permeability. All proposed algorithms are systematically ranked based on performance criteria. The models developed in this research, leveraging a few key inputs, offer engineers and scientists a reliable and efficient means of determining reservoir permeability with high accuracy. This significantly reduces the reliance on resource-intensive and time-consuming laboratory analyses. • Adaptive Boosting, Gradient Boosting and Hybrid Stacking are formulated for the first time in core data-based permeability modeling. • Tree-based boosting approaches prove successful in predicting permeability from core data. • Hybrid stacking exhibits a relatively less accurate measurement compared to boosting models. • A thorough comparison has been conducted between the current study and prior research. • The proposed methods provide higher accuracy relative to all other models observed in the literature.