Ahmed Elghoul

Reliable prediction of interfacial tension (IFT) is critical for optimizing engineering processes like hydrogen separation, high-density energy storage and fluid displacement in underground oil reservoir systems. The goals of this study are: (a) to assess the efficacy of optimized ensemble machine learning (EML) models and (b) parametric sensitivity analysis to interpret feature importance while predicting interfacial tension (IFT) across an extensive range of thermodynamic conditions. The dataset comprises 498 experimentally measured IFT records collected under diverse conditions of temperature (T), pressure (P), and gas compositions of hydrogen (H₂), carbon dioxide (CO₂), and methane (CH₄) in complex reservoir brine systems. The EML algorithms of extreme gradient boosting (XGB), random forest (RF), decision tree (DT), light gradient boosting (LGB) and adaptive boosting (ADB) are applied to construct the data-driven computational models to obtain IFT. The model’s effectiveness and robustness are evaluated using statistical performance indices. SHapley Additive exPlanations (SHAP) analysis is adopted to interpret and rank feature importance, thereby ensuring the model’s transparency and interpretability. The XGB model showed excellent performance, with high determination of coefficient (R², 83%) and least root mean square error (RMSE, 4.81 mN/m) compared to other models of RF (81, 5.06), DT (79.94, 5.23), LGB (77.52, 5.54) and ADB (69.32, 6.46) accordingly. Based on SHAP scores, it is revealed that T is the most significant parameter to control IFT, and we ranked them higher to lower weighty order, like T, H₂, P, CO₂ and CH₄. This study demonstrates the efficacy of EML and physics-guided computational models, coupled with feature selection techniques, in capturing IFT phenomena relevant to a subsurface hydrogen–brine geological system. It also establishes an interpretable artificial intelligent framework to uphold the project design, risk evaluation, and optimization of underground hydrogen storage systems in saline aquifers and other geologically complex formations for energy transition.

Machine learning-guided predictive models are attractive in rock modeling for different scholars to obtain continuous profiles of rock compressive strength in rock engineering. The major objectives of the study are to assess the implications of machine learning (ML)-based connectionist models to obtain the unconfined compressive strength (UCS) of rock, to perform parametric sensitivity analysis on petrophysical parameters, and to develop an improved correlation for UCS prediction. The least-squares support vector machine (LSSVM) is applied to develop data-driven models for the prediction of UCS. Additionally, the random forest (RF) algorithm is applied to verify the effectiveness of predictive models. A database containing well-logging data is processed and utilized to construct connectionist models to obtain UCS. For the efficacy of predictive models, statistical performance indicators such as the coefficient of determination (CC), average percentage relative error, and maximum average percentage error are utilized in the study. It is revealed that the RF- and LSSVM-based models for predicting UCS perform excellently with high precision. Considering the parametric sensitivity analysis in the predictive models for UCS, the formation compressional wave velocity and formation gamma-ray are the most strongly contributing predictor variables rather than other input variables such as the modulus of elasticity, acoustic shear wave velocity, and rock bulk density. The improved correlation for predicting UCS shows high precision, achieving a CC of 96% and root mean squared error of 0.54 MPa. This systematic research workflow is significant and can be utilized for connectionist robust model development and variable selections in the petroleum and mining fields, such as predicting reservoir properties, the drilling rate of penetration, sanding potentiality of hydrocarbon reservoir rocks, and for the practical implications of boring and geotechnical engineering projects.