Prediction of Porosity and Permeability from Well-Logs Using Genetic Algorithm-Optimised Neural Networks

Prediction of Porosity and Permeability Using Well Log and Core Data: A Data-Driven Approach

Accurate prediction of porosity and permeability is very important for reservoir characterization and hydrocarbon extraction. Traditional workflow in the form of empirical correlations is usually difficult, time-consuming, spatially limiting, and entirely dependent on formation geology. The current study examines a different approach, which uses machine learning (ML) regression models based on well-log and core data. Three regression architectures including Random Forest, CatBoost, and K-Nearest Neighbors (KNN) were trained and validated based on a dataset consisting of 340 samples of shaly sand gas reservoirs. The gamma ray (GR), resistivity (RLLD), spontaneous potential (SP), bulk density (RHOB), neutron porosity (NPHI), and depth were used as the input variables with the core-derived porosity (CPHI) and permeability (CKHG) being used as the targets. The quantitative measures of performance of the models included R2, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The findings indicated that KNN regression was better than its counterparts as it achieved R2 = 0.8933 and R2 = 0.9340 in terms of porosity and permeability prediction, respectively, and more acceptable metrics of errors showed. Comparatively, the traditional empirical methods showed a significantly lower accuracy rate. The findings highlight that machine learning has the potential to provide precise, scalable, and low-cost predictions of the reservoir properties which could lead to better choices for exploration and production activities.

Ensemble Machine-Learning-Based Horizontal Permeability Prediction and Hydraulic Flow Unit Characterization of the Hugin Sandstone

Understanding permeability is essential for evaluating reservoir quality and field development planning. Reliable permeability estimation can reduce the uncertainty in reservoir characterization, particularly in intervals where core data are limited. As the industry relies on log-based interpretations and empirical correlations, the limitations of these approaches become apparent. Data-driven approaches offer a promising alternative to conventional empirical methods. The data set in this study comprises 252 samples with seven features derived from conventional well logs. Data preprocessing includes handling missing values, smoothing logs, feature engineering to add an extra input, and transformation with the Yeo-Johnson technique. A center moving average filter was used to reduce variance and improve data consistency. Ensemble machine-learning (ML) and baseline models were developed and evaluated using a 75-25 train-test split, four-fold cross validation, and model complexity assessment. Ensemble methods outperformed baseline models, with extremely randomized trees (ET) and random forest (RF) emerging as the most stable, achieving a mean R² of 0.93 and 0.89 and a low R² standard deviation (0.3). Multilinear regression (MLR) and artificial neural networks (ANNs) show limited accuracy, while gradient boosting (GB) and extreme gradient boosting (XGBoost) methods exhibit overfitting despite perfect training scores. Predicted kh values were compared with core data. Both linear and nonlinear empirical equations were derived using MLR, polynomial regression, and a power-law model. The power-law model (empirical equation) achieved an R² value of 0.79 and can therefore be used to estimate permeability. Additionally, a Gaussian mixture model (GMM) was used for unsupervised classification of hydraulic flow units (HFU) using the flow zone indicator (FZI), computed from the continuous permeability curve obtained from the best ML model. Thus, ML-based permeability prediction is an indispensable component of HFU modeling. The model identified three distinct flow zones, enabling HFU clustering and defining their corresponding petrophysical properties and depositional environments.

Machine learning-driven modeling of soil plasticity and strength parameters with interpretability insights

This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index and Undrained Shear Strength and highlights the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.

Robust prediction of advanced reservoir petrophysical parameters using conventional logs: Insights from XGB and neural network models

A Permeability Prediction Method for Tight Sandstone Reservoirs Based on XGBoost Algorithm

Permeability is a crucial parameter that characterizes the fluid migration capacity of reservoirs and is essential for hydrocarbon migration during accumulation and subsequent exploration and development. For tight sandstone reservoirs, the diverse pore types, complex pore-throat structures, and strong heterogeneity make it difficult to establish a stable correlation between porosity and permeability, thereby hindering accurate permeability prediction with conventional methods. To address this issue, this paper proposes a permeability prediction method based on an XGBoost (eXtreme Gradient Boosting) algorithm optimized with Optuna (an automatic hyperparameter optimization framework). The model’s accuracy, inter-well generalization ability, and interpretability are comprehensively evaluated by combining well-group validation, blind well independent verification, and SHapley Additive exPlanations (SHAP) global-local interpretation. Six characteristic parameters—core porosity (CORE-POR), natural gamma ray (GR), acoustic transit time (AC), compensated density (DEN), compensated neutron log (CNL), and photoelectric absorption cross-section index (PE)—are selected as input variables to construct the permeability prediction model. The predictive performance of the model was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The SHAP interpretation method was used to conduct global and local interpretability analysis of the model. The results show that Optuna-XGBoost outperforms multiple regression models and traditional machine-learning models on both the validation set and blind wells, indicating that this method is well-suited to permeability prediction for tight sandstone reservoirs in this study area.

An Insight Into Rock Brittleness Prediction Based on Stacking Integrated Algorithm

The significance of rock brittleness is well‐recognized in the fields of geotechnical engineering and energy exploration. To enhance the predictive precision of rock brittleness, this paper proposes a Stacking integrated algorithm. This algorithm synergistically combines various meta models and foundational models, utilizing a suite of nine algorithm modes: Gaussian Process Regression, Support Vector Machine, Backpropagation Neural network, Extreme Learning Machine network, Decision Tree, Random Forest, Extreme Gradient Boosting, Lasso Regression, and Ridge Regression. Furthermore, Tuna and Bayesian optimization algorithms are utilized to refine the model's performance. Additionally, a new diversity index, k, based on the ratio of correlation coefficients, has been introduced to facilitate the optimal selection of base models for the Stacking integrated algorithm. The predictive accuracy of the Stacking integrated model, as determined by the proposed diversity index k, surpasses that of the finest individual base model and outperforms other sets of five integrated base models with an equivalent number of components. This underscores the efficacy of the diversity index k in guiding the selection of appropriate base models for the stacking process. The most effective model for predicting rock brittleness incorporates the Extreme Learning Machine network, Decision Tree, Random Forest and Extreme Gradient Boosting. This ensemble model demonstrates superior accuracy over the single best model, the Decision Tree, by reducing the average brittleness prediction error rate by 0.3745 and elevating the determination coefficient ( R 2 ) value from 0.9118 to 0.9629.When compared with the Particle Swarm Optimization model, this composite model achieves an increase of 0.1498 in R 2 .