| Popis: |
Sonic logs are essential for determining important reservoir properties such as porosity, permeability, lithology, and elastic properties, among others, and yet may be missing in some well logging suites due to high acquisition costs, borehole washout, tool damage, poor tool calibration, or faulty logging instruments. This study aims at predicting the compressional sonic log from commonly acquired logs (gamma ray, resistivity, density, and neutron-porosity) in the Tano basin of Ghana using Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) Machine Learning (ML) algorithms and comparing the performances of the algorithms. The algorithms were trained with 70% of the data from two wells and tested using the remaining 30% of the data from the wells after cross-validation. Subsequently, they were applied to the data from a third well to predict the sonic log in the well. The performances of the algorithms were assessed with five statistical tools: coefficient of determination (R2), adjusted R2, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). All three algorithms successfully predicted the compressional sonic log (DT). XGBoost demonstrated the highest prediction accuracy, with R2 of 0.9068 and the least errors. RF exhibited the next highest accuracy, with R2 being 0.85478, while SVM had R2 of 0.66591. Therefore, the ensemble algorithms (XGBoost and RF) proved to be more accurate than the non-ensemble algorithm (SVM) in this study. The outcome of the study will accelerate and enhance the understanding of oil and gas fields with few or no compressional sonic logs. To the best of the authors’ knowledge, this is the first study to have predicted the compressional sonic log from well data (logs) in a Ghanaian sedimentary basin using machine learning algorithms, and only a few such studies have been conducted in the whole West African sub-region. |
