Machine learning application for prediction of sonic wave transit time - A case of Niger Delta basin

Abstract

Sonic wave transit times are the major logs for estimating pertinent geomechanical parameters including overburden stress, pore pressure, effective stress, and unconfined compressible strength among others. These logs contain much important information about subsurface formation. However, in most oil and gas exploration wells, these logs are not usually acquired in all the wells which can be attributed to the high cost involved in logging. The application of the data-driven ML model has rarely been used for compressional and shear sonic logs (DTP and DTS) prediction within the Niger Delta basin where many reports of geomechanical instabilities have been reported. In this study, seven machine learning algorithms namely linear regression (LR), K-nearest neighbour (KNN), support vector regressor (SVR), random forest regressor (RFR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and back propagation artificial neural network (BPANN) were employed for prediction of DTP and DTS from conventional wireline logs data and their results were compared. For DTP prediction, the best model achieved a coefficient of determination (R2) value of 90 % using CatBoost. Also, CatBoost produced the model for DTS prediction with an R2 value of 95 %. A comparison results of the seven ML algorithms showed that six models performed really well for DTP and DTS predictions with a value of R2 > 80 % and 90 % respectively. In contrast, the worst performance models are LR for DTP prediction and SVR for DTS prediction. The blind testing carried out on some selected wells affirmed the efficiency of the proposed technique producing R2 values ranging from 92 to 94 % for DTP prediction and 94–99 % for DTS prediction. This present study confirmed the ability of building efficient ML models for estimating and predicting DTP and DTS which can be used to estimate geomechanical parameters in wells where both are not available.