Carbonate lithofacies identification using an improved light gradient boosting machine and conventional logs: a demonstration using pre-salt lacustrine reservoirs, Santos Basin

Due to limitations imposed by cored wells, lithological data are often incomplete, and correct identification of lithofacies is problematic. Identification is actually an issue of pattern recognition, and based on newly proved findings, LightGBM (light gradient boosting machine) is considered to be an excellent pattern recognizer and, therefore, well suited for recognizing lithofacies. To remove remaining disadvantageous features and to further enhance the prediction performance of LightGBM, CRBM (continuous restricted Boltzmann machine) and AFSA (artificial fish swarm algorithm) are adopted as assistants to provide, respectively, high-quality learning data and to create optimal hyper-parameter settings during data processing. Subsequently, a predictor characterized by new ensemble learning is proposed, named CRBM-AFSA-LightGBM. To establish comprehensive verification, several validations are designed based on logging data derived from pre-salt carbonate reservoirs of the Santos Basin. Validations demonstrate the effectiveness and significance of integrating CRBM and AFSA; a further two validations are aimed at revealing whether a change in the learning data has an impact on prediction. To highlight the validation effect, PNN (probabilistic neural network) and SVM (support vector machine) are introduced as contrasting predictors. The test results demonstrate three important points: (1) CRBM and AFSA are preferred to assist in the capability of LightGBM; (2) the LightGBM-cored predictor performs better when compared with PNN-cored and SVM-cored predictors, especially when dealing with larger-scale learning data; (3) better robustness of the new predictor because reliable identification still can be achieved even when learning samples are sparse. Because all the validation evidences are optimistic, CRBM-AFSA-LightGBM is verified as a highly efficient and robust prediction tool for lithofacies identification in carbonate reservoirs.