Data-driven lithology prediction for tight sandstone reservoirs based on new ensemble learning of conventional logs: A demonstration of a Yanchang member, Ordos Basin.

Lithologies are significant indicators to get deep insight of depositional and mineralogical properties of target formations, and the classic approach of achieving them is crossplot. Nonetheless, crossplot presents ineffectively when addressing classification of tight sandstone reservoirs, since most primary lithological components are characterized by similar logging responses. LightGBM (light gradient boosting machine) has been proved powerful to produce a remarkable classification, while its performance is seriously limited by the setting of hyper-parameters. LD-AFSA (linear decreasing-artificial fish swarm algorithm), an excellent solver for multi-objective optimization, then is introduced to modify the setting in a best circumstance. Besides, another integration for LightGBM is CRBM (continuous restricted Boltzmann machine), which specializes in generating less variables to speed up calculation. Consequently, a data-driven lithology predictor based on new ensemble learning is proposed, named CRBM-LD-AFSA-LightGBM. Data for validation of new predictor is cored by wells of Chang 4 + 5 member, Jiyuan Oilfield, Ordos Basin, and accordingly four experiments are designed to make a comprehensive evaluation. To highlight validating effect, SVM (support vector machine) and XGBoost (extreme gradient boosting) are adopted as competitors. Through comparison of experimental results, including prediction accuracy, F1-score, and AUC (area under curve), it is figured out that XGBoost-cored and LightGBM-cored predictors have capabilities to produce similar while more reliable results, meanwhile also exhibiting better generalization on prediction, but the computing time of latter predictor is only 1/25 shorter than that of the former. The results well demonstrate the proposed predictor plays a real high-efficient role in predicting lithologies and is deserved to receive a widespread employment in the field of logging interpretation because of its greater applicability. • Prediction capability of LightGBM can be enhanced by integration of CRBM and LD-AFSA. • CRBM-LD-AFSA-LightGBM is a real high-efficient predictor for lithologies. • Training more learning samples is effective to improve prediction effect. • A sparse learning data set can be processed by CRBM-LD-AFSA-LightGBM. [ABSTRACT FROM AUTHOR]