Lithology identification with wireline logs based on stacked- driven ensemble method for complex carbonate reservoirs

Carbonate reservoirs are influenced by structure, deposition and diagenesis, making its lithology complex and diverse. Thus, carrying out the research of lithology identification is significantly essential for reservoir evaluation. With the development of intelligent methods and ensemble strategies, the stacked-driven ensemble method (SDEM) was proposed. Firstly, the lithologies were determined through the logging while drill (LWD) data and core observations. And six sensitive wireline logs were selected as the input of the SDEM, namely GR (natural gamma ray logging), DEN (density logging), AC (compensated acoustic logging), RLLD (deep lateral resistivity logging), PE (photoelectric absorption cross-section logging) and CNL (neutron logging). Then, a two-level SDEM was constructed. For the first level, base models, including BPNN (back-propagation neural network), SVM (support vector machine) and DT (decision tree), were used, while the DT was employed as the meta-model in the second level. In addition, the grid search method combined with 10-fold cross-validation was adopted to search for the optimal hyperparameters of SDEM. The results showed that the average classification accuracy of 10-fold cross-validation reached 95.1%, which was higher (approximately 2.6%) than any individual method. Finally, two cases in different regions in the Sulige gas field of Ordos Basin were discussed and the results showed that the proposed SDEM outperforms all other individual approaches or traditional ensemble learning methods (ELMs) with higher accuracy and superior performance. Subsequently, the developed approach is applicable to the predictive work in other oil and gas exploration fields, which can improve exploration precision and raise hydrocarbon production.