A Novel Reservoir Modeling Method based on Improved Hierarchical XGBoost

Reservoir classification and evaluation are critical during the oil and gas exploration and production, and traditional production forecasting often require complex methods such as reservoir modeling, which is time-consuming and will delay the E&P process. With the rapid development of artificial intelligence technology, the production forecasting method based on machine learning has become a new hot spot for research. Since the characteristics of various reservoirs are different, it is difficult for conventional machine learning models to meet the required accuracy due to a large number of unknowns. In this paper, we proposed an improved hierarchical XGBoost (h-XGBoost) modeling method, which can effectively extract the features of different reservoirs and build a hierarchical data model. The study is conducted on tight conglomerates reservoir at the Mahu area in Xinjiang, and the results verify the effectiveness of the method.