A Knowledge Base of Shale Gas Play and Its Application on EUR Prediction by Integrating Knowledge Graph and Automated Machine Learning Techniques

The objective of this study is to analyze dominant controlling factors of the EUR of shale gas wells and then to forecast the EUR precisely by employing knowledge graph and automated machine learning techniques. First, an ontology knowledge representation model and a set of classification system for shale gas production are constructed, which include 13 shale gas objects such as basin, shale gas play, shale gas field, shale gas reservoir, and shale gas well, and their 112 geological, engineering and production parameters, such as mineral brittleness, fracturing section length, sanding intensity, and first-year production, and so on. Subsequently, structured data from existing databases are transformed, and loaded into the knowledge base. Large amount of unstructured data from papers, presentations, professional books are extracted and loaded by using various natural language processing (NLP) tools. The final shale gas knowledge base contains 56 shale gas plays and more than 1,000 shale gas wells worldwide. Based on the shale gas knowledge base, the graph embedding algorithm is used to convert the graph into a vector in order to train the machine learning models. Various automated machine learning frameworks such as TPOT, H2O, Auto-Sklearn, and AutoGluon are implemented and the performances are compared. According to the model with best performance, the main controlling factors of the EUR of shale gas wells are high-quality bed thickness, fracturing section length, and fracturing fluid volume, etc., which are consistent with shale gas production practices. The MSE and MAE of the best model on the testing dataset are 0.06 and 0.19, respectively. The approach of knowledge base construction and application developed in this paper can be extended to the entire life cycle of E&P process, which can make full use of various documents, data and knowledge accumulated in the oil and gas industry to conduct decision support.