The Construction of Knowledge Graph in Reservoir Geology and Its Application in Identifying Hydrocarbon Pay Zone

Abstract

AbstractThis paper presents an innovative method to construct a reservoir geology knowledge graph (KG) by combining theories in reservoir geology and oil & gas knowledge from experts, which incorporates the recent advances in KG and natural language processing (NLP) technologies. The reservoir geology ontology was built by extracting knowledge from professional books and hydrocarbon dictionaries in the oil & gas industry, which was used to guide the construction of a reservoir geology KG. The triplets of KG was extracted from the well logs, well descriptions, well formations, and well tests data of over 1,500 wells and contains more than 300,000 entities and 600,000 relations. By encoding the entities, relations and attributes in the knowledge graph into vectors, the authors built a knowledge-driven neural formation evaluation model for predicting different formation types. The model was applied to J pilot block in northwest C oilfield and discovered a new pay zone, which corrects the original interpretation of this formation done by experts.The exploration includes the following steps. Firstly the authors established a set of hydrocarbon geological knowledge classification criterion, then constructed a reservoir geological KG and building machine learning models for intelligence formation identification in the base of the aforementioned KG, together with relevant geological parameters, finally applied the models in two oilfield datasets.This work builds the first reservoir geology KG in the oil & gas domain. It is also the first attempt to combine knowledge-driven and data-driven approach for formation evaluation modeling. As the first intelligent model applied in practice, the formation evaluation model achieves significant outcomes in an oilfield, setting a successful precedent for other areas.KeywordsReservoir geologyKnowledge graphKnowledge-powered neural formation evaluation model