Construction of well logging knowledge graph and intelligent identification method of hydrocarbon-bearing formation

• Published in: Petroleum exploration and development Vol. 49; no. 3; pp. 572 - 585
• Main Authors: LIU, Guoqiang, GONG, Renbin, SHI, Yujiang, WANG, Zhenzhen, MI, Lan, YUAN, Chao, ZHONG, Jibin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2022; KeAi Communications Co., Ltd
• Subjects: graph embedding technique; hydrocarbon bearing formation identification; intelligent identification; knowledge graph; neural network; well logging; well logging; hydrocarbon bearing formation identification; knowledge graph; neural network; graph embedding technique; intelligent identification
• ISSN: 1876-3804; 1876-3804
• DOI: 10.1016/S1876-3804(22)60047-8

Based on the well logging knowledge graph of hydrocarbon-bearing formation (HBF), a Knowledge-Powered Neural Network Formation Evaluation model (KPNFE) has been proposed. It has the following functions: (1) extracting characteristic parameters describing HBF in multiple dimensions and multiple scales; (2) showing the characteristic parameter-related entities, relationships, and attributes as vectors via graph embedding technique; (3) intelligently identifying HBF; (4) seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation. Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin, NW China as objects, 80% of the wells were randomly selected as the training dataset and the remainder as the validation dataset. The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43% with the expert interpretation results and a coincidence rate of 84.38% for all the oil testing layers, which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation. In addition, a number of potential pays likely to produce industrial oil were recommended. The KPNFE model effectively inherits, carries forward and improves the expert knowledge, nicely solving the robustness problem in HBF identification. The KPNFE, with good interpretability and high accuracy of computation results, is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.