Intelligent Recognition of "Geological-Engineering" Sweet Spots in Tight Sandstone Reservoirs -An application to a tight gas reservoir in Ordos Basin, China

Kui Chen ¹ Minghao Zhao ² Yansong Feng ^3* Xiaoyan Fu ^3* Yifei Wang ^3* Hui Guo ^1* Jingchen Ding ^1* Qi Chen ^2*

• ¹ Sinopec (China), Beijing, Beijing Municipality, China
• ² College of Geosciences, China University of Petroleum, Beijing, China
• ³ PetroChina Changqing Oilfield Company, Xi'an, Shaanxi Province, China

Tight sandstone reservoirs have become a focal area in the exploration and development of oil and gas in recent years. However, the complexity of their geological conditions and the significant heterogeneity of reservoir properties make the identification of sweet spots challenging. Traditional methods heavily rely on the experience and judgment of geologists and engineers, which introduces considerable subjectivity and uncertainty. The advent of artificial intelligence offers new avenues for identifying sweet spots in tight sandstone reservoirs. This study, based on an integrated geologicalengineering perspective and utilizing data analysis and multiple machine learning methods, innovatively proposes a regression prediction model that integrates the Triangular Topology Aggregate Optimization (TTAO) algorithm, Random Forest (RF), and Multi-Head Self-Attention Mechanism (MSA), aiming to enhance the accuracy of oil and gas sweet spot identification. The case study utilizes actual data from the He8 section in the Ordos Basin, China. The results indicate that sweet spots are influenced by a combination of geological, rock mechanical parameters, and hydraulic fracturing operation parameters. The dominant reservoir properties affecting post-fracture productivity include gas saturation, porosity, and permeability, while the principal rock mechanics factors are fracture toughness and the difference in horizontal stresses. The critical fracturing operation factors are total fluid volume, total sand volume, and pre-pad fluid. Based on the analysis of dominant factors affecting productivity, the proposed hybrid machine learning model achieved an accuracy of 86.7% in identifying sweet spots. A three-dimensional geological-engineering sweet spot model considering lithology, physical properties, and rock mechanics characteristics was established, offering targeted areas for future well placement.