Construction of Fracturing Knowledge Graph and Fracturing Plan Optimization

As an efficient and intelligent means of knowledge organization, knowledge graph has become the core force driving the development of artificial intelligence. Hydraulic fracturing is an important measure for increasing production and injection in oil and gas fields, with complex design processes and numerous influencing factors. In order to achieve rapid and accurate optimization of fracturing plan, this paper proposes a method for optimizing fracturing plan based on knowledge graph. By combing the system of fracturing domain knowledge, fracturing knowledge graph is constructed. Extracting characteristic parameters describing the geological engineering double sweet spot in multiple dimensions and multiple scales, and showing the characteristic parameter-related entities, relationships, and attributes as vectors via graph embedding technique. Integrate expert knowledge with artificial intelligence to build a fracturing effect prediction model and optimize the fracturing plan. In this study, more than 500 fracturing oil wells in a tight sandstone block are taken as objects to build a knowledge graph. Based on well test and production test data and historical production, this study predicts the fracturing stimulation effect and optimizes the fracturing engineering parameters. The calculation results indicate that factors such as reservoir thickness, oil saturation, number of fracture clusters, and half length of fractures have a significant impact on the fracturing effect. The coincidence rate between the predicted capacity of production and the actual capacity of production is over 91%, and the efficiency of fracturing plan design is increased by more than 20 times. The research results can provide scientific basis for predicting fracturing effects and optimizing fracturing engineering parameters, greatly improving the efficiency and quality of fracturing design, and improving the success rate of fracturing construction.