Intelligent optimization of fracturing stage and cluster parameters for tight oil reservoir

Abstract

The Mahu oilfield in the Junggar Basin of Xinjiang has the characteristics of poor reservoir quality, large horizontal stress difference, and strong heterogeneity, which poses challenges in oil production due to the unclear hydraulic fracture geometry, large fracturing effectiveness difference among wells/stages, and the lack of automation in stage and cluster designs. To address the above issues, this study proposes systematically intelligent designs for stage and cluster parameters in the tight conglomerate oil reservoir in the Ma131 well area. First, through sensitivity analysis, the key parameters for stage division (storage coefficient, brittleness index, and minimum horizontal principal stress) are identified, and a stage division algorithm is developed based on the similarity of these key parameters. In order to quickly calculate the productivity of different cluster designs, a single cluster production prediction dataset was established by using the fracturing-production integrated numerical simulation method. Based on the results of fracturing stage division, cluster spacing and injection volume are quickly optimized using the above dataset, and the cluster locations are optimized with the objective of balanced fracture initiation and propagation. Finally, the automatic designs of fracturing stage and cluster starting from the well logging data is realized. Then, the proposed optimization method is applied to a practical well and both the production and profit are increased with the optimized designs. The proposed method can efficiently and intelligently optimize the stage and cluster designs for horizontal wells with the consideration of fracture propagation, productivity, and economic benefits, which helps provide theoretical and methodological support for fracturing designs in unconventional reservoirs such as the tight conglomerate oil reservoirs in this work.