A new score system using data-driven approach to rank carbonate gas reservoirs in Sichuan Basin

Abstract

AbstractIn the early stages of exploration, with only a limited amount of data available, it is difficult to evaluate a reservoir and optimize the sequence of the development plan. The score system is often used to rank the reservoir based on multidisciplinary factors that combine geology, production, and economics. However, current methods that are widely employed to classify the reservoir, such as analogy or single parameter, are qualitative or inaccurate, especially for carbonate gas reservoirs with complex geological conditions. In this study, we developed a score system using a data-driven approach to rank carbonate gas reservoirs in the Sichuan Basin. We developed two approaches, expert scoring and the random forest, to rank the quality of the reservoir, which agreed well with the field development plan. The expert scoring approach, which is highly dependent on the experience of experts in this area, is more suitable for reservoirs with limited data available, especially in the early exploration stage. The random forest model, which is more robust and able to reduce uncertainty from experience, is more suitable for developed areas with sufficient data. The developed score system can help rank new resource recovery and optimize the development plan in the Sichuan Basin.