A NOVEL OPTIMIZATION MODEL FOR ANALYZING PRODUCTION DATA

The production data analysis is very important for petroleum industry since it serves as the fundamental step for reservoir development. In computer science, the self‑adapting bionic optimization model is a state‑of‑the‑art intelligent computation technique which can be used to analyze production data. In this paper we applied the self‑adapting bionic optimization model to analyze production data. This method combines the heuristic approach, grey correlation analysis, and fuzzy clustering method in computer science to optimize the reservoir production plan. To develop the method, we firstly perform grey correlation analysis to describe the initial correlation between production data parameters. By applying fuzzy clustering method, the production data are divided into different categories according to the degrees of their correlations. This heuristic information is used to construct the production data analysis model. Finally the best‑optimized correlations among the various production data can be discovered. A simple example with related parameters is presented to show the algorithmic strategies. This example proved that our method is a flexible and cost‑effective quantitative analysis method for production data analysis. To evaluate this model, we selected 50 wells in some gas reservoirs from Xinjiang oilfield, China. The 50 wells have been developed for many years. Therefore, we have enough data on the 50 wells to test this model. It is demonstrated that this model is useful in production data analysis.