Selecting the Best Pilot Area for Water-based EOR Using Artificial Intelligence and Multi Criteria Decision Making Algorithms

Selecting the best candidate pilot area is one of the most important and challenges decisions in the oil and gas filed development plan. Pilot-scale projects are conducted to reduce reservoir uncertainties and investment risk and the lesson learned from this study will be extended to the full field implementation. The main objective of this study is to utilize sseveral geological, operational and economic criteria to make decisions optimally among candidate areas. Firstly, reservoir similarity index (RSI) is calculated using the oil production history, saturation data. For this reason, clustering methods including k-means, k-medoids and c-means are used to identify the center of the dominant cluster. Afterwards, other operational criteria such as the number of interference, adjacent wells, the average distance between these wells and area center and the average distance from facilities are determined for all candidate areas. Finally, the decision matrix is created and then multi-criteria decision making (MCDM) methods are utilized to calculate pilot opportunity index for each area. According to the obtained results, the assigned pilot opportunity index of the hierarchical analysis method was equal to 11.50, 10.46 and 6.97%, and for the Shannon entropy method it was equal to 11.67, 9.80 and 6.80%, respectively for the top three pilot areas. The area with the highest value is selected as the first rank candidate for pilot implementation. Moreover, mean rank method is utilized to aggregate and introduce the best pilot area.