Abstract

Abstract It was observed that numerous simplifications are still used in the oil and gas industry when making decisions, which can lead to erroneous decisions in complex design situations. The objective of the study is to enhance the decision-making process for one of the key design factors for water-flooding projects, namely pattern size, by using a simulation-economic-simulation approach. With the help of this approach, the best injection pattern size is chosen for use in the water-flooding design. Four reservoir simulation models of various water-flooding pattern sizes were updated with the wells, and reservoir characterization was run to obtain the production profile. Second, four economic models based on the production profiles from the dynamic models have been built to assist in decision-making for the four water-flooding pattern sizes. Thirdly, the economic model has been combined with a probabilistic simulation model based on an appropriate representation of all uncertain economic and development cost data for the four models. The optimal pattern size is then chosen using quantitative and objective analysis of all the water-flooding models to make decisions based on the economic indicators. The results from the four economic decision-making models showed that the method is suitable for use in a complex application, such as designing a water-flooding project with various pattern sizes. The Government (first party, FP) NPV and International Company (second party, SP) NPV and IRR for pattern sizes 160 acre, 260 acre, 360 acre, and 460 acres are estimated to be 4364 $MM, 109 $MM, 17%, and 3993 $MM, 400 $MM, 23.89, and 3534 $MM, 444 $MM, 26.34%, and 2910 $MM, 350 $MM, 24.89, respectively. The economic models of decision-making output are shown in Table 1. The best design of the pattern size has been selected based on the intersection point between the two curves of the NPV that gave the best profit for both parties. This work describes a new methodology for an integrated reservoir simulation, economic decision-making, and stochastic simulation model for achieving the optimum design. The proposed methodology, simulation-economic-simulation, is efficient and compatible with real-time operations, even in complex cases where the design is restrictive. This paper will learn how simulation insights help companies optimize their designs and systems, identify new innovations, and drive sustainable operations.

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