چكيده به لاتين
The first step of oil and gas reservoirs recovery is the drilling of new wells. And optimization of location and pattern of wells is an important task discussed in the oil and gas field development program. Because of the important economic role of the oil industry and the high capitalization of the oil industry and its high capital risk, decisions must be optimized at each step of reservoir development. In many cases, optimal decisions depend on many nonlinear related parameters, which make visual judgment difficult. In such cases, automatic optimization is efficient. Also, because of the pressure of the reservoir falls after a few years from the start of production of oil reservoirs, a method should be proposed to deal with this pressure drop. Gas injection is one of the preferred methods in secondary recycling. It seems necessary to provide an efficient optimization method to optimize the gas injection scenario and oil production and gas injection wells placement for the development of oil fields that are gas injected Because of many of the world's oil reservoirs have the potential to inject gas.
In this study, we first discuss an optimization method using a hybrid genetic optimization algorithm to optimize the number of production wells and total oil production rate of the well as a control parameter. In this study, due to changes in reservoir conditions over time, the control parameter of oil production in the wells is defined as a function of time. We defined a 4-parameter equation for these changes. This will reduce the number of variables to optimize the control parameter, the production rate of the oil wells, which is the number of years of production from the reservoir, to 4 variables, and the optimization time will be greatly reduced. After optimizing the number and control parameter of wells, the production wells placement are optimized using a combination genetic algorithm. In the tenth year, the production history of the gas injection scenario begins. To optimize this scenario, first, using a hybrid genetic algorithm, the number of wells and the control parameter of well gas injection, which is the pressure of the bottom of the well, is optimized. Finally, the production and injection wells are optimized by the proposed location algorithm.
According to the results of the run of the optimization scenarios, the importance of optimizing the number of wells, relevant control parameters and wells placement is clearly determined, for example, a comparison optimized reservoir and a random model show that optimized model's objective function is more than $ 70 million from random model. Also, the results of the gas injection scenario optimization indicate the importance of gas injection and optimization of its scenario. For example, a difference of about $ 5 billion between an optimized gas-free injection model and an optimized model with an optimal gas injection scenario can be noted.
Keywords: Well placement Optimization, Oil Field Development, Optimization of Gas Injection
