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Using Optimization Techniques for Determining Optimal Locations of Additional Oil Wells in South Rumaila Oil Field

机译:利用优化技术来确定南方南方油田额外油井的最佳位置

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Determining the optimal location of the wells is a crucial decision to be made during a field development plan. A reservoir simulator called "SimBest II" has been used in the present study. The oil field under study is a sector of South Rumaila oil field/ main pay. Two methods of optimization have been adopted in the present study. The first one is manual optimization and the second method is automatic optimization. Genetic Algorithm (GA) was used as the automatic optimization method to find the best number and locations of the wells. Genetic algorithm depends on the principle of artificial intelligence similar to Darwin's theory of Natural Selection. The genetic program is coupled with the simulator in order to re-evaluate the optimized wells at each iteration. The genetic algorithm computer program gave results similar to the results that are obtained by the manual method with less computer time. Both methods have been adopted according to the aspects of net present value (economic evaluation) as objective function. According to the relationship between net present value and future production time, the abandonment time was estimated before end of December 2014 for all proposed future scenarios. The optimal future scenario was with water injection of 15000 surface bbls/day per well. The optimal number of additional wells for this case was nineteen wells, but it may be impossible to drill such a number of wells in the one year especially in Iraq. Moreover, that an alternative option of drilling three wells with reduction of 0.07399 % in NPV has been considered. This option is also suggested if the surface injection facilities cannot handle the injection of 15000 surface bbls/day. The optimal number of additional wells for this choice is also three wells.
机译:确定井的最佳位置是在现场开发计划期间进行的重要决定。本研究中使用了称为“Simbest II”的储层模拟器。在研究下的油田是南方南方油田/主要工资的部门。本研究采用了两种优化方法。第一个是手动优化,第二种方法是自动优化。遗传算法(GA)用作自动优化方法,以找到井的最佳数量和位置。遗传算法取决于人工智能与达尔文自然选择理论的原则。基因程序与模拟器耦合,以便在每次迭代中重新评估优化的孔。遗传算法计算机程序给出了类似于手动方法而具有更少计算机时间的结果的结果。根据目标函数的净目前(经济评估)的各个方面采用了两种方法。根据净目前的关系与未来生产时间之间的关系,放弃时间在2014年12月底为所有拟议的未来情景之前估计。最佳的未来情景是用水注射每孔15000个表面BBLS。这种情况的最佳数量的额外井是十九个井,但在一年中可能是不可能在伊拉克的一年中钻出这样的井。此外,已经考虑了在NPV中钻取三个孔的替代选择,在NPV下减少0.07399%。如果表面注射设施不能处理15000个表面BBLS / Day的注射,还建议该选项。这种选择的最佳井的最佳数量也是三个井。

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