基于启发式优先级函数的高效钻序优化

Zhenzhen Wang, Jincong He, Shusei Tanaka, X. Wen
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摘要

钻序优化是油气行业普遍面临的难题,但由于其独特的特点和局限性,现有的优化方法无法有效解决。对于许多油田来说,目前的钻井队列是基于工程启发式人工设计的。本文将启发式优先级函数与传统优化器相结合,以较低的计算成本提高优化效率,加快决策过程。构建启发式优先级函数,将单井属性(如井指数和井间距离)映射到井优先级值。顾名思义,优先级较高的井将在队列中较早钻探。启发式优先级函数是井间连通和驱替效率的综合度量。例如,对产层提供快速支持的注水井或产层有更好机会排出未扫井区域的注水井往往得分较高。它包含权衡井的不同属性的组件。然后在优化过程中对这些组件进行优化,以生成有益的钻井序列。嵌入了油藏工程启发式方法,优先级函数帮助优化器专注于探索有前景的方案。提出的启发式优先级函数与遗传算法(GA)相结合,通过Brugge油田和Olympus油田的钻序优化问题进行了验证。直接在钻井序列上执行的优化被用作参考案例。研究了不同的连续/分类参数化方案和各种形式的启发式优先级函数。我们的勘探表明,启发式优先函数包括井类型、约束条件、井指数、与现有井的距离以及邻近的油位,可以获得最佳结果。与参考方法相比,所提出的方法能够实现更好的优化起点(由于更合理的钻取序列而不是随机猜测,提高了~ 5-18%),更快的收敛速度(结果稳定在12次迭代vs. 30次迭代),并且计算成本更低(150-250次vs. 1300次运行以实现相同的NPV)。在北海类型油藏的另一个应用中也观察到类似的性能改善。这证明了所提方法的普遍适用性。与传统的直接优化钻序方法相比,启发式优先级函数的应用提高了钻序优化的效率和可靠性。它可以作为一个独立的模块轻松地嵌入到商业或研究模拟器中。此外,它也是一个自动过程,很适合迭代优化算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Drill Sequence Optimization Using a Heuristic Priority Function
Drill sequence optimization is a common challenge faced in the oil and gas industry and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drill queue is currently designed manually based on engineering heuristics. In this paper, a heuristic priority function is combined with traditional optimizers to boost the optimization efficiency at a lower computational cost to speed up the decision-making process. The heuristic priority function is constructed to map the individual well properties such as well index and inter-well distance to the well priority values. As the name indicates, wells with higher priority values will be drilled earlier in the queue. The heuristic priority function is a comprehensive metric of inter-well communication & displacement efficiency. For example, injectors with fast support to producers or producers with a better chance to drain the unswept region tend to have high scores. It contains components that weigh the different properties of a well. These components are then optimized during the optimization process to generate the beneficial drill sequences. Embedded with reservoir engineering heuristics, the priority function helps the optimizer focus on exploring scenarios with promising outcomes. The proposed heuristic priority function, combined with the Genetic Algorithm (GA), has been tested through drill sequence optimization problems for the Brugge field and Olympus field. Optimizations that are directly performed on the drill sequence are employed as reference cases. Different continu- ous/categorical parameterization schemes and various forms of heuristic priority functions are also investigated. Our exploration reveals that the heuristic priority function including well type, constraints, well index, distance to existing wells, and adjacent oil in place yields the best outcome. The proposed approach was able to achieve a better optimization starting point (∼5-18% improvement due to more reasonable drill sequence rather than random guess), a faster convergence rate (results stabilized at 12 vs. 30 iterations), and a lower computational cost (150-250 vs. 1,300 runs to achieve the same NPV) over the reference methods. Similar performance improvement was also observed in another application to a North Sea type reservoir. This demonstrated the general applicability of the proposed method. The employment of the heuristic priority function improves the efficiency and reliability of drill sequence optimization compared to the traditional methods that directly optimize the sequence. It can be easily embedded in either commercial or research simulators as an independent module. In addition, it is also an automatic process that fits well with iterative optimization algorithms.
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