利用代理模型和元启发式算法优化邻井设计:Bakken案例研究

Ahmed Merzoug, Vamegh Rasouli
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引用次数: 3

摘要

裂缝驱动的相互作用FDI(俗称“压裂冲击”)是两口或多口井之间裂缝的干扰。这种干扰可能会对油井产量产生重大影响,这取决于非常规油气藏的兴趣(可能是积极的,也可能是消极的)。在这项工作中,代理模型与元启发式优化算法一起用于优化Bakken区块的完井设计。在基于物理的模拟器中建立了一个数值模型,该模拟器结合了水力压裂、地质力学和油藏数值建模作为连续模拟。采用各向异性扩展Eaton法估算应力。利用微地震耗尽圈定(MDD)和微地震事件对裂缝进行校准。通过调整相对渗透率曲线,将储层模型校准为10年的生产数据和井底压力。利用MDD和FDI记录的压力数据校准了由于枯竭引起的应力变化。校正模型后,使用Sobol和Latin Hypercube采样对注入体积、簇数、簇间距和井间距进行敏感性分析。结果利用人工神经网络建立代理模型。训练和测试的相关系数均为0.96。利用代理模型构建整个系统的净现值模型,利用灰狼算法和粒子群算法对模型进行优化,并进行优化设计。最佳设计是更宽的井距(1320英尺)、更小的簇间距(22英尺)、高注入量(1950 STB/簇)和低每级簇数(7个簇)的组合。该研究提出了Bakken地区一口水平井的最佳设计方案,该水平井与一口已经生产了10年的井相邻。该设计可应用于毗邻枯竭井的新井,以优化系统的产油量。
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Offset Well Design Optimization Using a Surrogate Model and Metaheuristic Algorithms: A Bakken Case Study
Fracture-driven interaction FDI (colloquially called “Frac-hit”) is the interference of fractures between two or more wells. This interference can have a significant impact on well production, depending on the unconventional play of interest (which can be positive or negative). In this work, the surrogate model was used along with metaheuristic optimization algorithms to optimize the completion design for a case study in the Bakken. A numerical model was built in a physics-based simulator that combines hydraulic fracturing, geomechanics, and reservoir numerical modeling as a continuous simulation. The stress was estimated using the anisotropic extended Eaton method. The fractures were calibrated using Microseismic Depletion Delineation (MDD) and microseismic events. The reservoir model was calibrated to 10 years of production data and bottom hole pressure by adjusting relative permeability curves. The stress changes due to depletion were calibrated using recorded pressure data from MDD and FDI. Once the model was calibrated, sensitivity analysis was run on the injected volumes, the number of clusters, the spacing between clusters, and the spacing between wells using Sobol and Latin Hypercube sampling. The results were used to build a surrogate model using an artificial neural network. The coefficient of correlation was in the order of 0.96 for both training and testing. The surrogate model was used to construct a net present value model for the whole system, which was then optimized using the Grey Wolf algorithm and the Particle Swarm Optimization algorithm, and the optimum design was reported. The optimum design is a combination of wider well spacing (1320 ft), tighter cluster spacing (22 ft), high injection volume (1950 STB/cluster), and a low cluster number per stage (seven clusters). This study suggests an optimum design for a horizontal well in the Bakken drilled next to a well that has been producing for ten years. The design can be deployed in new wells that are drilled next to depleted wells to optimize the system’s oil production.
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