4.5 Article

Integrated optimization design for horizontal well placement and fracturing in tight oil reservoirs

期刊

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.petrol.2019.03.006

关键词

Fractured horizontal well; Integrated optimization design; Important design domain; Adaptive surrogate model

资金

  1. National Science and Technology Major Project [2017ZX05009-005]
  2. National Natural Science Foundation of China [51774255]

向作者/读者索取更多资源

Hydraulically fractured horizontal well technologies, which combine hydraulically fractured technology with horizontal well technology, is widely used to enhance oil recovery in tight reservoir production. Although there are many advantages to this method, it requires more investment in drilling and completion. Most academic literature to date has treated the optimization of well placement and fracturing as two separate problems. Computationally expensive numerical simulation techniques are often involved both in well placement and fracturing design optimization processes, but only suboptimal solutions are achievable. In order to achieve co-optimization for horizontal well placement and fracturing, an optimal model has been constructed, with an objective function targeted at maximizing the net present value (NPV), which is a function of location, direction and length of a new horizontal well, the number of fractures, half-length of the fracture etc. Due to the large number of optimization variables, investigating the impacts of horizontal well parameters on the objective function must utilize repetitive runs of numerical simulations involving large computation times for evaluating possible well placement and fracturing scenarios. Aiming to address this challenge, a new method, called the Multipoint Adaptive Gaussian Process Surrogate Model with Important Design Domain (MAGPSM-IDD), is proposed. Then MAGPSM-IDD were applied to a synthetic field and a real reservoir, and our results showed that MAGPSM-IDD could obtain better results with higher efficiency than the popular optimization method, Hybrid Genetic Algorithm (HGA).

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