4.5 Article

Determination of vertical/horizontal well type from generalized field development optimization

Journal

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
Volume 162, Issue -, Pages 652-665

Publisher

ELSEVIER
DOI: 10.1016/j.petrol.2017.10.083

Keywords

Field development; Well placement; Well type; Rate optimization; Well spacing

Funding

  1. King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at the King Fahd University of Petroleum & Minerals (KFUPM) as part of the National Science, Technology and Innovation Plan [12-OIL2998-04]

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Field development planning entails determination of the optimal number of wells (horizontal/vertical), well types (producer/injector), well locations, and well controls. The numerous variables to be estimated and the reservoir geological uncertainty complicate the problem of determining the optimal development plan. The improvements in computational science have advanced the use of automated optimization for field development decisions. In this paper, we present a generalized field development optimization methodology for estimating vertical and horizontal well-types in developing a hydrocarbon reservoir. Two approaches for the generalized field development optimization are presented. One approach is based on the well control zonation (WCZ) procedure; the other is based on the mixed integer non-linear programming (MINLP) procedure. In the WCZ procedure, five zones were identified, each signifying a region of either horizontal injection wells, vertical injection wells, no wells, vertical production wells or horizontal production wells. In the MINLP approach, separate variables were defined for the determination of the well type. Additionally, we present an efficient method to constrain the minimum well spacing of the horizontal and vertical wells to predetermined parameters. The aim was to optimize the number of wells, the well types, the well locations and the well controls (production rates). These operational variables were estimated simultaneously. Particle swarm optimization (PSO) algorithm was used as an optimizer to determine the optimal parameter values. The WCZ and MINLP approaches were tested with two examples to determine which methodology was more effective. The results indicated that both methods could successfully determine the optimal parameters while satisfying the spacing constraints imposed by the user. The comparison of the results showed that the WCZ approach was more effective than the MINLP approach.

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