Abstract
Drilling cost is one of the most critical aspects in the reservoir management plan. Costs may exceed a million dollars; thus, optimal designing of the well trajectory in the reservoir and completion are essential. The implementation of a multilateral well (MLW) in reservoir management has great potential to optimize oil production. The object of this study is to develop an integrated workflow of end-point multilateral well placement optimization integrated with the reservoir simulator supported by artificial intelligence (AI) methods. The paper covers various types of MLW construction, including: horizontal, bi-, tri-, and quad-lateral wells. For quad-lateral wells, the capital expenditure is highest; nevertheless, acceleration of oil production affects the project’s NPV (net present value), indicating the type of well that is most promising to implement in the reservoir. Tri- and quad-lateral wells can operate for 12.1 and 9.8 years with a constant production rate. The decreasing hydrocarbon production rate is affected by reservoir pressure and the reservoir water production rate. Other well design patterns can accelerate water production. The well’s optimal trajectory was evaluated by the genetic algorithm (GA) and particle swarm optimization (PSO). The major difference between the GA and PSO optimization runs is visible with respect to water production and is related to the modification of one well branch trajectory in a reservoir. The proposed methodology has the advantage of easy implementation in a closed-loop optimization system coupled with numerical reservoir simulation. The paper covers the solution background, an implementation example, and the model limitations.
Highlights
We provide a state-of-the-art background and place the proposed approach among other solutions.The Introduction consists of a review of the application of multilateral well system in various production and injection scenarios, a review of previous works related to multilateral well optimization, a brief description of the optimization algorithms that can be employed in the solution with special consideration of the genetic algorithm and particle swarm optimization.1.1
The end-point multilateral well optimization model is based on the reservoir numerical simulator
The optimal solutions for the location of the nodal points of the multilateral wells obtained using optimization algorithms are presented in Tables 4 and 5
Summary
We provide a state-of-the-art background and place the proposed approach among other solutions.The Introduction consists of a review of the application of multilateral well system in various production and injection scenarios, a review of previous works related to multilateral well optimization, a brief description of the optimization algorithms that can be employed in the solution with special consideration of the genetic algorithm and particle swarm optimization.1.1. The Introduction consists of a review of the application of multilateral well system in various production and injection scenarios, a review of previous works related to multilateral well optimization, a brief description of the optimization algorithms that can be employed in the solution with special consideration of the genetic algorithm and particle swarm optimization. Multilateral Wells as an Asset in Reservoir Management. The interest in multilateral wells has increased. The advanced technologies for the completion of productive horizons using multilateral or intelligent wells offer several possibilities to optimize hydrocarbon recovery and economic results [1]. The biggest advantages of multilateral wells are the increased formation drainage area and enhancement of productivity. This technology brings the benefits of reducing water cresting and coning due to complex structured branch wells usually
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