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Abstract: This paper presents novel optimisation model to evaluate the optimal number of wells for underground gas storage operations. More specifically, generalized well-placement scheme has been formulated to minimize the total number of wells required to fulfil prescribed multiple constrains including wells interference. The scheme integrates nonlinear models of reservoir and well performance. The optimization problem has been formulated mathematically and solved by iterative use of the SIMPLEX method. The method is dedicated to gas reservoirs or underground gas storages management. The method may be useful as a base for more detailed numerical reservoir simulation. The paper is illustrated by a field example related to optimisation of the production cycle of the underground gas storage in Poland. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

The application of Enhanced Oil Recovery (EOR) techniques are encouraged by the current oil price and the growing global demand for oil. Optimizing oil production from current resources becomes a main strategic target for many oil producing companies around the world. Among EOR processes, polymer flooding is an attractive option in many reservoirs. The objective of polymer flooding is to control water mobility inside oil reservoirs to ensure high oil recovery factors. Several designing parameters are critical for the success of polymer flooding applications. Chemical concentration depends on geological features of reservoir and well pattern are crucial to make decisions for the reservoir management. In addition, identifying the time of polymer injection is also important in a project as well as slug size. The goal of this article is to evaluate the efficient polymer injection strategy based on global optimization algorithms. Authors investigated the design of polymer flooding process in terms of optimal concentration, slug size and initiation time employ nature inspired algorithms coupling with reservoir simulation. Numerical testing and comparative study show that proposed natural-inspired algorithms outperform widely reported in literature optimization methods in that it provides a higher quality solution. [ABSTRACT FROM AUTHOR]