Optimization of Well Placement

Abstract

Optimal placement of oil, gas or water wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, as well as economic parameters. An optimization approach that enables the evaluation of all these information is presented. A hybrid of the genetic algorithm (GA) forms the basis of the optimization technique. GA operators such as uniform, single-point, two-point crossover, uniform mutation, elitism, tournament and fitness scaling were used. An additional operator that employs kriging is proposed. The GA was hybridized with the polytope algorithm, which makes use of the trends in the search space. The hybrid algorithm was tested on a set of mathematical functions with different characteristics in order to determine the performance sensitivity to GA operators and hybridization. Simple test cases of oil production optimization on 16×16 simulation grids with known optimum well locations were carried out to verify the hybrid GA results. Next, runs were carried out for a 32×32 problem. The locations of a production and injection well were optimized in the case of three existing producers. Exhaustive runs were carried out for these cases to determine the effects of the operators, hybridization and the population size on the performance of the algorithm for well placement problems. Subsequently, the optimum configuration of two injection wells were determined with two existing producers in the field. It was observed that the hybrid algorithm is able to reduce the required number of simulations substantially over simple GA. [S0195-0738(00)00502-1]

Petrol ve Doğal Gaz Platformlarının Optimal Yerleştirilmesi ve Entegrasyonu Problemi Üzerine

“…Petrol ve doğal gaz kuyularının yerleştirilmesi ile ilgili çalışmalar Güyagüler ve Horne [4], kuyu yerleştirme optimizasyonu ile ilgili yeni bir algoritma sunmuşlardır. Bu çalışmada petrol, doğal gaz ve su kuyularının optimal yerlerinin bulunması için gereken rezervuar ve sıvı özellikleri, kuyu ve yüzey ekipmanlarının özellikleri ve ekonomik parametreler gibi birçok değişkenin değerlendirilmesine olanak sağlayan bir optimizasyon yaklaşımı sunulmuştur.…”

Flexible genetic algorithm: A simple and generic approach to node placement problems

“…The node placement problem (NPP) is an important problem in many fields, such as radio frequency identification (RFID) [1], wireless sensor networks (WSNs) [2], wind farm design [3], and oil and gas industry [4]. The task of an NPP solver is to place a number of nodes optimally in a given area to meet 5 certain predefined objectives.…”

“…For example, in the field of WSNs, an NPP is 10 sometimes referred to as a 'relay node placement problem' [2,[5][6][7][8]. In other fields, specific terms such as RFID network planning (RNP) [1,[9][10][11][12], wind farm layout optimization (WFLO) [3,[13][14][15][16], and well placement optimization (WPO) [4,[17][18][19] problems are used. The definitions of the problems are different, but have something in common.…”

Use of quality maps in reservoir management

“…Based on a study of the time for simultaneous eruption of water and gas, the authors sought to determine the ideal location for the wells and stated that the parameters which influenced the most this allocation were oil flow rate, oil viscosity, oil formation volume factor, difference of density between the oil, perforation interval length, oil water mobility ratio and water column height. Some authors have chosen to fully automatise the process of optimising production systems, by making use of recent techniques, such as genetic algorithms (GA): Bittencourt and Horne (1997) and Montes et al (2001) resorted to GA to help in the search for the best location for wells, whereas Guyaguler and Horne (2000) presented an optimisation procedure based on a hybrid genetic algorithm, which reduced the number of simulations when compared to simple GA. Yang et al (2003) presented a system which simulated the reservoir, the wells and the surface facilities; the authors used GA and simulated annealing (AS) algorithms to optimise the global system. ABCM Another possible approach is to develop a methodology which allows the combination of the reservoir engineer's experience and common sense with the great potential that visualisation techniques present for the solution of problems such as the one outlined previously.…”