Optimization of Vertical Well Placement for Oil Field Development Based on Basic Reservoir Rock Properties using a Genetic Algorithm

Abstract

Comparing the quality of basic reservoir rock properties is a common practice to locate new infills or development wells for optimizing an oil field development using a reservoir simulation. The conventional technique employs a manual trial and error process to find new well locations, which proves to be time-consuming, especially, for a large field. Concerning this practical matter, an alternative in the form of a robust technique was introduced in order that time and efforts could be reduced in finding best new well locations capable of producing the highest oil recovery. The objective of the research was to apply Genetic Algorithm (GA) in determining wells locations using reservoir simulation to avoid the manual conventional trial and error method. GA involved the basic rock properties, i.e., porosity, permeability, and oil saturation, of each grid block obtained from a reservoir simulation model, which was applied into a newly generated fitness function formulated through translating the common engineering practice in the reservoir simulation into a mathematical equation and then into a computer program. The maximum of the fitness value indicated a final searching of the best grid location for a new well location. In order to evaluate the performance of the generated GA program, two fields that had different production profile characteristics, namely the X and Y fields, were applied to validate the proposed method. The proposed GA method proved to be a robust and accurate method to find the best new well locations for field development. The key success of this proposed GA method is in the formulation of the objective function.

Prospects of Intelligent Oil and Gas Fields

“…These problems include the intellectualization of analysis of large amounts of data collected from oil and gas fields, the intellectualization of drilling process, the forecast of reserves and optimization of oil and gas production, the optimization of the location and management of oil and gas fields, etc. For solving these and other problems, artificial neural networks [15][16][17][18][19], fuzzy logic [20][21][22][23][24], expert systems [25][26][27][28], machine learning methods [29], intelligent agents [30,31], genetic algorithms [32][33][34][35], data extracting methods [36,37], case-based reasoning -CBR [38][39][40], etc.…”

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

“…Various parameters must be considered to determine optimal location. These include but are not limited to permeability, porosity, and oil saturation [85]. Well location optimization algorithms often run all iterations within commercial high fidelity reservoir simulators, although use of proxy modeling is becoming more prominent.…”

“…Bukhamsin et al [97] use continuous GAs to optimize the development of a real field located in the Middle East and consider complicated well geometry such as multilateral wells (MLWs) and maximum reservoir contact (MRC) wells. Ariadji et al [85] use GAs in a commercial simulator with varying well drainage radii and find this is a superior method to traditional guess and check. Emerick et al [98] utilize the Genocop III algorithm (GA for Numerical Optimization of Constrained Problems) to handle the well placement constraints.…”

Optimizing the wells location using the Machine learning algorithms, as well as optimizing the drilling time of production and injection wells in one of the reservoirs in south west of Iran

“…In 2012 Ariadji ,T., et al used the GA optimization algorithm for well placement optimization. Their investigations indicate that the GA algorithm is a precise and reliable method for optimizing well placement in the studied eld [12]. In 2013, D. Reid et al used the optimization algorithms DE, PSO, and HPSDE for well placement optimization.…”