Efficient Assessment and Optimization of a Deepwater Asset Development Using Fixed Pattern Approach

Abstract

Decisions made during the reservoir development phase have major impact on the project economics. Due to reservoir complexity and severe nonlinear interactions between decision parameters, automated optimization algorithms may be needed to supplement intuitive reservoir engineering analysis. Previous algorithms, proposed for well placement optimization, search for optimum well locations without full consideration of engineering knowledge and practice. This may result in degradation of optimization efficiency (number of simulations) and output unrealistic suboptimal well configurations. This study proposes an approach that superimposes engineering knowledge and practice on an automated optimization routine (the Hybrid Genetic Algorithm, HGA) to maximize the value and efficiency of field development optimization. The new approach is called the Fixed Pattern Approach (FPA), since it enforces the HGA to locate thewells on a user defined pattern inspiring from the idea that the wells are more likely to be drilled in specific patterns (line, staggered-line, five-spot etc.). The FPA essentially incorporates reservoir engineering concepts within the parameterization of optimization problem and reduces the search-space size considerably by eliminating the simulation of infeasible development scenarios. The FPA was applied to a deepwater field to get a quick understanding about the possible optimum development scenarios. In the scope of this study, finding the optimum development plan was defined as determining the optimum number and location of producers and injectors, as well as production rates and the starting time of injection that maximizes the Net Present Value (NPV) of the project. These parameters were optimized simultaneously using a black oil reservoir simulator as the objective function evaluator. Comparison with respect to the conventional window approach showed that FPA significantly reduced the CPU time and resulted in practical and economical well locations. Uncertainty in the geological properties (porosity, permeability) and reservoir structure was also addressed for this field by performing optimization on five different models constructed by a Plackett-Burman experimental design. The new methodology recommended an optimum field development plan while taking the uncertainty of property distributions and reservoir structure into account. Introduction Genetic Algorithms (GAs) have been used as global search engines in various optimization problems[1, 2] since they have been introduced by Holland[3] (1975). Hybridization of GAs with the hill-climbing methods[4] (simplex) improved GAs' local search capability. Aanonsen et al.[5] (1995) optimized the well locations using several regression techniques and showed that simple statistical methods could be adequate to find optimal solutions in some cases. Bittencourt and Horne[6] (1997) showed that optimization efficiency of GAs were improved when GAs were hybridized with the polytope algorithm and tabu search. They called this hybrid optimization technique the Hybrid Genetic Algorithm (HGA). Guyaguler et al.[7] (2000) minimized the number of simulations of an injector location optimization by coupling a HGA with a kriging proxy. Yeten et al.[8] (2002) optimized the trajectory of non-conventional wells by combining GA with hill-climbing methods and an Artificial Neural Network (ANN) proxy. Ozdogan and Horne[9] (2004) optimized the recovery of oil and information from the wells by embedding recursive probabilistic history matching steps into a HGA. They showed that early stage well locations could be optimized in order to reduce the uncertainty associated with the later stage wells. Previous approaches utilized various types of proxies to minimize the computational effort but none of them focused on the geometrical limitations of the well placement problem.

Optimization Wells Placement Policy for Enhanced CO2 Storage Capacity in Mature Oil Reservoirs

“…In the case of large-scale problems, optimization of the individual well's location is computationally demanding and time-inefficient [33]. Reducing the size of the optimization problem involves introducing certain well patterns [34][35][36][37].…”

Process systems engineering perspective on the planning and development of oil fields

“…Most works assume a linear trunk with some recent attempts at curved trunks . Although many studies have focused on the structure and functionality (injector vs producer) of a single well, Ozdogan et al and Onwunalu and Durlofsky addressed patterns of repeated well structures for multiple wells.…”