Reservoir Development and Design Optimization
Authors: Bittencourt Antonio C. , Horne Roland N. 
Published: December 2007

Abstract
Optimization of reservoir development requires many evaluations of the possible combinations of the decision variables, such as the reservoir properties, well locations and production scheduling parameters. to obtain the best economical strategies. Running a simulator for such a large number of evaluations may be impractical due to the computation time involved.
In this study, a hybrid Genetic Algorithm (GA) was developed. The optimization algorithm integrated economic analysis, simulation and project design.
The layout of 33 new wells for a real oil field development project was proposed by the company's project team. The algorithm was used to determine an optimal relocation of the wells, by evaluating an objective function from a cash flow analysis for the production profile obtained from simulation at each iteration. The wells were allowed to be placed anywhere in the reservoir and could be vertical or horizontal and, if horizontal. any direction in the same layer could be considered. A total of 99 decision variables were used to solve this problem. All the real restrictions were included. Based on the cost of the sea bottom flowlines a second level optimization found the best platform location. The results were compared against the proposed solution, and showed that the algorithm performed very well finding an optimized well distribution. A reduction of the total number of new wells was found as part of the solution. An improvement of about 6% in the project profit was found. representing about US$70 million additional income.

Introduction
The main task of a reservoir engineer is to develop a scheme to produce as much hydrocarbon as possible within economic and physical limits. The solution of this kind of problem encompasses two main entities: the field production system and the geological reservoir. Each of these entities presents a wide set of decision variables and the choice of their values is an optimization problem. In view of the large number of decision variables it is not feasible to try to enumerate all possible combinations. Analysis tools encoded in computer programs can spend hours or days of processing for a single run, depending on their sophistication and features. Also, it can be costly to prepare the input data if many hypotheses are going to be considered.
A typical reservoir development involves many variables that affect the operational schedule involved in its management. These variables are usually used as input to a reservoir simulator that generates a forecast of the production profile. Using this forecast, the production engineer has to consider several hypotheses to achieve the best strategy for the field development. Also, each hypothesis can generate others, and so the overall process is one of generating a hypothesis tree. More and more data are generated and analyzed. The solution of these problems requires the effort of several people as well as considerable computer work and physical time.
An optimization procedure requires the characterization of the function to be optimized (minimized or maximized), known as the objective function. as well as the choice of an appropriate optimizing method. The complexity of predicting hydrocarbon production profiles requires the use of reservoir simulators. So, the simulator must be part of the evaluation of the objective function.
This work concerns the optimization of characteristic petroleum production problems considering economic factors. A hybrid algorithm based on direct methods such as Genetic Algorithm (GA). polytope search and Tabu search was developed. Hybrid techniques were found to improve the overall method. The objective function consisted of a cash flow analysis for production profiles obtained from simulation runs. The optimizing procedure was able to interface with commercial simulators (generating their input data and retrieving the results) that worked as data generators for the objective function evaluation.
These hybrid mathematical approaches were found to be successful in obtaining the optimal solution with less time and work than existing techniques. P. 545^