"Optimizing Hydrocarbon Field Development Using a Genetic Algorithm Based Approach" by Antonio Carlos Bittencourt

A real project was optimized using two approaches: the first one had the proposed solution inserted in the initial population and the second one did not. This second approach was able to find a better solution spending less function evaluations than the first one, which suffered from premature convergence.

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 infeasible 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 and if it is desirable to allow the parameters to vary.

A typical reservoir development problem 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 problem. Also, each hypothesis can generate other ones, and so, the overall process is one of generating a hypothesis tree.
More and more data is generated and analyzed. From this analysis more questions and hypotheses arise, often causing the engineer a sense of something left out when concluding the work. The solution of this problem requires the effort of several people as well as 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 a proper 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.

The objective function consists of a cash flow analysis for production profiles obtained from simulation runs considering a particular set of parameter values. The optimizing procedure is able to interface with commercial simulators (generating its input data and retrieving the results) that work 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. These approaches can speed up the study of a hydrocarbon reservoir development plan and allow consideration of a wider range of hypotheses. The engineer can also keep track of economics during the study, allowing better project decisions.