Abstract
Recently our business has had great success in developing tools for using full physics simulators in Assisted History Matching (AHM) and prediction of future performance. However, the end user is often left wondering what to do with the large number of forecasts from these studies; any optimisation of the remaining potential has to be done manually.
This paper discusses a method in which the principles of AHM are used to optimise the development. In AHM the practitioner attempts to minimise the error between the observed and simulated data; the ‘objective function’. A large number of ‘geological’ variables to the simulator are sensitised. ‘Control’ variables are used in the predictive work. These are only limited by the scheduling features of the reservoir simulator and would normally be the number and location of development wells; change in facilities constraints; re-completion; workovers etc. The objective function for development optimisation tends to be an economic one, i.e. Net Present Value (NPV) or some other profitability indicator. The method described in this paper has an advanced feature for generating additional results based on the simulator output time series.
Schedule optimisation is known to be a very difficult problem, not amenable to standard mathematical programming algorithms, and we describe how, in conjunction with the use of a proxy model, we have successfully applied genetic algorithm optimization techniques, to solve the scheduling problem with minimal CPU processing.
The method has been used a number of times and its application is described by some case studies; optimising the number and location of development wells and timing of scheduling events. As a very large number of possible combinations of variables are used the optimisation tends to be less subjective than a manual giving generally better results.
