Abstract
Reservoir modelling is repeatedly employed in petroleum reservoir studies to understand and analyse petroleum reservoirs, evaluate and/or quantify subsurface uncertainties and to generate production forecasts. For the reservoir to be described and understood with sufficient accuracy, the presence of an external energy in the reservoir must be identified and described. One of the key petroleum tools employed for this endeavouris the Material Balance. Itcan be used to understand the reservoir behaviour, including the size and response of any connecting aquifer that may be present.
This study focuses on the application of a non-deterministic method - Particle Swarm Optimization Algorithm (PSO) in identification and estimation of the aquifer properties – aquifer-to-reservoir radius ratio and original oil in place in a petroleum reservoir. The method can be applied to both simple and complex reservoirs for quick analysis and interpretation of historical production data. While current methods use a deterministic approach to generate an ‘optimum’ solution, PSO generates an ensemble of solutions making it possible to estimate the uncertainty bandwidth and thus a confidence interval for certain parameters. This is done by applying the Havlena-Odeh material balance equation and by using production data available. We show that Particle Swarm Optimization algorithm is able to identify the aquifer-to-reservoir radius ratio and original oil in place uncertainty bandwidth. Furthermore, a P90 analysis of the results obtained compared fairly well to other methods and actual results.
