A New Methodology for Addressing Nonlinear Inverse Problems and its Application to Characterise a Real Petroleum Reservoir

In Petroleum Engineering, reservoir management aims to maximise the profit from a hydrocarbon reservoir. Highly nonlinear numerical models, which describe the internal structure of hydrocarbon reservoirs, are used to make reservoir management decisions. These models are aimed at providing accurate predictions of the reservoir behaviour under different scenarios. This requires that the model parameters are calibrated so that the model reproduces all the available data. Given the impossibility of directly measuring these parameters in the field, one has to infer them from indirect measurements, such as the oil production rate at a given reservoir well. This is an example of a nonlinear inverse problem. This talk will describe a recently developed methodology for addressing nonlinear inverse problems and its application to the characterisation of a real petroleum reservoir. This methodology is based on a real-coded Genetic Algorithm which has been modified to run on a cluster of computers