Evolutionary Algorithms with Pairwise Conditional Sampling for History Matching Optimisation

When modelling of oil and gas reservoirs is concerned, one considers a wide range of reservoir model parameters, some of which may be dependent on others. Examples include correlation between porosity and permeability values, dependency between the structural model parameters such as fault relay ramp geometry versus its transmissibility. A reservoir engineer is always aware of the possibility of such interactions within the model studied. The logical conclusion then is to try and use this information when performing reservoir history-matching and prediction studies. The core of an efficient history matching optimisation technique is its sampling quality. Any extra information capable of guiding the search within the solution space based on the assumptions of dependence or independence between the model parameters should be welcomed into the optimisation process. This paper will concentrate on a class of evolution-inspired stochastic optimization techniques capable of sampling conditional probability distributions of model parameter – multivariate Estimation Of Distribution Algorithms. Using a synthetic reservoir model we will show that even when one considers only pairwise chain-like types of interaction between optimization parameters, this not only impacts the convergence speed of the optimization process itself but significantly influence the diversity and quality of achieved solutions.