Inverse Problems in the Prediction of Reservoir Petroleum Properties using Multiple Kernel Learning
Seminar Room 1, Newton Institute
In Reservoir engineering a common inverse problem is that of estimating the reservoir properties such as Porosity and Permeability by matching the simulation model to the dynamic Production data. Using this model, future predictions can then be made and the uncertainty of these predictions quantified using Bayes Rules.
Multiple Kernel Learning (MKL) is an inverse problem that maps input data into a feature space with the use of kernel functions. MKL is a predictive tool that has been applied in the Petroleum Industry to estimate the spatial distribution of Porosity and Permeability. The parameters of the kernels and the choice of the kernels are determined by matching to hard data for Porosity and Permeability found at the wells thus producing a static model that is used as input into the dynamic model.
In this paper we show how we combine the above mentioned inverse problems. We estimate the Porosity and Permeability into a static model then match to the dynamic production data to tune the parameters in the Multiple Kernel Learning Framework. Specifically we integrate the MLE estimation from the MKL objective Function into the History Matching Function.