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Reservoir model calibration, called history matching in the petroleum industry, is an important task to make more accurate predictions for better reservoir management. Providing an ensemble of good matched reservoir models from history matching is essential to reproduce the observed production data from a field and to forecast reservoir performance. The nature of history matching is multi-objective...
Petroleum Cyber-Physical System (CPS) marks the beginning of a new chapter of the oil and gas industry. Combining vast computational power with intelligent Computer Aided Design (CAD) algorithms, petroleum CPS is capable of precisely modeling the flow of fluids over the entire petroleum reservoir and leveraging the massive field data remotely collected at the production wells. It provides field operators...
For coaled methane reservoir relative permeability curves is difficult to determine. This paper uses artificial intelligence algorithms including genetic algorithms and neural network algorithms to establish mathematical model for inversion relative permeability curves, by combination of dynamic actual production data. The results show that this method is feasible, highly practical in industry.
This work proposes a new approach for history matching using Kernel PCA to adjust the reservoir permeability field obeying geostatistical constraint. Although there are several methodologies in literature for history matching, most of them don't take into account geostatistical restrictions. Besides, history matching is a problem of huge dimensionality. So, Kernel PCA was chosen due to its ability...
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