An efficient automatic history matching method through the probabilistic collocation based particle filter for shale gas reservoir

Abstract

Shale gas can be economically developed with the advancement of horizontal well drilling and hydraulic fracturing. The complex multiscale flow mechanisms associated with the shale gas production simulation model can lead to severe model non-linearity, and the model parameters may not have the Gaussian distribution. Particle filter is a sequential data assimilation method derived from Bayesian theorem and can be an effective automatic history matching method for shale gas reservoir without the limitation on the model linearity and Gaussian assumption. However, particle filter may require a large amount of model evaluations to obtain the stable and accurate results. To improve the computational efficiency of the particle filter, we propose the probabilistic collocation based particle filter method (PCPF). An illustrative case study on shale gas production is conducted to validate the efficiency and accuracy of the proposed PCPF method. The results show that the computational efficiency can be greatly improved and it makes the full particle filter analysis computationally affordable. With more measurement data available, the parameter estimations and the corresponding shale gas production predictions can be more accurate and the uncertainties associated with the parameter estimations and shale gas production predictions are significantly reduced during the history matching process.