Summary

Reservoir model parameters generally have quite large uncertainty ranges and need to be calibrated by history matching available production data. The ensemble of calibrated models has a direct impact on business decision making, such as EUR assessment and well spacing optimization. Multi-realization history matching (MHM) techniques have been applied to quantify the EUR uncertainty. In practice, MHM requires performing a large number of reservoir simulations on a distributed computing environment. Given the current low oil price environment, it is demanding to reduce the computational cost of MHM without compromising forecasting quality. To solve this challenge, this paper proposed a novel EUR assessment method that integrates a machine learning technique with an advanced distributed computing technique in a history matching workflow.

Starting from an initial ensemble of reservoir models, each realization is calibrated iteratively with a previously published Distributed Gauss-Newton method (DGN). Responses of the reservoir realizations are generated by running simulations on a high-performance-cluster (HPC) concurrently. The responses generated during iterations are added to the training data set, which is used to train a set of support vector regression (SVR) models. As the sensitivity matrix for each realization can be estimated analytically from the SVR models, the DGN can use the sensitivity matrix to generate better search point such that the objective function value can decrease more rapidly. The procedure is repeated until convergence.

The proposed method is applied to assess EUR for several wells in the Permian Liquid Rich Shale Reservoir field with complicated subsurface oil/water/gas multiphase flows. The uncertain parameters include reservoir static properties, hydraulic-fracture properties, and parameters defining dynamic properties such as relative permeabilities, etc. With the integration of SVR in DGN, the new method saves about 65% of the simulation runs compared to the method without using SVR. Efficiency comes from learning the simulated results in the previous iterations. The case study indicates that the new method provides faster EUR forecast and comparable uncertainty ranges compared with those obtained without using SVR. More importantly, the new method enhances the statistical learning of reservoir performance and therefore significantly increases capital efficiency for exploiting unconventional resources.

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