Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical Application

Recently, production
optimization has gained increasing interest
in the petroleum industry. The most computationally intensive and
critical part of the production optimization process is the evaluation
of the production function performed by the numerical reservoir simulator.
Employing proxy models as a substitute for the reservoir simulator
is proposed for alleviating this high computational cost. In this
study, a new approach to construct adaptive proxy models for production
optimization problems is proposed. An adaptive difference evolution
algorithm (SaDE) optimized least-squares support vector machine (LSSVM)
is used as an approximation function, while training is performed
using a self-adaptive response surface experimental design (SaRSE).
SaDE selects the optimal hyperparameters of LSSVM during the training
process to improve the prediction accuracy of the proxy model. Cross-validation
methods are used in the recursive training and network evaluation
phases. The developed method is used to optimize the production of
block gas reservoir models. Computational results confirm that the
developed adaptive proxy model outperforms traditional regression
methods. It is further verified that when the experimental data are
updated, the alternative model still has high prediction accuracy
when performing the objective function evaluation. The results show
that the proposed proxy modeling approach enhances the entire optimization
process by providing a fast approximation of the actual reservoir
simulation model with better accuracy.