Investigating Proxy Models for a Production System in Integrated Simulations with Oil Reservoir

This work evaluates the Proxy model application representing the production system for integrated simulation with a reservoir to reduce computational time while preserving the representativeness of financial return and hydrocarbon production behavior relative to a reference model. It includes specific Proxy models for production systems in integrated simulations that include their geometrical parameters, focusing on field production strategy optimization. The production system’s Proxy models are developed through response surface methodology (RSM) and artificial neural network (ANN), which are generated and validated from a medium fidelity model (MFM). The validation is performed by cross-checking simulations. The developed RSM-based Proxy model obtained the highest representativeness by combining discrete variables (pipe segment diameters and the gas flow rate for artificial lift) with split continuous variables (lengths of the production column and flowline, liquid rate, and water cut) using several response surfaces. The developed ANN-Based Proxy model enhanced representativeness by combining all variables and increasing the number of MFM samples for ANN training. The RSM-Based Proxy model was selected due to its lower residual value than the ANN-Based Proxy model. The results from the production strategy of the simulated Proxy model in the MFM showed a difference of ۴% in net present value compared to the simulation of the reference model, with both strategies obtained inside a production strategy optimization process. The reduction of computational time was close to ۳۰% with the selected Proxy model, which it presents an advantage of using the proposed approach in optimization applications. The developed methodology provides an alternative to replace more robust production system models in integrated simulations with several advantages, such as: reduction of computational times, applications in more complex problems, and better-exploring uncertainties, and thereby,  faster decision-making is obtained.

کلیدواژه ها:

Proxy models ، Numerical Simulation ، Response Surface Methodology ، Artificial Neural Network ، Optimization