Deep Learning-Driven Optimization of Development Plans for Hydrocarbon Reservoirs
Authors: Gupta D. K. , Belayouni N. , Tahir S. , Al Tandi A. A. , Ayoubi R. , Gubbala M. , Elfeel M. A. , Su S. , Amri M. , Mustapha H. 
Published: October 2023

Abstract
This paper explores the potential of deep learning for developing metamodels that can be used as proxies for large-scale reservoir simulators. We investigate how these proxies can be used to optimize any reservoir or field development plans. Using an advanced optimization technique with a numerical reservoir model for large reservoirs is computationally very challenging, hence we investigate if using deep learning proxies to replace the physical simulator can help to alleviate this issue.
We present an optimization framework wrapped around data-driven metamodels to perform structured and inexpensive optimization of reservoir development plans in an end-to-end approach. The overall performance of any reservoir depends on various factors such as field-control parameters (oil production rate cutoff, water injection rate cutoff, etc.) as well as well-related parameters, among others, referred to as FDP constraints. Our approach uses a pipeline of deep learning models to accurately predict these parameters, which in turn generate the economic outputs associated with the reservoir development scenario. These models are trained and tested using 700+ simulation cases with carefully sampled FDP constraints.
We demonstrate the efficacy of the presented approach on synthetic as well as real datasets. For all cases, we consistently observe that our optimized results are superior to all the simulation cases that are used for training and testing the metamodels. The presented approach is robust to the choice of the economic model and the FDP constraints can be optimized for any choice of the economic objective. Our optimization framework implicitly ensures that the trained metamodels are robust and accurate enough, and this is achieved through novel evaluation metrics built for this purpose. These metrics serve as quality measure to ensure that the optimization results are minimally hampered by the limitations of the metamodel. They are used to rank the predictions of the metamodel against the ground-truth values to validate how well the trained metamodel can reconstruct the parameter hyperspace. Moreover, the uncertainty measure associated with each optimized solution provides the flexibility to make a wise business choice based on any additional constraints that are not considered in the optimization process. Overall, the learnt proxy metamodels serve well to replicate the behavior of the original reservoir simulators, and the optimization results seem promising.
The standard approach of running a physics-based reservoir simulator for every specific case is computationally very expensive, and it cannot be used to optimize the FDP constraints. Our presented approach alleviates this issue and can be used to find the optimal parameters almost inexpensively. The full optimization including the training of the metamodels can be completed in a few hours on an average grade laptop without the need of any powerful graphics card.