Smart Proxy Modeling of a Fractured Reservoir Model for Production Optimization: Implementation of Metaheuristic Algorithm and Probabilistic Application

Abstract

Numerical reservoir simulation has been recognized as one of the most frequently used aids in reservoir management. Despite having high calculability performance, it presents an acute shortcoming, namely the long computational time induced by the complexities of reservoir models. This situation applies aptly in the modeling of fractured reservoirs because these reservoirs are strongly heterogeneous. Therefore, the domains of artificial intelligence and machine learning (ML) were used to alleviate this computational challenge by creating a new class of reservoir modeling, namely smart proxy modeling (SPM). SPM is a ML approach that requires a spatio-temporal database extracted from the numerical simulation to be built. In this study, we demonstrate the procedures of SPM based on a synthetic fractured reservoir model, which is a representation of dual-porosity dual-permeability model. The applied ML technique for SPM is artificial neural network. We then present the application of the smart proxies in production optimization to illustrate its practicality. Apart from applying the backpropagation algorithms, we implemented particle swarm optimization (PSO), which is one of the metaheuristic algorithms, to build the SPM. We also propose an additional procedure in SPM by integrating the probabilistic application to examine the overall performance of the smart proxies. In this work, we inferred that the PSO had a higher chance to improve the reliability of smart proxies with excellent training results and predictive performance compared with the considered backpropagation approaches.

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

“…There are a few limitations about the data-driven proxies developed in this paper. One of them pertains to the (Mohaghegh 2011(Mohaghegh , 2017a(Mohaghegh , 2017bNg et al 2021), the data-driven model is only relevant to the reservoir model being studied. This denotes that it cannot be implemented as the substitute for another reservoir.…”

“…There are also other interesting literatures (Nait Amar et al 2018Navrátil et al 2019;Alakeely and Horne 2020;Ng et al 2021) that discuss and present the use of ML methods in the establishment of proxies of numerical models in petroleum domain, especially for reservoir engineering. Regarding this, there is a riveting insight being provided by Nait Amar et al (2018) about the modeling of proxies, which is the difference between static and dynamic proxy models.…”

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

“…In this paper, we discuss more thoroughly the methodology to construct an SPM, describing in detail the different steps, such as sampling and training the underlying model. In the existing SPM literature [18][19][20][21][22][23], only one technique for each step is tested. For example, only one type of underlying model (ANN) or one type of sampling technique (Latin hypercube sampling) is tested.…”

Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content

“…Oil or natural gas reserves are constantly depleting as a result of continued oil and gas exploitation, and the existing reservoir production levels are substantially decreasing [1][2][3][4][5]. Therefore, Source rock and unconventional reservoirs have increasingly piqued interest [6][7][8][9].…”

“…Several scholars established empirical TOC correlations (summarized in Table 1) based on well logs and where the densities are in g/cm 3 Used data from Devonian shale and predicts TOC in volume percentage. The data were taken from seven wells located in Virginia, West Virginia, Kentucky and Ohio.…”