Abstract
A significant challenge in oil and gas Industry, especially in reservoir engineering and its management context, is related to the capacity of run optimization strategies. It occurs because the current computational time costs are prohibitive and demand too many resources, even for a medium-size simulation. Perhaps the currently scientific solutions, none of them used deep Learning techniques in low-level granularity for predictions, especially the grid-cell level size approach. This presentation proposes, analyzes, and states a feature selection, a model design, and a training strategy with the application of Deep Learning techniques (DNN and CNN), the Design of Experiment, and all statistical evaluation-based metrics and its graphic tools. This defined process intends to work as a solution to create a proxy model for reservoir numerical software simulation.
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@misc{santos2024ROG,
title = {Deep Learning/Proxy Modeling Approaches for Reservoir Simulation and Production Forecasting},
author = {M. D. Santos and R. M. Magalh\~{a}es and G. C. P. Oliveira and T. J. Machado},
editor = {Rio Oil and Gas e. 2024},
year = {2024},
date = {2024-09-24},
abstract = {A significant challenge in oil and gas Industry, especially in reservoir engineering and its management context, is related to the capacity of run optimization strategies. It occurs because the current computational time costs are prohibitive and demand too many resources, even for a medium-size simulation. Perhaps the currently scientific solutions, none of them used deep Learning techniques in low-level granularity for predictions, especially the grid-cell level size approach. This presentation proposes, analyzes, and states a feature selection, a model design, and a training strategy with the application of Deep Learning techniques (DNN and CNN), the Design of Experiment, and all statistical evaluation-based metrics and its graphic tools. This defined process intends to work as a solution to create a proxy model for reservoir numerical software simulation.},
howpublished = {Rio Oil and Gas e. 2024},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}