4.6 Article

Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

Journal

PETROLEUM SCIENCE
Volume 20, Issue 5, Pages 2951-2966

Publisher

KEAI PUBLISHING LTD
DOI: 10.1016/j.petsci.2023.04.001

Keywords

Production optimization; Random forest; The Bayesian algorithm; Ensemble learning; Particle swarm optimization

Ask authors/readers for more resources

This study proposes an optimization framework based on proxy models, which combines Bayesian random forest and particle swarm optimization algorithm. It improves prediction accuracy, saves time, reduces gas-oil ratio, and increases oil production in carbonate reservoirs.
Production optimization is of significance for carbonate reservoirs, directly affecting the sustainability and profitability of reservoir development. Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization. We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest (BRF) with the particle swarm optimization algorithm (PSO). The BRF method is implemented to construct a proxy model of the injection-production system that can accurately predict the dynamic parameters of producers based on injection data and production measures. With the help of proxy model, PSO is applied to search the optimal injection pattern integrating Pareto front analysis. After experimental testing, the proxy model not only boasts higher prediction accuracy compared to deep learning, but it also requires 8 times less time for training. In addition, the injection mode adjusted by the PSO algorithm can effectively reduce the gas-oil ratio and increase the oil production by more than 10% for carbonate reservoirs. The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry, which can provide more options for the project decision-makers to balance the oil production and the gas-oil ratio considering physical and operational constraints. (c) 2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available