Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty

In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under un...

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Bibliographic Details
Published in:Energies (Basel) 2022-10, Vol.15 (20), p.7484, Article 7484
Main Authors: Moradi, Ali, Tavakolifaradonbe, Javad, Moldestad, Britt M. E.
Format: Article
Language:English
Subjects:
advanced wells
AICV
Algorithms
ANN
Artificial intelligence
Control equipment
Design
Design and construction
Design improvements
Design of experiments
Electronic data processing
FCD
Flow control
Fluids
Gas industry
Neural networks
Oil
Oil fields
Oil recovery
Oil wells
Partial differential equations
Permeability
Petroleum mining
Physics
proxy models
Reservoirs
Simulation
Uncertainty analysis
Viscosity
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Summary:In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well–reservoir models created by the Eclipse® reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulations need to be performed to cover all possible scenarios. In this study, the developed proxy models were applied for uncertainty quantification of oil recovery from advanced wells completed with different types of downhole flow control devices (FCDs). According to the obtained results, compared to other types of well completion design, advanced wells completed with autonomous inflow control valve (AICV) technology have the best performance in limiting the production of unwanted fluids and are able to reduce the associated risk by 91%.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15207484