Application of Machine Learning to Accelerate Gas Condensate Reservoir Simulation

Anna Samnioti, Vassiliki Anastasiadou and Vassilis Gaganis
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Anna Samnioti: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece
Vassiliki Anastasiadou: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece
Vassilis Gaganis: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece

Clean Technol., 2022, vol. 4, issue 1, 1-21

Abstract: According to the roadmap toward clean energy, natural gas has been pronounced as the perfect transition fuel. Unlike usual dry gas reservoirs, gas condensates yield liquid which remains trapped in reservoir pores due to high capillarity, leading to the loss of an economically valuable product. To compensate, the gas produced on the surface is stripped from its heavy components and reinjected back to the reservoir as dry gas thus causing revaporization of the trapped condensate. To optimize this gas recycling process compositional reservoir simulation is utilized, which, however, takes very long to complete due to the complexity of the governing differential equations implicated. The calculations determining the prevailing k-values at every grid block and at each time step account for a great part of total CPU time. In this work machine learning (ML) is employed to accelerate thermodynamic calculations by providing the prevailing k-values in a tiny fraction of the time required by conventional methods. Regression tools such as artificial neural networks (ANNs) are trained against k-values that have been obtained beforehand by running sample simulations on small domains. Subsequently, the trained regression tools are embedded in the simulators acting thus as proxy models. The prediction error achieved is shown to be negligible for the needs of a real-world gas condensate reservoir simulation. The CPU time gain is at least one order of magnitude, thus rendering the proposed approach as yet another successful step toward the implementation of ML in the clean energy field.

Keywords: transition fuel; natural gas recycling; reservoir simulation; regression; machine learning (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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