Abstract
A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. The predicted flow rates may aid the daily control and optimization of a petroleum asset. Gray-box modeling is an approach that combines mechanistic and data-driven modeling. The objective is to create a computationally feasible VFM for use in real-time applications, with high prediction accuracy and scientifically consistent behavior. This article investigates five different gray-box model types in an industrial case study using real, historical production data from 10 petroleum wells, spanning at most four years of production. The results are diverse with an oil flow rate prediction error in the range of 1.8%–40.6%. Further, the study casts light upon the nontrivial task of balancing learning from both physics and data. Therefore, providing general recommendations towards the suitability of different hybrid models is challenging. Nevertheless, the results are promising and indicate that gray-box VFMs can reduce the prediction error of a mechanistic VFM while remaining scientifically consistent. The findings motivate further experimentation with gray-box VFM models and suggest several future research directions to improve upon the performance and scientific consistency.
Highlights
To optimally control a petroleum asset and maximize the recovery of oil and gas, it is necessary to have an adequate understanding of the behavior of the petroleum production system
This article contributes towards the development of gray-box virtual flow meters in the petroleum industry
The focus has been on white-to-gray box models where a mechanistic model is used as a baseline and data-driven elements inserted to increase model flexibility
Summary
To optimally control a petroleum asset and maximize the recovery of oil and gas, it is necessary to have an adequate understanding of the behavior of the petroleum production system. Some examples exist in the literature (Xu et al, 2011; Al-Rawahi et al, 2012; Kanin et al, 2019; Bikmukhametov and Jaschke, 2020a) Most of these studied different gray-box approaches on synthetic data, either as an experimental set up in a test rig (Xu et al, 2011) or a multiphase flow loop (Al-Rawahi et al, 2012), or using lab data available online (Kanin et al, 2019). The results in this research are in respect to the VFM application, and the generalizability to other application areas is not considered
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