Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms

K.A. Pechko; I.S. Senkin; E.V. Belonogov

doi:10.3997/2214-4609.202156025

Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms
Authors K.A. Pechko¹, I.S. Senkin¹, E.V. Belonogov¹
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Affiliations: ¹ OOO «NTC gazpromneft»
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, Data Science in Oil and Gas 2021, Aug 2021, Volume 2021, p.1 - 5
DOI: https://doi.org/10.3997/2214-4609.202156025

Abstract

Summary

Bottom hole pressure prediction is crucial issue in integrated field modeling. This article proposes a new approach to well modeling implementing machine learning algorithms. In this paper bottomhole pressure is analysed as dependent variable on four parameters such as level of wellhead pressure, flow rate, gas factor and water cut. The model is developed using the "Random forest" approach with gradient boosting. The model was tested on synthetic and real data from different wells and fields. The prediction accuracy satisfies company requirements and is more than 90 times faster than traditional empirical correlations.

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2021-08-04

2024-04-19

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References

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Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms

Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202156025

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Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms

Abstract

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