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Machine Learning Improves Accuracy of Virtual Flowmetering and Back-Allocation

In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells. The main goal was to develop computationally efficient data-driven models to determine multiphase production rates of individual phases (gas and liquid) in wells using existing measured data in fields. The results showed that ANNs were capable of estimating multiphase flow rates accurately in both simulated and field data.

Introduction

Virtual-flowmetering (VFM) methods are categorized in two types generally: physics-based models (hydrodynamical approach) and data-driven models. In the physics-based approach, multiphase flow rates are estimated by simplified hydrodynamical models using sensor data (e.g., pressure and temperature) as input parameters. The second approach is solely dependent on the available data in the field, performing statistical analysis on this data and deriving relations between input features and quantities of interest (in this case, multiphase flow rates). Such an approach requires a sufficient data set to train the models. In the context of data-driven VFM, such a data set could be obtained from periodic test-separator data or well tests.

In this study, two types of ANNs were assessed: feed-forward (multilayer-­perceptron) and recurrent [long short-term memory (LSTM)] to capture temporal dependencies. ANNs were used for real-time gas flowmetering at an individual well level using total production rates measured downstream of the combined production separators.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 192819, “Improving the Accuracy of Virtual Flowmetering and Back-Allocation Through Machine Learning,” by Pejman Shoeibi Omrani, SPE, Iulian Dobrovolschi, and Stefan Belfroid, SPE, TNO, and Peter Kronberger and Esteban Munoz, Wintershall Noordzee, prepared for the 2018 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 12–15 November. The paper has not been peer reviewed.
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Machine Learning Improves Accuracy of Virtual Flowmetering and Back-Allocation

01 March 2019

Volume: 71 | Issue: 3

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