A Machine Learning Approach for Virtual Flow Metering and Forecasting

TL;DR

This paper presents a machine learning approach using LSTM networks to accurately estimate and forecast multiphase flow rates in oil and gas production, outperforming traditional hydrodynamical models especially in noisy and variable conditions.

Contribution

The study demonstrates that LSTM networks can serve as effective virtual flow meters and forecasters for multiphase flow rates, with superior performance in synthetic and real-world scenarios.

Findings

LSTM accurately estimates current flow rates.

LSTM forecasts future flow rates effectively.

Model performs well with noisy and changing data.

Abstract

We are concerned with robust and accurate forecasting of multiphase flow rates in wells and pipelines during oil and gas production. In practice, the possibility to physically measure the rates is often limited; besides, it is desirable to estimate future values of multiphase rates based on the previous behavior of the system. In this work, we demonstrate that a Long Short-Term Memory (LSTM) recurrent artificial network is able not only to accurately estimate the multiphase rates at current time (i.e., act as a virtual flow meter), but also to forecast the rates for a sequence of future time instants. For a synthetic severe slugging case, LSTM forecasts compare favorably with the results of hydrodynamical modeling. LSTM results for a realistic noizy dataset of a variable rate well test show that the model can also successfully forecast multiphase rates for a system with changing flow patterns.