A Machine Learning Approach for Virtual Flow Metering and Forecasting
This addresses the challenge of limited physical measurements for flow rates in oil and gas production, offering a practical tool for operators, though it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of forecasting multiphase flow rates in oil and gas production using an LSTM network, achieving accurate virtual flow metering and forecasting that compared favorably with hydrodynamical modeling in synthetic cases and handled noisy, variable-rate data.
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.