A Machine Learning Approach for Virtual Flow Metering and Forecasting

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

Impact of Gas Liberation Effects on the Performance of Low Permeability Reservoirs

Production from the North Sea reservoirs often results in a pressure decrease below the bubble point. The gas is liberated from oil, in the form of bubbles or as a continuous flowing phase. In such cases, the two phases, gas and oil, flow in the reservoir simultaneously, and the flow is governed by the values of relative permeabilities. Traditional core flooding in low permeability rocks is challenging, therefore we use a novel experimental approach to determine the oil relative permeabilities below the critical gas saturation. A mathematical model has been created to reconstruct both the gas and the oil relative permeabilities for the whole saturation range. Laboratory observations have shown that in low-permeable rocks the relative permeabilities may strongly decrease, even when the amount of the liberated gas is small. The goal of this work is to verify, on a specific example, whether the designed model for the relative permeabilities may explain the observed production behavior for a low-permeable chalk reservoir in the North Sea. We perform a sensitivity study using the parameters of relative permeabilities and analyze the corresponding differences in well productivities. A reasonably good match of <10% can be obtained to the historical well production data. A few cases where the match was not satisfactory (14% to 65%) are also analyzed, and the difference is attributed to the imprecise fluid model. The developed experimental and modeling methodology may be applied to other reservoirs developed by the solution gas drive mechanism