Improving the Accuracy of Virtual Flow Metering and Back-Allocation through Machine Learning

Abstract

In this study we have investigated a fully data-driven approach (artificial neural networks) for real-time back-allocation and virtual flow metering in oil and gas production wells. The main goal of this study is to develop computationally efficient data-driven models to determine the multiphase production rates of individual phases (gas and liquid) in wells using existing measured data in fields. The developed approach was tested on simulated and field data from several gas wells. Two different type of artificial neural networks (ANNs) were tested on simulated and field data to assess the accuracy of estimations for steady-state, transients and dynamics in productions due to cyclic operation (shut-ins and restart). The results showed that ANN was capable of accurately estimate the multiphase flow rates in both simulated and field data. The accuracy of the production rates estimation depends on the type of neural networks employed, production behavior (steady-state or transients) and uncertainties in data.