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 The paper has not been peer reviewed.
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.
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.
The feed-forward ANN is a suitable method for modeling the relations between a set of features and output parameters by functional mapping of the features in the input layer to the outputs. Fig. 1 shows a schematic of a feed-forward ANN. The relation of the input to the output parameters is found by calibrating the weights and biases in the hidden (middle) layers. Depending on the complexity of the input/output relations, the number of hidden layers, and neurons therein, could be varied. The disadvantage of feed-forward networks is their inability to identify temporal trends in the data set.
Recurrent neural networks (RNNs) are suggested for prediction of systems in which states are changing continuously before reaching an equilibrium, or when the time-dependency of states is crucial for predicting these states. A common disadvantage of some RNNs is their inability to capture long-term dependencies in the data. Use of an LSTM network is suggested for predictions involving systems in which both short- and long-term dependencies are present. An LSTM network is composed of sever-al units of RNN, with each unit composed of a cell with an input, output, and forget gate.