Virtual Multiphase Flowmeter Using Deep Convolutional Neural Networks

Abstract

Summary Petroleum wells produce a combination of oil, gas, and water in what is called a multiphase flow. This mixture is transported through flowlines to a tank separator that isolates and quantifies the volume of each fluid. However, this mechanical gravity separation process takes a long time, and the tank is often shared between many other wells in a field, making it difficult to allow an individual online measurement of the extracted fluids. Without this information, operators cannot effectively control production or estimate each well’s depletion rate, leading to losses or reduced profits. This paper aims to propose a low-cost, instantaneous model to perform this measure using artificial intelligence, commonly known as a virtual flowmeter (VFM). The idea behind it is to use data from pressure and temperature sensors already available on every well in addition to the state of the opening control valve to train a deep neural network with a convolutional layer to output each fluid’s volume rate. The proposed method is computationally simpler than recurrent neural networks and provides similar results. However, it still requires data to train the neural network. Adequate free databases of well production with telemetry are hard to find, so this paper proposes using the Schlumberger OLGA multiphase flow simulator software to provide the data, adjusting the simulator with fluid and operational information from actual wells. Tests have shown that the approximation with the proposed methods achieves up to 99.6% accuracy, making it possible to replace an expensive multiphase meter or use it as a redundant digital sensor for fault alerts of possible inaccurate readings.