Machine-learning based system for virtual flow metering

Abstract

Various aspects described herein relate to a system that utilized deep learning and neural networks to estimate/predict an amount of natural resource production in a well given a set of parameters indicative of physical changes to the well. In one aspect, a virtual flow meter includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to receive one or more input parameters indicative of physical changes to at least one well; apply the one or more input parameters to a trained neural network architecture; and determine one or more outputs of the trained neural network architecture, the one or more outputs corresponding to predicted fluid output of the at least one well.