Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study

TL;DR

This paper introduces a Bayesian neural network-based virtual flow meter that models uncertainty, demonstrating improved robustness and predictive accuracy on diverse oil and gas well data, advancing data-driven flow measurement methods.

Contribution

It presents the first probabilistic VFM using Bayesian neural networks with variational inference, addressing uncertainty and robustness in small data regimes.

Findings

Achieved 4-6% average error on historical data

Achieved 8-13% average error on future data

Variational inference yields more robust predictions

Abstract

Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as computationally cheap evaluation and ease of calibration to new data, have sparked optimism for the development of data-driven virtual flow meters (VFMs). Data-driven VFMs are developed in the small data regime, where it is important to question the uncertainty and robustness of models. The modeling of uncertainty may help to build trust in models, which is a prerequisite for industrial applications. The contribution of this paper is the introduction of a probabilistic VFM based on Bayesian neural networks. Uncertainty in the model and measurements is described, and the paper shows how to perform approximate Bayesian inference using variational inference. The method is studied by modeling on a large and heterogeneous dataset, consisting of 60 wells across five different oil and gas assets. The predictive performance is analyzed on historical and future test data, where an average error of 4-6% and 8-13% is achieved for the 50% best performing models, respectively. Variational inference appears to provide more robust predictions than the reference approach on future data. Prediction performance and uncertainty calibration is explored in detail and discussed in light of four data challenges. The findings motivate the development of alternative strategies to improve the robustness of data-driven VFMs.