Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry

TL;DR

This paper proposes a comprehensive uncertainty quantification protocol for machine learning and mechanistic models in the natural gas industry, aiming to improve decision-making by providing credible bounds of prediction confidence.

Contribution

It introduces a structured workflow for uncertainty assessment, identifying key sources, applicable propagation methods, and estimators, with practical application to industry-relevant test cases.

Findings

Protocol effectively estimates uncertainties in gas industry models

Application to case studies demonstrates improved confidence bounds

Guidelines facilitate wider adoption of uncertainty quantification

Abstract

Simulations using machine learning (ML) models and mechanistic models are often run to inform decision-making processes. Uncertainty estimates of simulation results are critical to the decision-making process because simulation results of specific scenarios may have wide, but unspecified, confidence bounds that may impact subsequent analyses and decisions. The objective of this work is to develop a protocol to assess uncertainties in predictions of machine learning and mechanistic simulation models. The protocol will outline an uncertainty quantification workflow that may be used to establish credible bounds of predictability on computed quantities of interest and to assess model sufficiency. The protocol identifies key sources of uncertainties in machine learning and mechanistic modeling, defines applicable methods of uncertainty propagation for these sources, and includes statistically rational estimators for output uncertainties. The work applies the protocol to test cases relevant to the gas distribution industry and presents learnings from its application. The paper concludes with a brief discussion outlining a pathway to the wider adoption of uncertainty quantification within the industry