Estimation of Long-term Power Demand of Oil and Gas Installations using Hybrid Models

A methodology to forecast power demand of oil and gas installations which uses publicly production available data, parametric models and data-driven Gaussian regression methods is presented. The methodology also captures the expected fuel gas consumption and energy ratio. The proposed methodology is...

Full description

Saved in:
Bibliographic Details
Published inComputer Aided Chemical Engineering Vol. 53; pp. 2935 - 2940
Main Authors Andersson, Leif Erik, Reyes-Lúa, Adriana, Schümann, Heiner, Knudsen, Brage Rugstad
Format Book Chapter
LanguageEnglish
Published 2024
Subjects
Forecast
Gaussian Process Regression
Oil and Gas
Power demand
Gaussian Process Regression
Power demand
Oil and Gas
Forecast
Online AccessGet full text

Cover

More Information
Summary:A methodology to forecast power demand of oil and gas installations which uses publicly production available data, parametric models and data-driven Gaussian regression methods is presented. The methodology also captures the expected fuel gas consumption and energy ratio. The proposed methodology is tested on the Brage field on the Norwegian Continental Shelf. It is shown that the general oil and water production behaviour as well as fuel gas consumption trends can be predicted. However, the forecast inherits a significant uncertainty due to the publicly available dataset lacking metadata and a complete description of the energy sinks.
ISBN:9780443288241
0443288240
ISSN:1570-7946
DOI:10.1016/B978-0-443-28824-1.50490-7