Digital Twins and Financial ROI: Assessing Tech Investments in Refinery Operations

The global oil and gas industry is experiencing unprecedented digital transformation, with digital twin technology emerging as a critical catalyst for operational excellence and financial returns. This research presents a comprehensive analysis of digital twin implementation in refinery operations, examining the financial return on investment (ROI) and operational performance implications. Through analysis of 150+ refinery implementations across four operational scales, this study demonstrates that digital twin technology delivers average ROI timelines of 12-36 months, with maintenance cost reductions ranging from 25-55% and operational efficiency improvements of 15-42%. Key findings reveal that mega-scale refineries (500K bpd) achieve the most favorable economics, with payback periods of 1.4 years and net present values exceeding $132 million. The research establishes quantitative frameworks for assessing digital twin investments, providing definitive metrics for technology adoption decisions. Implementation challenges include data integration complexity, cybersecurity concerns, and change management requirements, yet the compelling financial benefits support widespread industry adoption. This analysis provides strategic guidance for refinery operators considering digital twin investments and contributes to the growing body of knowledge on industrial digitalization ROI assessment.

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-09-08
DOI
https://doi.org/10.52783/jisem.v10i59s.12955
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital Twins and Financial ROI: Assessing Tech Investments in Refinery Operations

Journal of Information Systems Engineering & Management
Geochemistry and Geologic Mapping
article

Digital Twins and Financial ROI: Assessing Tech Investments in Refinery Operations

article en

Abstract

The global oil and gas industry is experiencing unprecedented digital transformation, with digital twin technology emerging as a critical catalyst for operational excellence and financial returns. This research presents a comprehensive analysis of digital twin implementation in refinery operations, examining the financial return on investment (ROI) and operational performance implications. Through analysis of 150+ refinery implementations across four operational scales, this study demonstrates that digital twin technology delivers average ROI timelines of 12-36 months, with maintenance cost reductions ranging from 25-55% and operational efficiency improvements of 15-42%. Key findings reveal that mega-scale refineries (500K bpd) achieve the most favorable economics, with payback periods of 1.4 years and net present values exceeding $132 million. The research establishes quantitative frameworks for assessing digital twin investments, providing definitive metrics for technology adoption decisions. Implementation challenges include data integration complexity, cybersecurity concerns, and change management requirements, yet the compelling financial benefits support widespread industry adoption. This analysis provides strategic guidance for refinery operators considering digital twin investments and contributes to the growing body of knowledge on industrial digitalization ROI assessment.

Journal of Information Systems Engineering & ManagementVol. 10(59s)
Industry, innovation and infrastructure
Openalex Percentile: Top 31%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.