Sub-surface geospatial intelligence in carbon capture, utilization and storage - A machine learning approach for offshore storage site selection

Aug 5, 2026

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A data-driven machine-learning framework predicts site scores for offshore CO2 storage site screening in Carbon Capture, Utilization and Storage (CCUS). It fuses heterogeneous sub-surface geospatial datasets with expert-aided, expert-weighted criteria, supporting different levels of data availability while covering technical and non-technical factors. The approach aggregates and analyzes geospatial evidence to identify priority geologic CCUS regions while aligning with stringent safety, risk, and environmental guidelines. Benchmarking across XGBoost, Random Forest, Multilayer Extreme Learning Machine, and Deep Neural Network shows DNN delivers the highest predictive accuracy and error performance, enabling scalable, efficient decision support for policymakers and industry.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Keywords and article overview

Machine-learning framework for offshore CCUS site scoring

Data integration and expert-weighted criteria

Model benchmarking and performance evaluation

Introduction

CO2 emissions and climate-change motivation

Shift toward sub-surface geospatial intelligence (SSGI) for CCUS

Background: Sleipner project and global safety assessment

Edith Cowan University Research Online Research outputs 2022 to 2026 10-1-2024 Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection Mehdi Nassabeh Edith Cowan University Zhenjiang You Edith Cowan University Alireza Keshavarz Edith Cowan University Stefan Iglauer Edith Cowan University Follow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026) Part of the Civil and Environmental Engineering Commons [10.1016/j.energy.2024.132086](10.1016/j.energy.2024.132086) Nassabeh, M., You, Z., Keshavarz, A., & Iglauer, S. (2024) . Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection. Energy, 305, 132086. [https://doi.org/10.1016/j.energy.2024.132086](https://doi.org/10.1016/j.energy.2024.132086) [This Journal Article is posted at Research Online.](This Journal Article is posted at Research Online.)[ ](This Journal Article is posted at Research Online.)[https://ro.ecu.edu.au/ecuworks2022-2026/4394](https://ro.ecu.edu.au/ecuworks2022-2026/4394) Energy 305 (2024) 132086 Contents lists available at ScienceDirect Energy journal [homepage:](homepage: www.elsevier.com/locate/energy)[ www.elsevier.com/locate/energy](homepage: www.elsevier.com/locate/energy) | Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection | | | | --- | --- | --- | | *
Mehdi Nassabeh , Zhenjiang You , Alireza Keshavarz , Stefan Iglauer
Centre for Sustainable Energy and Resources, School of Engineering, Edith Cowan University, Joondalup, WA, 6027, Australia | | | | A R T I C L E I N F O
Keywords:
Carbon capture, utilization, and storage (CCUS) Site screening
Site score
Machine learning
Deep neural network (DNN) Cross-validation technique | A B S T R A C T
This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a highresolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expertweighted model to identify suitable geologic CCUS regions. This process adheres to stringent safety, risk control, and environmental guidelines, addressing situations where human analysis may fail to recognize patterns and provide detailed insights in suitable site screening techniques. The primary emphasis of this research is to bridge the gap between scientific inquiry and practical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, and ecosensitivity metrics contribute valuable insights for policymakers and industry leaders. To ensure the accuracy, efficiency, and scalability of the established offshore CO2 storage facilities, the proposed machine learning approach undergoes benchmarking. This comprehensive evaluation includes the utilization of machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of

FAQs

What problem does the study address in CCUS offshore projects?

It addresses the need to accurately screen and prioritize offshore CO2 storage locations by predicting site scores using a data-driven framework.

How does the proposed method combine geospatial information?

It integrates diverse sub-surface geospatial datasets with human-aided, expert-weighted criteria and considers both technical and non-technical factors.

Which machine-learning algorithm performs best for predicting site scores?

The Deep Neural Network (DNN) algorithm shows the most effective performance in site score prediction, with the highest accuracy indicators reported in the study.

Aug 5, 2026

Upload by Maeve