Data-Driven Machine Learning Modeling of Mineral/CO2/Brine Wettability Prediction: Implications for CO2 Geo-Storage

Zeeshan Tariq, Muhammad Ali, Bicheng Yan, Shuyu Sun, Mohammad Khan, Nurudeen Yekeen, Hussein Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


CO2 wettability and the reservoir rock-fluid interfacial interactions are crucial parameters for successful CO2 geological sequestration. This study implemented the feed-forward neural network to model the wettability behavior in a ternary system of rock minerals (quartz and mica), CO2, and brine under different operating conditions. To gain higher accuracy of the machine learning models, a sufficient dataset was utilized that was recorded by conducting a large number of laboratory experiments under a realistic pressure range, 0-25 MPa and the temperatures range, 298-343 K. The mica substrates were used as a proxy for the caprock, and quartz substrates were used a proxy for the reservoir rock. Different graphical exploratory data analysis techniques, such as heatmaps, violin plots, and pairplots were used to analyze the experimental dataset. To improve the generalization capabilities of the machine learning models k-fold cross-validation method, and grid search optimization approaches were implemented. The machine learning models were trained to predict the receding and advancing contact angles of mineral/CO2/brine systems. Both statistical evaluation and graphical analyses were performed to show the reliability and performance of the developed models. The results showed that the implemented ML model accurately predicted the wettability behavior under various operating conditions. The training and testing average absolute percent relative errors (AAPE) and R2 of the FFNN model for mica and quartz were 0.981 and 0.972, respectively. The results confirm the accuracy performance of the ML algorithms. Finally, the investigation of feature importance indicated that pressure had the utmost influence on the contact angles of the minerals/CO2/brine system. The geological conditions profoundly affect rock minerals wetting characteristics, thus CO2 geo-storage capacities. The literature severely lacks advanced information and new methods for characterizing the wettability of mineral/CO2/brine systems at geo-storage conditions. Thus, the ML model's outcomes can be beneficial for precisely predicting the CO2 geo-storage capacities and containment security for the feasibility of large-scale geo-sequestration projects.

Original languageEnglish (US)
Title of host publicationSociety of Petroleum Engineers - Middle East Oil, Gas and Geosciences Show, MEOS 2023
PublisherSociety of Petroleum Engineers (SPE)
ISBN (Electronic)9781613999806
StatePublished - 2023
Event2023 Middle East Oil, Gas and Geosciences Show, MEOS 2023 - Manama, Bahrain
Duration: Feb 19 2023Feb 21 2023

Publication series

NameSPE Middle East Oil and Gas Show and Conference, MEOS, Proceedings


Conference2023 Middle East Oil, Gas and Geosciences Show, MEOS 2023


  • Carbon Geo-Storage
  • Contact Angle/Wettability
  • Machine learning
  • Reservoir rocks

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology


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