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Related Experiment Video

Updated: Jun 16, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Benchmarking water saturation models for the Mishrif formation using dean-stark data.

Rahman Kareem Alzamili^1,2, Hadi Mahdavi Basir³, Ali Kadkhodaie⁴

¹Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, Tehran, Iran.

Scientific Reports

|June 14, 2026

Summary

This study benchmarks 83 water saturation (Sw) models for the Mishrif Formation, finding the Borai Dual-Water Linear model superior among Archie-based methods and Random Forest for data-driven approaches in shaly carbonate reservoirs.

Keywords:

Archie‑derived models Carbonate reservoir Dean–stark Mishrif formation Neural networks Water saturation

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Last Updated: Jun 16, 2026

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Published on: June 8, 2015

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Area of Science:

Petroleum Geoscience
Reservoir Engineering
Machine Learning Applications in Earth Science

Background:

Accurate water saturation (Sw) estimation is critical for hydrocarbon reserve calculations and reservoir characterization.
Shaly carbonate formations present unique challenges for traditional Sw estimation methods.
A comprehensive comparison of diverse Sw models is needed for optimizing reservoir analysis.

Purpose of the Study:

To systematically benchmark 83 water saturation (Sw) models for the Mishrif Formation in southern Iraq.
To compare the performance of traditional Archie-based models against advanced data-driven machine learning approaches.
To establish a robust model selection methodology for shaly carbonate reservoirs.

Main Methods:

Benchmarking 64 Archie-based formulations (Indonesian, Dual Water, Waxman-Smits, Simandoux, Juhasz) and 19 data-driven models (Random Forest, LGBM, SOM).
Integration of well-log data with 131 depth-matched core samples from three wells.
Quantitative performance evaluation using MSE, CCC, R, and R2 metrics, with dual-ranking algorithms for robust selection.

Main Results:

The Borai Dual-Water Linear model demonstrated superior performance among Archie-based models.
The Random Forest model achieved the highest rank among data-driven approaches.
Machine learning models consistently outperformed Archie-based models in this shaly carbonate reservoir, with acceptable uncertainty for volumetric applications (|error|≤0.20).

Conclusions:

The proposed benchmarking workflow effectively reduces model selection ambiguity.
Advanced machine learning models offer superior accuracy for water saturation estimation in shaly carbonate reservoirs.
The methodology is transferable to similar geological settings globally, enhancing reservoir characterization practices.