Enhancing sand production assessment through accurate determination of Young's modulus and Poisson's ratio.

Fahd Saeed Alakbari, Syed Mohammad Mahmood, Mahmoud M Abdelnaby, Haithm Salah Hagar, Funsho Afolabi, Mysara Eissa Mohyaldinn

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Fahd Saeed AlakbariInterdisciplinary Research Center for Hydrogen Technologies and Carbon Management, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia. fahd.akbari@kfupm.edu.sa.

Syed Mohammad MahmoodPetroleum Engineering Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia.

Mahmoud M AbdelnabyInterdisciplinary Research Center for Hydrogen Technologies and Carbon Management, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.

Haithm Salah HagarDepartment of Chemical and Petroleum Engineering, The University of Kansas, Lawrence, KS, 66045, USA.

Funsho AfolabiDepartment of Chemical Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.

Mysara Eissa MohyaldinnPetroleum Engineering Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia. mysara.eissa@utp.edu.my.

Funding

Yayasan UTP 015LC0-451

6 · The paper itself

Abstract

Sand production is a critical concern in the petroleum industry, often leading to costly equipment damage, production downtime, and safety risks. Accurate prediction of sand-prone zones is essential for proactive sand management and optimized well design. Although several empirical and machine learning-based models exist to estimate static Young's modulus (Es) and static Poisson's ratio (νs)-key inputs for sand production prediction methods such as the Sand Production Index (B), shear modulus to bulk compressibility ratio (G/Cb), and Schlumberger Sand Index (S/I)-their performance varies widely, and their reliability has not been studied. This study addresses that gap by conducting a comparative evaluation of multiple estimation models for Es and ν

Indexed as

Poisson’s ratioSand controlSand predictionSand productionYoung’s modulus

Identifiers

PMID41620480

DOI10.1038/s41598-026-36761-2

PMCPMC12917246

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LicenceCC BY-NC-ND

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