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Record W4415878694 · doi:10.2118/229643-ms

The Role of Salt Dome Characterization in Developing Salt Caverns for Underground Hydrocarbon Storage: A First Case Study in UAE

2025· article· W4415878694 on OpenAlexaboutno aff
Ahmed Farouk, Maniesh Singh, M. N. A. Alblooshi, Mohammed Saad, J. Ahmed, Tamer Koslan, Hedayat Hashmi, Bashar Adel Abu Snaineh, Jwan AlGayyali, Wilthon Gilles

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCoringSalt domeSalt (chemistry)DrillingCore sampleCharacterization (materials science)FaciesHydrocarbon exploration

Abstract

fetched live from OpenAlex

Abstract Salt dome characterization is essential for underground hydrocarbon storage, as it aids in identifying suitable zones for leaching, ensuring cavern integrity, and optimizing storage capacity. The objective of this study is to implement a comprehensive characterization of salt formations to enhance the understanding of their behavior and suitability for hydrocarbon storage. Historically, the first salt caverns for hydrocarbon storage were developed in Canada in the early 1940s, followed by numerous projects in the USA and recently expansion into new areas globally. This paper presents the first case study in the UAE focusing on salt caverns intended for oil storage. To achieve the project's objectives, two caverns were drilled, and fully oriented cores were collected, encompassing the entire salt section, including insoluble zones. Conventional mud logging, along with a comprehensive suite of logs, was performed to ensure proper salt dome characterization and optimize the selection of leaching intervals for cavern creation. Routine core analysis and extensive geomechanical core assessments were conducted to understand the behavior of the salt rock, complemented by detailed core descriptions to define various salt facies. Advanced technologies, including walk-away seismic measurements, far-field acoustic measurements, and electromagnetic propagation wave technologies, were employed to delineate the extension of the salt dome. Preliminary classifications of salt facies were conducted during coring using core chip descriptions and drilling cuttings from mud logging data. These salt facies were refined through detailed core descriptions and integration with additional analyses, such as CT scan, thin sections, scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray fluorescence (XRF). An integrated salt study utilizing Quad-Compo data, imaging logs, spectroscopy, mini-fracture tests, and core analyses led to the identification of numerous salt bodies interbedded with various insoluble formations. These salt bodies were ranked to optimize leaching intervals for cavern creation based on criteria such as salt purity, halite percentage, thickness, evidence of fractures, lateral extension, and measured porosity and permeability from routine core analysis and geo-mechanical studies. The lateral extension of the salt dome was further assessed using advanced tools, such as walk-away seismic surveys, deep shear acoustic waves, and three-dimensional borehole electromagnetic waves, which confirmed the absence of faults or fractures near the selected leaching intervals. Ultimately, this comprehensive dataset supports critical cavern design parameters, including height, width, leaching intervals, and storage capacity calculations. This integrated study, encompassing extensive data gathering and analysis, will serve as a benchmark for upcoming salt cavern projects. Notably, this endeavor is considered a pilot project in the region for salt dome characterizations and provides insights into the optimal development of salt caverns for underground hydrocarbon storage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.230
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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