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Record W2624301506 · doi:10.2118/186089-pa

Magnetic-Resonance Imaging of Fines Migration in Berea Sandstone

2017· article· en· W2624301506 on OpenAlexaff
Armin Afrough, Mohammad Sadegh Zamiri, Laura Romero‐Zerón, Bruce J. Balcom

Bibliographic record

VenueSPE Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPermeability (electromagnetism)Phase (matter)GeologyVolumetric flow rateInletPorosityMineralogyMechanicsNuclear magnetic resonanceChemistryGeotechnical engineeringGeomorphologyPhysics

Abstract

fetched live from OpenAlex

Summary Fines migration is a phenomenon of practical importance in the petroleum-production and drilling industry. The movement of clay particles, induced by incompatible aqueous-phase chemistry or high flow rate, obstructs pore throats downstream of the fluid flow, leading to permeability reductions that can be as large as two orders of magnitude. Magnetic-resonance-imaging (MRI) methods derived from the Carr-Purcell-Meiboom-Gill (CPMG) method (Meiboom and Gill 1958) can map T2 distributions in porous rocks, hence showing the spatial variation of the pseudo-pore-size distribution. In this work, the traditional water-shock experiment was used to mobilize clay particles in the aqueous phase flowing in Berea core plugs. Spin-echo single-point imaging (SE-SPI), a phase-encoding MRI method derived from the CPMG method, was used to determine spatially resolved T2 spectra of the samples, and therefore the pseudo-pore-size distributions. The shift in the T2 spectra of the core inlet and outlet showed opposite trends. The pore-size distribution of the inlet and outlet, inferred from T2 distributions, were shifted to larger and smaller values, respectively. Therefore, the average pore size was increased at the inlet of the core and reduced at the outlet of the core. This MRI method provides a new analytical approach to screen reservoirs for potential fines-migration problems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.062
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.318
Teacher spread0.310 · 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 teacher head, 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".

Quick stats

Citations23
Published2017
Admission routes1
Has abstractyes

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