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Record W2922516143 · doi:10.2118/194969-ms

Fundamental Behaviors and Borehole Deformation on Wellbore Stability and Sand Production in Conventional and Hydrates-Bearing Gas Reservoirs

2019· article· en· W2922516143 on OpenAlexaff
Yarlong Wang

Bibliographic record

VenueSPE Middle East Oil and Gas Show and Conference · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsGeomechanicsBoreholeGeotechnical engineeringGeologyPetroleum engineeringClathrate hydratePermeability (electromagnetism)Effective stressMechanicsHydrateChemistry

Abstract

fetched live from OpenAlex

Abstract A general formulation for a coupled Thermal-Hydraulic-Mechanical with hydrate Dissociation (THMD) system is developed and applied to sand prediction for conventional gas and gas hydrate bearing sediments (GHBS). Two-phase fluid and conductive heat flow are coupled to an elastoplastic geomechanics model. Series of solutions for simplified models are presented. Fundamental geomechanics behaviors before and after plastic yielding, sanding, and gas hydrate dissociation are defined, discussed, and simulated differently and sanding onset for both conventional gas formations and GHBS are defined by an effective plastic strain (EPS) criterion. The accuracy and reliability of the proposed conventional model are verified by comparing the model prediction with the results of hollow cylinder tests on two different types of sandstone. The advantages of using the EPS over stress-based criteria as an indicator for onset of borehole collapse and sand production are discussed. Introducing a moving gas hydrate dissociation zone (front), the fundamental geomechanics behaviour and elastoplastic deformation of the skeleton formation are highlighted. The effects on sand prediction due to the characteristics of non-linear plastic yielding criteria and gas flow in porous media are also emphasized.

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 score0.048
Threshold uncertainty score0.525

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 head0.026
GPT teacher head0.213
Teacher spread0.187 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

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