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Record W2142001212 · doi:10.2118/108971-pa

An Early-Time Model for Drawdown Testing of a Hydrate-Capped Gas Reservoir

2009· article· en· W2142001212 on OpenAlexafffund
Shahab Gerami, Mehran Pooladi‐Darvish

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsClathrate hydrateHydrateDrawdown (hydrology)Petroleum engineeringReservoir engineeringNatural gasGeologyMineralogyGeotechnical engineeringChemistryAquiferPetroleumGroundwater

Abstract

fetched live from OpenAlex

Summary Development of natural gas hydrates as an energy resource has gained significant interest during the past decade. Hydrate reservoirs may be found in different geologic settings including deep ocean sediments and arctic areas. Some reservoirs include a free-gas zone beneath the hydrate and such a situation is referred to as a hydrate-capped gas reservoir. Gas production from such a reservoir could result in pressure reduction in the hydrate cap and endothermic decomposition of hydrates. Well testing in conventional reservoirs is used for estimation of reservoir and near-wellbore properties. Drawdown testing in a hydrate-capped gas reservoir needs to account for the effect of gas from decomposing hydrates. This paper presents a 2D (r,z) mathematical model for a constant-rate drawdown test performed in a well completed in the free-gas zone of a hydrate-capped gas reservoir during the earlytime production. Using energy and material balance equations, the effect of endothermic hydrate decomposition appears as an increased compressibility in the resulting governing equation. The solution for the dimensionless wellbore pressure is derived using Laplace and finite Fourier cosine transforms. The solution to the analytical model was compared with a numerical hydrate reservoir simulator across some range of hydrate reservoir parameters. The use of this solution for determination of reservoir properties is demonstrated using a synthetic example. Furthermore, the solution may be used to quantify the contribution of hydrate decomposition on production performance.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.280
Teacher spread0.243 · 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 designSimulation or modeling
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

Citations15
Published2009
Admission routes2
Has abstractyes

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