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Record W2084042346 · doi:10.2118/114778-ms

Predicting Sorption-Induced Strain and Permeability Increase with Depletion for CBM Reservoirs

2008· article· en· W2084042346 on OpenAlexaff
Christopher R. Clarkson, Zhejun Pan, I. D. Palmer, Satya Harpalani

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsSorptionCoalbed methanePermeability (electromagnetism)ShrinkageMaterials scienceAdsorptionPetroleum engineeringCoalThermodynamicsGeotechnical engineeringChemistryGeologyComposite materialCoal miningPhysics

Abstract

fetched live from OpenAlex

Abstract It is well known that absolute permeability changes occur in coalbed methane (CBM) reservoirs during primary depletion or enhanced recovery/CO2 sequestration operations. Sorption-induced strain in CBM reservoirs, also known as matrix-shrinkage or -swelling, may dominate permeability changes at low pressures, as is the case for CBM wells undergoing primary depletion in the Fruitland Coal fairway of the San Juan Basin. Several analytical models have been developed to predict changes in coal permeability as a function of stress and sorption. Most models, however, utilize an empirical method for estimating sorption-induced strain. Recently, a theoretical model for sorption-induced strain was developed and applied to single-component adsorption/strain experimental data. The new model was developed from basic thermodynamic principles and is more predictive than the empirically-based approaches. In this paper, the theoretical model is expanded to incorporate multi-component adsorption models that are more rigorous, and sometimes more accurate, than the commonly-applied Extended Langmuir equation. This improves predictions of multi-component gas sorption-induced strain, as demonstrated by comparison to experimental data. The new sorption-induced strain model is then used to calculate the sorption-strain component of the popular Palmer and Mansoori equation, which in turn can be used to model permeability changes during both primary (single or multi-component gas) and enhanced recovery operations. Finally, the coupled sorption-strain/permeability model, incorporated into an analytical simulator, is used to predict and match permeability growth in a producing CBM well in the Fruitland Coal fairway, which has a binary (CH4+CO2) sorbed/produced gas composition. Matches to field-derived permeability growth using the new model are accurate but non-unique, due to lack of available data, particularly rock mechanical properties. Given the availability of rock mechanics and adsorption isotherm data, the rigorous thermodynamic basis of the new model should allow for more accurate predictions of coalbed permeability changes, but further testing is required.

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 score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.032
GPT teacher head0.236
Teacher spread0.204 · 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

Citations42
Published2008
Admission routes1
Has abstractyes

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