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Record W2053917066 · doi:10.2118/100451-ms

Effect of Fracture Compressibility on Gas-in-Place Calculations of Stress-Sensitive Naturally Fractured Reservoirs

2006· article· en· W2053917066 on OpenAlexaff
Roberto Aguilera

Bibliographic record

VenueSPE Gas Technology Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompressibilityFracture (geology)Volume (thermodynamics)Stress (linguistics)Compressibility factorMatrix (chemical analysis)MechanicsGeotechnical engineeringPetroleum engineeringGeologyThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract The tank material balance (MB) equation for gas reservoirs has been written taking into account the effective compressibility of matrix and fractures. The method has direct application on stress-sensitive naturally fractured reservoirs (nfr's). Under some conditions ignoring the effect of fracture compressibility (cf) can lead to over-estimating the volume of original gas in place using a cross-plot of p/z vs. Gp. The equation presented in this paper has been developed to overcome this weakness. The use of this MB is illustrated with an example. It is concluded that fracture compressibility can play an important role in the calculation of gas in place in naturally fractured reservoirs. The subject matter is significant because historically formation and water compressibilities have been neglected when carrying out MB calculations of conventional gas reservoir. This assumes that these compressibilities are negligible compared to gas. The assumption implies that the reservoir strata are static. When water influx is ignored, the assumption leads to a straight line in a cross-plot of p/z vs. cumulative gas production (Gp). However, this study shows that in those instances where fracture compressibility is large, the assumptions can lead to significant error.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.002
GPT teacher head0.218
Teacher spread0.216 · 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.

Study designBench or experimental
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

Citations4
Published2006
Admission routes1
Has abstractyes

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