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Record W2086528099 · doi:10.2118/75701-ms

Diagnostic Fracture Injection Test in Coals to Determine Pore Pressure and Permeability

2002· article· en· W2086528099 on OpenAlex
Muthukumarappan Ramurthy, Douglas M. Marjerisson, Scott B. Daves

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSPE Gas Technology Symposium · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsCarbon Engineering (Canada)BP (Canada)
Fundersnot available
KeywordsPermeability (electromagnetism)Petroleum engineeringCoalbed methaneCoalHydraulic fracturingTight gasPore water pressureGeologyGeotechnical engineeringMaterials scienceCoal miningEngineeringChemistryWaste management

Abstract

fetched live from OpenAlex

Abstract Permeability and pore pressure are critical parameters in the evaluation of a coalbed methane (CBM) project. Coal permeability is particularly problematic, as it is highly stress dependent and estimates made from cores generally do not adequately reflect in situ reservoir conditions. Pressure buildup, injection falloff and more often slug tests have been used to determine in situ permeability in coal. However, buildup tests are costly, time consuming, and cannot be applied effectively in underpressured reservoirs; slug tests require an accurate estimate of wellbore storage effects. Similar to buildup tests, injection falloff tests are very time consuming and costly because of the longer shut-in times. Also, if fracture pressure is exceeded during an injection-falloff test, conventional analysis can give erroneous results. This paper presents a more effective method for determining pore pressure and permeability in coals using a diagnostic fracture injection testing technique. A diagnostic fracture injection test (DFIT) is a small-volume, cost-effective, and short-duration test that has been used successfully in tight gas sands in the Piceance and other basins. The test consists of (1) a G-function derivative analysis to identify the leakoff mechanism and closure, (2) a calibrated before-closure analysis using modified Mayerhofer method to determine the permeability, and (3) an after-closure analysis to estimate pore pressure and permeability. The uniqueness in applying this test in coals is that both the before- and after-closure analysis can be utilized where pseudo-radial flow is not dependent upon the fracture half-length. The technique works because the permeability in coals is high enough that after-closure pseudo-linear and pseudo-radial flows are normally observed with an extended shut-in. Once pseudo-radial flow is observed, estimating pore pressure and transmissibility becomes straightforward and provides calibration for the before-closure analysis. Hundreds of diagnostic fracture injection tests have been conducted in all the CBM basins in the rockies and in Canada with remarkably consistent results. Examples are provided from San Juan basin and Canadian coals where diagnostic injection tests have been applied successfully for various operators. DFIT's have been applied successfully in other CBM basins like Sand Wash, Greater Green River, Piceance, and (western) Powder River basin.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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