Diagnostic Fracture Injection Test in Coals to Determine Pore Pressure and Permeability
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".