Using Wireline Formation Evaluation Tools To Characterize Coalbed Methane Formations
Bibliographic record
Abstract
Abstract Wireline formation evaluation tools have been used for over 50 years in conventional formations to acquire formation pressures, permeability, fluid samples and many other reservoir characteristics. These same wireline tools have now been used successfully to characterize coal bed methane (CBM) formations in Alberta, Canada. Field case studies indicated that conventional pressure transient analysis can be used to interpret CBM wireline formation pressure draw down and build up transients. Downhole optical tools were used during pump out to predict formation fluids in place and determine in situ critical desorption pressures. This method of formation characterization offers a potentially superior and more complete method of acquiring CBM formation properties than more traditional injection fall off tests. Ambiguities caused by fall off testing can be caused by inflation of coal cleats and fractures, multi-phase permeability and well bore storage. Formation pressures, permeabilities, in situ critical desorption pressure and formation fluid type are important reservoir properties in CBM plays as in any hydrocarbon bearing formation. Traditional methods of injection fall off data acquisition may be cost effective but difficulties in data quality and interpretation may not justify the initial cost savings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".