Systematic Build-Up Pressure Analysis Advances Accurate Fingerprinting of Formation Breathing in Gas Wells
Bibliographic record
Abstract
Abstract Differentiating wellbore breathing from real influxes in Alberta's Deep Basin has been problematic in the past as they both result in similar surface parameters. An incorrect interpretation of formation breathing may lead to significant non-productive time (NPT) as secondary well control operations from an influx can take days. On the contrary, a false negative will force drillers to perform secondary well control procedures that may lead to loss of circulation if excessive and unnecessary pressure is exerted on the formation. MPD allows for a systematic approach to identify wellbore breathing more accurately in gas wells. The process involves a series of consecutive pressure build-up and flowback tests with close real-time monitoring to identify a breathing formation that is returning fluid to surface as microfractures close. This paper describes the protocol designed for distinguishing wellbore breathing and illustrates how several drilling parameter trends were interpreted to correctly identify wellbore breathing characteristics and differentiate them from a migrating gas influx. Testing the procedure on multiple wells resulted in 70% operational time savings from reduction in post mud rollover delays on breathing wellbores. This paper shows that the methodology utilized provides consistent and effective results using the MPD techniques, eliminates the ambiguity of wellbore breathing versus actual influxes, and shows the potential application in more areas that are prone to this problem, reducing uncertainty, NPT, and total drilling time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".