Understanding the Effects of Leakoff Tests on Wellbore Strength
Bibliographic record
Abstract
Summary Leakoff tests (LOTs) are performed to test the strength or pressure containment of the shoe after a cement job to help ensure that the new hole has been securely isolated from what has been cased off. A successful LOT can also be used to calibrate the least principal stress (many times, in the case of a vertical well, the minimum horizontal stress), or for geomechanics modeling. This will require initiating a fracture at the wellbore. Because of the near-wellbore stress concentration, for the purpose of geomechanics calibration, it is preferred to take the leakoff to the far-field stress region. To perform this extended LOT (XLOT), a relatively long fracture has to be created. Though an XLOT is needed for these reasons, some engineers tend to refrain from performing this test for fear that the test may damage the wellbore and consequently cause drilling problems. This paper addresses this issue by investigating the effect of wellbore damage on wellbore "strength" or pressure containment. Various issues are discussed to help engineers determine when it may or may not be a concern. This should give practicing engineers the necessary insight into this complex rock-mechanics issue. The discussions are supported with results from analytical and numerical simulations based on rock-mechanics principles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".