Reduction of Wellbore Effects on Gas Inflow Evaluation Under Underbalanced Conditions
Bibliographic record
Abstract
Summary Wellbore storage effects have been identified to significantly smear the accuracy of evaluating reservoir productivity through the fluid outflow rate from the annulus during underbalanced drilling. Such effects have continuously introduced considerable errors in characterizing the reservoir during underbalanced drilling. Conceptually, because of the ready volume-changing ability of the gas, wellbore storage becomes a determining factor during underbalanced drilling of a gas reservoir. Wellbore storage could either cause decrease (unloading effects) or increase (loading effects) in the annular gas density, depending on the choke opening procedures. Correspondingly, annular fluid outflow rate is considerably affected. Because it is practically difficult to deduct the fluid-flow rate attributable to the wellbore storage from the total fluid outflow rate, reducing the influence of wellbore effects on the evaluation of gas-reservoir productivity is presented in this study. Volumetric production analysis at the wellbore-sand face is introduced through a mathematical modeling of inflow of gas bubbles into the wellbore. This mathematical modeling utilizes forces such as the viscous force, drilling fluid ejecting forces from the bit nozzles, buoyancy, interfacial tension, and gas-reservoir forces for its analyses. Some analytical results that are overshadowed by wellbore storage are presented and supported by extensive experimental studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".