Production and Video Logging in Horizontal Low-Permeability Gas Wells
Bibliographic record
Abstract
Abstract The intention of this paper is to discuss and recommend effective production logging techniques for low rate horizontal gas wells. Between June 2005 and September 2006, a total of eight wells were evaluated using either traditional production logging (PL) tools or downhole video logging tools. Pressure, temperature, nuclear fluid density, single capacitance, capacitance array, fullbore spinner, caliper, and gamma-ray data were retrieved by production logging methods. Video logging collected thousands of wellbore images as well as pressure and temperature information. A significant issue in logging horizontal wells is the method of delivering tools into the well. Tubing diameter, open hole diameter, horizontal length, horizontal trajectory and the desired results all influence the selection of conveyance method. The methods used in this study were CoRod, coiled tubing (CT) and wireline tractor. The production logging data indicated liquid accumulations however the different logging tools showed varying degrees of detail. As an example, the capacitance array tool tended to show liquid with gas pockets while the radioactive density tool and spinner tool indicated 100% liquid. Video logging data provided definitive images of gas bubbles and slugs flowing through liquid. The objectives of a logging program will dictate either a qualitative or quantitative evaluation. Video logging is an excellent technique to use as a general overview when initially investigating openhole sections. It can provide insight on complex production problems that never would be suspected or detected using traditional production logging methods.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".