Killing of a Gas Well: Successful Implementation of Innovative Approaches in a Middle-Eastern Carbonate Field—A Field Case
Bibliographic record
Abstract
Abstract A casing collapse occurred in a gas producing well with about 2.5 million cubic meters per day gas flow rate at a depth of 216 ft due to tectonic movements. As a result, the well blew out and different serious procedures were put into play to kill the well (Figure 1). This paper aims to review the practical and innovative approach that was used to secure and extinguish the well. Figure 1Gas Seepages in Kangan Field Initially attempts to kill the well included pumping water and or cement into the well to kill and secure it but it was not successful. Two directional relief wells, Kanagan-23A and Kanagan-23B were drilled to secure the well, the latter one was successful. The 23B well was drilled down to total depth of 2500m and entered into drainage area of Kangan-23 gas well. Different directional surveys were tried according to geophysical and geological data for entering the target area. The well relief and intersection was done by combining the new wellpath with the old well and injecting different pills and acidizing the new well to generate connectivity between the two wells. This type of innovative techniques has never been used in killing a well successfully before. This paper presents the unique thinking and practical approach used for securing this well including the failures as well as successes. The successful procedure may be used in killing future wells under similar condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".