Using Electric-Line Tools To Clean Wellbore Laterals and Perforate Toes
Bibliographic record
Abstract
Abstract Coiled tubing (CT) is invaluable equipment in shale oil/gas completion operations. From well cleanout to perforating and fracturing operations, CT does it all. However, with the boom in shale oil/gas development and the number of wells required to develop these unconventional resources, demand and cost for CT can be high and often availability can be limited. In an effort to reduce completion costs and minimize CT use, reserving it for only those tasks that absolutely require it, an operator in the Eagle Ford shale decided to investigate alternative technologies for performing post-cementing well cleanup and toe perforating. The operator was aware of an electric-line (e-line), tractor-conveyed cleaning tool with a reverse circulating bit (RCB) that was being used for drifting and cleanout runs. These tools have been used successfully in Norway and Canada, offshore and on land, to clean out cement stringers and other debris from the wellbore, ensuring a clear path for the toe perforations, which follow. With these tasks successfully completed, the pumping down of frac plugs can be accomplished with confidence during multi-stage completion operations. The operator decided to try the e-line technology on a five-well pilot project of cased, horizontal wells in the Eagle Ford development. This was the first time the technology was used in U.S. land operations. The e-line cleaning tool cleaned the wells, and the tractor-conveyed perforating guns reached the depth cleaned out by the e-line cleaning tool. On this pilot project, the average field time per well for an e-line cleanout and tractor-conveyed toe perforation was 24 hours. The pilot test was considered a success. This paper discusses the details of the project and tool operations for each well, and the lessons learned and applied to each successive well in the pilot to develop a successful strategy for using these tools across a broad range of operating conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".