Bibliographic record
Abstract
Abstract The number of fields that are being developed, or redeveloped, using multi-lateral wells to enhance the effective drainage from minimal surface facilities is increasing. Some of these types of wells have complex Level 3, or higher, junctions but the vast majority are still Level 1 junctions. Often drilling and other damage can be imposed on the various exposed leg well bores, as in the case of a Level 1 junction, while the other branches of the well are being drilled. This formation near well bore damage can, in the extreme, lead to a re-evaluation of the field development strategy as there has been no intervention method available to enter the various branches and perform effective stimulation or clean-up and, therefore, achieve the full production potential from the well. This paper describes a new Bottom Hole Assembly (BHA), enabling a new method to workover multilateral wells using coiled tubing. The BHA and its method of navigating through multi-lateral wells makes the assumption a smart and/or selective entry completion is not installed in the well. Therefore, the onus is on the BHA to intelligently navigate through a junction(s) whether it is an open hole or cased hole. The BHA's operational success however, is dependent on the geometry of the junctions. Examples from case studies performed in Canada Venezuela will be used to explain how the geometry is critical for successful operation of the tool. In addition, these case studies will explain what type of workovers have been tested and proven and what further possibilities exist.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".