Selective Stimulation of Multiple Sets of Perforations With Straddle Tools: Lessons Learned Lead to Equipment and Methodology Optimization
Bibliographic record
Abstract
Abstract Many of the more recent wells in the Ekofisk Field in the Norwegian North Sea have been completed as monobores with lengthy horizontal or high angle sections and several distinct perforation clusters – as many as 20 separate zones can occur. The early methods of stimulating these chalk reservoirs involved high volume acid jobs, featuring bull heading, and the use of frac balls to divert flow to the less permeable sections. However after collection and evaluation of much data it became clear that many of the perforated intervals were unproductive. In order to address this situation it was decided to change to a selective stimulation method using newly designed coiled tubing (CT) deployed straddle pack-off tools. Subsequent evaluation of this method indicated that it was a more productive approach; however the reliability of the method was less than desirable due to equipment and methodology failures. This realization lead to a detailed study of these CT operations carried out over a period of 9 months involving evaluation of the results of stimulating 9 separate wells with perforation sets ranging from 7 to 20. The study involved review of equipment design and function, material choice and methodology such as pumping techniques, use of glycol trains, accuracy of depth measurements, reliability of pressure measurement equipment etc. with the objective of optimizing them for future operations. In this paper the authors will briefly review the history and results of the initial stimulation of these wells and the decision to go to straddle pack-off methods. They will then describe in detail the results of the study to evaluate and improve equipment and techniques, the lessons learned and the changes that resulted, leading to more reliable and effective stimulation techniques that are now being used.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".