Overcoming Challenges of Stimulating a Deepwater Frac-Pack-Completed Well in the Gulf of Mexico Using Coiled Tubing with Real-Time Downhole Measurements
Bibliographic record
Abstract
Abstract Achieving effective fluid coverage of stimulation operations in deepwater frac-pack completions is often challenging due to a variety of factors, including, but not limited to, the length of screened intervals, the uncertainty of damage mechanisms, and the ability of diversion materials/fluids to divert beyond the screens and into the formation. This case study demonstrates a successful technique used in conditions not previously attempted. This treatment in a deepwater, frac-packed well with fiber-optic-equipped coiled tubing (CT) and a rotating, hydraulic high-pressure jetting tool achieved the successful stimulation of a 500-ft-long frac-packed zone after several previous failures using different techniques. By using a CT equipped with fiber optics and downhole measurement tools, engineers were able to perform a data-driven operation based on real-time bottomhole measurements and distributed temperature surveys. This successful treatment improved productivity by 75% compared to the well before treatment. Typically, treatments of this nature are investigated and techniques for a field or region are refined over the course of multiple stimulation operations of large numbers of similar wells in the area. However, in deep water, most fields have only a very small number of wells. The costs associated with gaining wellbore access to conduct an acid treatment and with handling produced stimulation fluids are very large compared with costs in other geographic areas. Each individual well has a high productivity, and improper stimulation is an enormously costly lost opportunity for the operator. This makes it very important to ensure that every job is performed as optimally as possible, without resort to iterative or empirical methods. This method increases the opportunity to produce a successful treatment the first time and expands the technical envelope of application. These enhancements should allow other operations of this type to be conducted that previously would have been too high risk to consider. This was a high-pressure application compared to previous operations. It was one of the longest fiber optic cables injected into a CT reel. Modifications were made to the CT reel to support the expanded weight. A stronger type of fiber optic carrier had to be utilized. A customized testing and validation procedure had to be used to extend the operating envelope of the fiber-optic-enabled downhole tools to perform reliably.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".