Live Fiber-Optic Telemetry Downhole Measurements on Coiled-Tubing-Enabled High-Efficiency Sand Cleanout in a Subhydrostatic Well for the First Time in the Gulf of Mexico
Bibliographic record
Abstract
Abstract Gulf of Mexico (GoM) reservoirs consist mainly of unconsolidated sandstones. Elevated production rates and high near-wellbore fluid velocities increase the possibility of dragging the unconsolidated sand grains into the wellbore and surface production equipment. Formation sand invasion into wellbore is a common problem in the GoM, especially in wells not completed with sand control features or wells where initial sand control failed. Wellbore sand cleanouts, therefore, represent the majority of coiled tubing (CT) interventions in the GoM. As the reservoirs mature, the number of through tubing well interventions (TTWI) in sub-hydrostatic wells is constantly growing. These types of wells cannot sustain a full column of fluid which presents a significant challenge to maintain circulation and sufficient annular fluid velocity required to transport the solids to surface. Nitrified and foamed cleanouts can provide at-balance or slightly over-balanced downhole conditions necessary to minimize the fluid losses into formation. Nitrified cleanouts are normally considered as more cost effective solution which is quite important since most of the sub-hydrostatic wells are very marginal producers. However, planning and execution of nitrified jobs require accurate well information which is not always accurate or available. This drives the operators to make the most common decision; to utilize the more expensive and extensive cleanout technique - the foamed cleanout - in order to ensure job objectives with reduced risks. Real-time downhole measurements provided by a fiber-optic enabled telemetry CT helps overcome the typical challenges and operational risks associated with sub-hydrostatic well cleanouts; it also helps reduce the overall operational costs by making the job more efficient in terms of time, equipment and product usage. This paper will present a case study of the first optimized CT sand cleanout in the Gulf of Mexico.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".