Horizontal Well Cleaning and Evaluation Using Concentric Coiled Tubing: A 3 Well Case Study from Australia
Bibliographic record
Abstract
Abstract Some of the horizontal wells that were drilled in the Saladin field on Thevenard Island in Western Australia have produced below expectations or have experienced sharp production declines. The reasons for this were not known, although the following were suspected to be contributing factors: Reservoir quality could vary considerably.The horizontal sections were intentionally drilled in a sinusoidal shape and the low points could become traps for sand and heavy fluids.Formation damage may have occurred in some wells that were drilled with a water based mud and a high overbalance. A specialized well cleaning system, which consists of a specially designed jet pump run on concentric coiled tubing, was determined to be a better method than conventional coiled tubing to clean the horizontal sections and investigate the causes of the production problems. The tool can be operated in a fines removal mode or a fluid removal mode which provides a localised drawdown at every point in the wellbore. The localized drawdown allows the mud-damaged zones, gas, oil and water intervals to be identified. The well cleaning system has been successfully used to remove sand and drilling mud damage from horizontal wells in Canada, Venezuela and California. The well cleaning tool can be configured to work with different concentric coiled tubing sizes and a 1" inside 2" concentric string was designed for the Australia project. This paper reviews the design and mechanism of the well cleaning system and provides results on the first 3 wells that were cleaned in the Saladin field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".