Composite Plug Milling Efficiency Improvement through Rheology Control - Lessons Learned from the Horizontal Completions in the Duvernay Shale
Bibliographic record
Abstract
Abstract The Duvernay Formation in North Western Alberta holds an estimated 443 trillion cubic feet of gas and 61.7 billion barrels of oil. Horizontal wells are drilled and completed with multiple fractures to maximize the reservoir contact. The most common technique used for hydraulic fracturing in these wells is the plug n' perf method. Once the fracturing operation is completed, the plugs must be milled out using coiled tubing to restore wellbore access, as well as to put the well into production. While much emphasis is placed on improving fracture design and proppant placement in order to yield maximum production, optimizing the milling process typically focuses on mill bits, motors, and plug designs. Mill bit design and motor life have considerably improved as a result of intensive research and development in this field. New plug designs promise quick disintegration during milling. However, little attention has been directed toward understanding solids transportation and movement in the well bore. Rules of thumb are still commonly used to pump gel sweeps or determine the number of wiper trips needed when milling plugs or cleaning a well bore. As a result, current milling practices leave a significant amount of debris in the well bore, and pipe sometimes sticks due to cuttings or plug debris not transported to surface as a result of inadequate rheology. These risks not only jeopardize the safety of operations but incur extra completion costs. In an effort to improve plug milling efficiency, a new technique was introduced during a well completion project in the Duvernay shale. The objective of this technique was to optimize the entire milling process while focusing on fluid rheology. This paper describes case histories comparing the results before and after the implementation of this innovative concept. It also outlines the benefits realized in terms of savings and the impact on project economics. The lessons learned and best practices developed during this process are shared to improve the understanding of milling process.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".