Targeted Annular Hydraulic Fracturing Using CT-Enabled Frac Sleeves: A Case History from Montana's Bakken Formation
Bibliographic record
Abstract
Abstract Unconventional reservoirs require innovative completion techniques and technology to become more economical. Formations vary drastically in lithology, lateral lengths, completion methods, and financial drivers. Since reservoir conditions typically mandate the use of hydraulic fracturing for economical production results, a significant amount of resources are focused on making the fracturing process more efficient and lowering its environmental impact. This case history includes an operational review focused on a novel technique to rapidly perform targeted annular hydraulic fractures by deploying an activation tool on coiled tubing (CT) to open frac sleeves in a horizontal well. The paper covers results and information gathered from the first operation of its kind in Montana's Bakken Formation – where a total of 19 stages were fractured in the shaley dolomite. The new technology does not involve dropping balls or setting composite plugs to isolate stages. Rather, the technique utilizes frac sleeves that are activated using a CT bottomhole assembly to save time between stages. The sleeves can be cemented in place if desired and have a full-bore internal diameter that does not require post-treatment composite plug millouts, further reducing overall completion time and time to production. Additional capabilities of the system enable the use of memory gauges to monitor downhole pressure and temperature characteristics. The operator preferred this method due to its targeted nature, efficiency, and fluid savings. As opposed to multicluster fracturing methods (such as ‘plug-n-perf"), a single entry point during the frac job promotes increased fluid velocity, enabling a more aggressive sand ramp and reduction in required hydraulic horsepower. Similarly, the single entry point ensures that a fracture is generated at each port, as opposed to the possibility that some clusters are not treated. In addition, no composite plugs or diverting devices are required to be pumped down the wellbore, so water usage is reduced considerably. The system, already used in thousands of fracturing stages in Canada, speeds up the completion process, uses less fluid, minimizes risks, and reduces overall downtime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".