Optimization of Stimulation Treatments in Naturally Fractured Carbonate Formations through Effective Diversion and Real-Time Analysis
Bibliographic record
Abstract
Abstract Long horizontal open holes and naturally fractured reservoirs have always presented a challenge to the industry for successful matrix treatment operations. This is particularly true in western Canada where the reservoirs are competent carbonate formations completed open hole over a length of 2,000 m with a bottomhole static temperature of 110°C. These naturally fractured formations exhibit substantial and unpredictable permeability variations over the length of the interval. During treatment, all or part of the acid may thief to a high-permeability interval leaving the rest of the wellbore poorly stimulated. The industry has developed a range of products and techniques to divert the stimulation treatments from these thief zones in an attempt to improve wellbore coverage and reservoir drainage. However, the placement of these diverting techniques and the evaluation in situ and in real time of their effectiveness were yet to be accomplished. An innovative technique, developed in Western Canada, combines state of the art viscoelastic acid diversion with fiber optic technology for accurate downhole fluid placement and optimum diversion effectiveness. This is a unique system consisting of live downhole temperature and pressure measurements transmitted to surface through fiber optic telemetry installed in the coiled tubing (CT). Real-time analysis of distributed temperature survey (DTS) and single-point downhole measurement of temperature and pressure, along with petrophysical data, provide an in-situ visualization of the dominant thief zones. The analysis of this information allows for on-the-fly adjustment to the diversion placement schedule matching current downhole conditions. This technique provides a unique way to ensure the entire pay zone is fully and homogeneously stimulated, optimizing the reservoir contact and delivering the full well potential. The technique was systematically applied to all newly drilled wells in the Suncor Panther field in the western Canadian Rocky Mountain foothills. The comparison of gas production over the entire field for 16 new wells illustrates that results have substantially improved since the introduction of this innovative technique.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".