Optimizing Well Completions in the Canadian Bakken: Case History of Different Techniques to Achieve Full ID Wellbores
Bibliographic record
Abstract
Abstract The development of the Bakken formation has seen a rapid growth in the last ten years thanks to the improvement of horizontal drilling and multistage stimulation techniques. The Canadian Bakken is located in the SE corner of the Province of Saskatchewan. This formation is a Dolomitic Siltstone reservoir that is shallower (1400 to 1700 m TVD) and thinner (4 to 12 m thick) than its counterpart on the US side; adding some challenges to its exploitation. Moreover, it has an overlying water bearing zone (Lodgepole limestone formation) that can dramatically increase the water cut percentage during production if communication occurs during the fracturing process. For this reason, fracture stimulations are typically done at low rates and low sand tonnages and concentrations. One operator’s focus to date has been to complete the wells using ball drop systems and open hole packers, leaving the balls and ball seats in the wellbore and milling them out later based on well-by-well analysis. This approach does not allow further workover operations or refrac opportunities without removing the restrictions. Production performance dictates the decision of when to mill out the ball seats; however it has several drawbacks like additional cost, loss of production during the operation, longer operation time and excessive fluid losses. Cost effective and technically sound alternatives to provide full inner diameter (ID) wellbores during initial operations were investigated and a variety of those methods were implemented in the field. The selections included: retrievable frac seat assembly, coiled tubing half straddle frac tool with sliding sleeve ports and milling out conventional frac ports during the initial completions. All of the completions were executed with open hole packers similar to the previous approach. This paper describes and summarizes the observations and lessons from the application of these methods, as well as the modifications to the fracturing fluid systems based on field application. Also, a comparison of all techniques implemented, including time, cost and production, along with this operator’s assessment for future development in the area are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".