Utilization of CO2 Injection to Improve Oil Recovery of the Handsworth Bakken Formation
Bibliographic record
Abstract
The Bakken formation has become a prominent oil resource for south-east Saskatchewan, especially with the advent of horizontal well technology and new hydraulic fracturing methods. As more wells are drilled, there is a desire to determine whether there is potential for improved oil recovery and to evaluate the economic feasibility. This paper evaluates the benefit of implementing waterflooding, CO2 injection or WAG (water-alternating-gas) recovery methods for improved oil recovery of the Bakken formation. A simulation model resembling the study area was built using CMG-GEM (computer modeling group-generalized equation of state model) reservoir simulation package and a history match of the primary recovery data available was performed. Based on the simulation results, it was concluded that waterflooding had a significant influence on the oil recovery factor, although CO2 provided the highest increase in crude oil recovery. The capital expenditure for surface facilities and cost of injected fluid was the most economically viable for implementation of waterflooding. The WAG injection simulation results were similar to CO2 injection, except that reservoir pressure was able to be better maintained. Given that high-quality source water is available, waterflooding is the most economically feasible choice according to the simulation results obtained from this study.
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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 | 0.001 | 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".