Reviving a Mature, Watered-Out Fractured Carbonate Reservoir: An Integrated Approach to Design a Miscible EOR Scheme for the Bigoray Field in Alberta
Bibliographic record
Abstract
Abstract The Bigoray area of the Pembina field in western Alberta consists of approximately 50 naturally fractured Nisku carbonate reefs. Production from the Bigoray Nisku D and E pools started in 1978, and shortly after, water injection was initiated to maintain reservoir pressure as a secondary drive mechanism. By 2013, the pools had reached high water cuts, making them uneconomical to produce. In 2017, a decision was made to reactivate the pools and initiate a solvent injection enhanced oil recovery (EOR) project feasibility assessment. A multidisciplinary team was assembled to review and reinterpret all the geoscience data with modern methodologies to characterize the reservoirs and create new static model descriptions to be used in a dynamic model. Data from well logs, seismic, core measurements, and image logs were integrated into a comprehensive and consistent model that could be used with certainty as a prediction tool. A history-matching process was carried out by creating different realizations of the static model to honor well-to-well connectivity and water movement within the pools. The history-matching process was performed while ensuring that the model updates were global in nature and consistent with the geological understanding of the reservoirs. The history-matched model was used to optimize the location of new producers and injectors based on remaining oil saturations and reservoir structure. Optimization of the EOR scheme involved testing a matrix of scenarios to investigate the effect of injection rates and solvent volumes as well as production pressures and voidage ratios. Additionally, in an effort to improve displacement efficiency, a large number of simulation runs were devoted to test and establish the most efficient locations for the well perforations in both the new injectors and producers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".