Full Field Chemical EOR in a Mature Southern Alberta Water Flooded Reservoir - The Little Bow Case Study
Bibliographic record
Abstract
Abstract Despite the recent focus on unconventional resources such as shale gas and tight oil in North America, large unrecovered volumes of oil remain in conventional reservoirs making them viable candidates for chemical enhanced oil recovery processes. Replacing or following traditional water flooding with aqueous chemicals that use both surfactant & alkali to reduce interfacial tension and polymer to improve sweep efficiency has been successful in recovering incremental oil from these reservoirs. However, designing the chemical injection scheme is complex, and must be tailored to specific reservoir rock and fluid properties. A strategic design methodology can help provide an optimal, well-performing chemical formulation, even for challenging reservoirs. Currently, there are around ten Canadian chemical injection projects in operation with the latest being the Little Bow Upper Mannville "I" Pool. An ASP EOR project was initiated in this reservoir in March 2014 in a multiphase development plan. When the project was initiated, Little Bow oil (Phase 1&2) production was around 350 bbl/day. Successful implementation of this project is expected to result in incremental recovery of 5.2 million barrels of oil (12% of the OOIP) over the base waterflood. As of January 2016, 6.648 million barrels of ASP solution has been injected in the reservoir and the field is now showing first signs of incremental production. This paper presents a workflow that integrates laboratory results, geological and geophysical data and production history into an effective forecasting model. Complex geology and a long production history of the partly depleted Little Bow reservoir have presented a challenge for history matching the primary and water-flood production. Through an iterative process, a full field model was built, history matched, then used as a base case to determine an optimal operational design for the full field. A multidisciplinary team including geologists, exploitation and reservoir engineers collaborated to develop a 3D geological model and achieve the history match using an iterative approach; resulting in an idealized workflow and a superior history matched model. Using this model and an advanced optimization algorithm, a full field ASP operation design was optimized (based on NPV) for slug sizes, chemical concentrations, pattern design for injection/production wells locations, and drilling & workover locations. The optimized ASP injection scheme is implemented and some field results from January till June 2016 are presented in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".