Alkaline Surfactant Polymer Flooding To Revitalize Oil Production from a Mature Water Flooded Field
Bibliographic record
Abstract
Abstract Mitigating production decline is a challenging task that every oil company will be faced with at some point over the life of an oil reservoir. However, depending on the existing reservoir fluid and rock characteristics, saturation distribution, and the level of heterogeneity of the reservoir rock, Enhanced Oil Recovery (EOR) programs can be implemented to alleviate the decline in oil rate and improve overall recovery. This paper presents an example of a how a mature waterflooded field in southwestern Saskatchewan, Canada could be revitalized using Alkaline-Surfactant-Polymer (ASP) flooding. In this study, laboratory tests were undertaken to select effective chemicals and optimize concentrations that would yield the highest potential oil recovery. Subsequent radial coreflood experiments demonstrated a wide range of potential recovery that depended on slug size and chemical concentration. A detailed numerical simulation of the optimum core displacement was performed in order to calibrate the interaction of the EOR agent with the reservoir rock and fluids, and ultimately upscaled to the full field numerical model Reservoir simulation sensitivity runs were conducted in order to identify an optimum field development strategy using the selected ASP fluid. The results from this optimized development strategy were compared to the waterflood base case to demonstrate the potential upside of the chemical flood. This paper also presents a holistic roadmap for developing EOR projects from initial concept to field implementation and beyond.
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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) | 0.001 | 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 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".