Optimizing Waterflood Performance by Utilizing Hot Water Injection in a High Paraffin Content Reservoir
Bibliographic record
Abstract
Abstract The application of heated water injection in a conventional oil reservoir under secondary recovery has received sparse review in the literature. In general, such a scheme can be difficult to justify. This is often due to both the additional capitalization and fuel costs offsetting the potential advantages that can be practically achieved for the effects of the differential water temperature to that of the formation. However, under appropriate circumstances, hot water injection can provide a significant advantage for secondary recovery that may warrant further investigation. As a case study, the Senex field of northern Alberta, Canada contains a 37°API crude with high paraffin content in a low permeability shelf carbonate. The oil is essentially saturated with paraffin at the reservoir temperature of 36 C. Paraffin precipitation within the reservoir porosity accounts for declining production rates, which have been commonly treated and temporarily reversed with an extended history of chemical and solvent squeezes. Due to the northern latitude of the field, source water has been injected at ambient temperatures to within several degrees of the freezing point for up to 7 months of the year. The impact of this operation on injector and reservoir performance proved to be of concern after an injection well appraisal revealed an extended loss of injection capacity in the early startup of waterflooding operations. Subsequently, the thermal implications for the viscous forces to the reservoir dynamics in waterflooding were studied in a numerical simulator, which demonstrated this mechanism for recovery impairment. In addition, a qualitative assessment of the further thermal implications for deterioration of reservoir performance from paraffin deposition was established. In order to test and further validate the conclusions of this analysis, a pilot hot water injection scheme was initiated in April, 2001. This paper will describe the aspects of planning, testing and modeling that were completed prior to the field implementation of this project. Preliminary field results are also presented.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".