The Myths of Waterfloods, EOR Floods and How to Optimize Real Injection Schemes
Bibliographic record
Abstract
Abstract Waterflood implementation accounts for more than half of the oil production worldwide. Despite the observations and extensive research from a large number of floods and thousands of simulation studies, managing waterfloods and Enhanced Oil Recovery (EOR) floods is still a technical challenge. A major contributor to this challenge are waterflood induced fractures (WIF). Managing waterfloods is a multivariable problem although WIF are one aspect, it is by no means the only controlling factor. The best evidence that WIF are one of the main factors controlling flow in reservoirs is the insensitivity of injection pressure to injection rates. With our experience, in hundreds of waterfloods, we have frequently observed this phenomenon in the field data. If fluid flow depended on diffusive Darcy flow alone, we would expect higher injection rates with higher injection pressures. However, it is common to observed relatively constant injection pressures over a wide range of water injection rates. Rapid well communication and changes in water cuts that vary with injection rates also support an interpretation of high permeability induced fractures between injector and producer. In some reservoirs, interwell tracer data can be used to determine the influence of induced fracture features. The interwell tracers usually show very fast water movement. Induced fractures in waterfloods and EOR projects can be caused by a number of mechanisms such as but not limited to, pressure depletion, changing pressure regimes, thermal effects, or plugging effects. These fractures can either be beneficial to the reservoir performance or effect performance negatively. Benefits include improved injectivity and increased throughput of the displacing fluid. Negative effects can come in the form of reduced volumetric sweep efficiency, impaired ultimate recovery or injected fluid losses out of zone. Case studies, theory, and available literature from Western Canada will be reviewed in order to suggest and improve reservoir management strategies for waterfloods. We have completed hundreds of waterflood feasibility, waterflood management and EOR flood studies worldwide and continue to be amazed and humbled by the complexity that many waterfloods and EOR floods exhibit due to induced fracturing. WIF and EOR induced fractures (EIF) are common and should be analysed to optimize production. Growth of the WIF, response to waterflood with the presence of WIF, implication of WIF and reservoir management are the main areas which will be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".