Optimizing Injector-Producer Spacing for CO2 Injection in Unconventional Reservoirs of North America
Bibliographic record
Abstract
Abstract Shale oil reservoirs such as Bakken, Niobrara, and Eagle Ford have become the main target for oil and gas investors as conventional formations started to deplet and diminish in number. These reservoirs have a huge oil potential; however, the predicted primary oil recovery is still low as average of 7.5 %. Carbon dioxide (CO2) flooding has been a controversial approach to increase oil recovery in these poor-quality formations. This study investigated the effect of injector-producer spacing, in range of 925-1664 ft, on CO2 performance in these plays by using numerical simulation methods. CO2 utilization value under differrent injector-producer spaces has been calculated. Increments in oil production rate, cumulative oil, and oil recovery factor have been determined in 1, 5, 10 years of CO2-flooding start-point. In this study, unfractured horizontal injectors are modeled to avoid conformance problems in natural fractured unconventional formations. Furthermore, the physical behavior for CO2 flooding under different conditions has been discussed. Finally, simulation results were analyzed and compared with some of pilot tests which had been conducted in North Dakota and Southeast Saskatchewan. The results indicated that CO2 flooding performance would be more pronounced, by increasing oil production rate and oil recovery factor, as the injector-producer spacing minimized. However, CO2 utilization value is significant high when the injector-producer spacing is very short due to depleted volume closeness. Interestingly, CO2 utilization value for all spacing scenarios would gradually be reduced with flooding time. This reduction in the injected-gas utilization-value has been matched with the pilot test which performed in southeast Saskatchewan. In addition, CO2 efficiency indicator is generally in range of 4.85-44.5 Mscf/STB in these unconventional reservoirs which is relatively high as compared with conventional reservoirs. These results have been confirmed by a good match which has been obtained between simulation results and some of pilots’ performance. This paper provides a thorough idea about how to optimize the injector-producer spacing for CO2 flooding in these complex plays. Also, this work explains that CO2 efficiency indicator is different in these unconventional formations as in conventional reservoirs.
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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 | 0.000 | 0.001 |
| 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 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".