Modeling Gas Injection into Shale Oil Reservoirs in the Sanish Field, North Dakota
Bibliographic record
Abstract
URTeC 1581998 The Sanish Field, which is located in the Mountrail County, North Dakota, is the focus of the current study. The primary recovery factor of the Sanish Field remains low and has been estimated to be less than 15%. Other than horizontal drilling and multi-stage fracturing application, enhanced oil recovery is the essential process to increase the recovery factor and maximize the potential production from this field. Among several EOR options, CO2 flooding may be effective to increase the recovery factor. Earlier studies of Bakken in the Elm Coulee Field and in the Saskatchewan part of the Bakken indicated that the recovery factor could be increased by 10-15% when using gas injection. In this paper, a numerical reservoir simulator is used to evaluate the performance of CO2 injection for the Bakken interval in a sector of the Sanish Field. There are presently three 10,000 foot laterals in the 4 square miles sector. For modeling purposes, reasonable data values were chosen from known ranges, and well and completion information from the research area was included. A low primary recovery factor of 5.42% was obtained through flow modeling, and declining trends of the future production performance of wells in the research area were observed. Several different scenarios of gas injection are tested to analyze gas injection performance and evaluate its technical feasibility and effect. It appears that gas injection is suitable in such tight environments, as the recovery factors increased significantly for miscible CO2 injection. Sensitivity analysis was ran by using different injection rates, by adding additional wells to the pattern, by comparing different fracture conductivities and by evaluating different injectants. Depending on the scenario, the recovery factor increases the most by 24.59% through adding four new horizontal injectors into the field sector. Moreover, gas injection was confirmed to be effective than water flooding. Maximum of 8000 psia injection pressure and maximum injection rate of 5000 Mscf/day along with more horizontal injection wells were estimated to be better options for gas injection in the study area. This study can help to evaluate expected ultimate recovery (EUR) for future projects in the Sanish Field. It can also help to estimate the future economic viability of using gas injection and evaluate risks for the Sanish Field potential development. All these factors will directly impact the oil companies’ interests and future unconventional resources development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".