Investigating the Effect of Employing Inflow Control Devices on the Injection Well on the SAGD Process Efficiency Using an Integrated Production Modeling
Bibliographic record
Abstract
Abstract Hot steam preparation for so long as of in the Steam Assisted Gravity Drainage (SAGD) process has proved very costly. The "Fort McMurray" reservoir in Alberta, as a large Canadian heavy oil resource, has undergone SAGD, to reach acceptable production rates. The published data was used to investigate new methods, such as that introduced in this paper, for further enhancing this popular heavy oil recovery method. Lack of a uniform steam distribution along the horizontal injection well has demonstrated itself as a major challenge since a considerable portion of hot steam enters the reservoir through perforations near the heel. Thus, regions connected to the toe section of the well do not receive the heat required for equally reducing the heavy oil viscosity. On the other hands, Inflow Control Devices (ICDs) have been used successfully to create a uniform pressure distribution along horizontal wells by providing an extra pressure against the fluid flow near the wellbore. This paper is aimed at conducting the simulation of injection/production systems in the SAGD process on the "Fort McMurray" reservoir. Three scenarios were defined to accurately investigate the effect of employing ICDs on the cumulative oil production as the objective function. The first scenario was designed and run with only one production well to highlight reservoir's poor ability in naturally delivering the oil to the surface. In the second scenario, an injection well was introduced through which hot steam was to be injected, to simulate the SAGD process and in the final case; this injection well was equipped with ICDs. Finally, results indicated a dramatic rise from 3.96 to 6.39 MMSTB in the cumulative oil production as a consequence of implementing ICDs in the injection well in the end of the injection/production period. Moreover, the recovery factor, initially was equal to 40.5%, was improved to 65.2%. Toe temperature, as another parameter to be monitored, experienced an increase of almost 75 °F and reached 400 °F following the use of these devices. In this study, ICDs that had been mostly used in production wells, were utilized to make a smart injection well in the SAGD process which according to simulation results, were successful. Moreover, an integrated production modeling approach saw the reservoir, wells and surface facilities taken into account simultaneously to monitor the effect of interaction of different parts of the production system on eachother.
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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 | 0.001 | 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) | 0.000 | 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".