Comparing Different Scenarios for Thermal Enhanced Oil Recovery in Fractured Reservoirs Using Hybrid (Solar-Gas) Steam Generators, A Simulation Study
Bibliographic record
Abstract
Abstract The application of the renewable energy sources, especially solar energy, for thermal enhanced oil recovery methods as an economical and environmental valuable technique has received many attractions recently. Concentrated Solar Power systems are capable of producing substantial quantities of steam by means of focused sunlight as the heat source for steam generation. This paper aims to investigate viability of using this innovative technology in fractured reservoirs to generate steam instead of using conventional steam generators. A synthetic fractured reservoir with properties similar to those of giant carbonate oil reserves in the Middle East was designed by using commercial thermal simulator. The dual porosity model was used to account for differences in matrix and fracture parameters. Different cyclic and continuous steam injection scenarios using combination of both solar energy and fossil-fuel to generate steam were designed. The cyclic scenarios were different in terms of contribution of solar energy in steam generation and in case of 100% solar scenario a small nightly steam injection using fossil-fuel was suggested to prevent flow back into the wellbore. It was assumed that total amount of injected steam in 10 year time period is the same for all the scenarios regardless of how steam was generated. Simulation results showed that nightly injection of insignificant amount of fossil-fuel-generated-steam in a 100% solar-generated-steam injection process increases the cumulative oil production compared to 100% solar-generated-steam injection system with no nightly injection. Furthermore, there was no significant difference between the final oil recoveries for all the designed cyclic injection scenarios. Although continuous steam injection scenario had the highest final oil recovery among all scenarios, a detailed economical study showed that net present value for 100% solar-generated-steam scenario is the highest. An environmental analysis on all scenarios also indicated significant reduction of CO2 emission into the atmosphere for the latter scenario. Therefore, hybrid steam generators which utilize solar energy instead of traditional fossil-fuel for steam generation is proposed for Middle East fractured reservoirs where there is abundance of sunshine during day time. The findings illustrate high economic efficiency of solar-generated steam injection and highlight it as a green EOR method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".