Increasing Production Flow Rate and Overall Recovery from Gas Hydrate Reservoirs Using a Combined Steam Flooding-Thermodynamic Inhibitor Technique
Bibliographic record
Abstract
Abstract When producing from gas hydrate reservoirs using steam flooding, since hydrate dissociation is an endothermic reaction, the heat is used up. This results in a decrease in reservoir temperature which causes the hydrate equilibrium conditions to be established again, thus causing hydrate reformation. This research studies the effect of injecting thermodynamic inhibitors during steam injection on overcoming the problem of hydrate reformation which in turn will increase hydrocarbon recovery significantly from hydrate reservoirs. The reservoir model was built based on data collected from previous models found in the literature. After specifying all parameters for the reservoir, and the hydrate layer, a systematic study was performed in order to assess the use of inhibitors with steam flooding. The production methods studied include depressurization, steam flooding, inhibitor injection including both brine and glycol, and finally the combined steam flooding inhibitor injection method. The conditions for the steam flooding were kept the same during all runs in order to be able to compare them. Results indicated that the use of the thermal stimulation alone without inhibitor managed to increase recovery, however, the problem of hydrate reformation occurred which caused a cessation of production. Using inhibitors alone managed to increase recovery as well, however the recovery increase was much less compared to thermal stimulation. The type of inhibitor also played a role in recovery with the glycol producing the most, followed by the brine. By combining both steam flooding and inhibitor injection, the recovery increased significantly more than what was observed when using each of the methods on its own. To the authors' knowledge, no extensive study has been performed by combining both steam flooding and inhibitor to increase hydrocarbon recovery from hydrate reservoirs. This research can help in improving real field gas hydrate projects by making the overall project much more economic by increasing hydrocarbon recovery.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".