Reducing Excessive Water Production Associated With Gas Hydrate Reservoirs Using a Thermal In-Situ Heating-Inhibitor Method
Bibliographic record
Abstract
Abstract Gas hydrates are one of the most abundant sources of energy present today. They are formed at high pressures and low temperatures, and contain mainly water and methane. When dissociated, a large volume of water forms, much of which is produced. This research performs a simulation study on how to decrease the volume of water produced from gas hydrate reservoirs by utilizing an in-situ heating method combined with a low concentration thermodynamic inhibitor injection. Since gas hydrates form at high pressures and low temperatures, depressurizing the reservoir, or increasing its temperature would cause the solid hydrates to become unstable, and dissociate. The research begins by building a hydrate reservoir model using almost the same description of the models present in the literature in order to compare the results obtained. Several simulation runs were then performed using various production methods, several types of inhibitors, and finally testing and optimizing the newly proposed production method which combines thermal stimulation with inhibitor injection. The optimization process involves testing the novel method using 5-spot, 7-spot, and 9-spot production methods. The effect of each variable on the water recovery was studied, and the conditions under which the lowest water recovery were obtained. The highest water production occurred during glycol injection since it had the largest endurance to hydrate reformation and thus the largest water flow duration. When the glycol was combined with the thermal stimulation method however, the lowest water recovery was obtained. This is mainly due to two factors which include high rate of depletion of reservoir pressure, and the significant decrease in glycol concentration when used with thermal stimulation. This novel production method was chosen as the best method in terms of low water recovery based on a comparison of its recovery with that of all the other methods. The second task was to further optimize this method by introducing several well patterns and comparing their performance to that of the single well case. The largest number of wells, 9-spot pattern, was found to have the lowest water recovery due to the extremely high rate of reservoir pressure depletion. Gas hydrate production is still considered in its preliminary steps due to the complexity of hydrate reservoirs. By understating the mechanism by which these reservoirs can flow, and trying to reduce the excessive water production associated with these reservoirs a better understating of how to economically and safely produce from gas hydrate reservoirs is reached. This may lead to the utilization of this source of energy in the near future.
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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 | 0.001 |
| 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".