Simulation of natural gas production from submarine gas hydrate deposits combined with CO2 storage: Poster at EGU General Assembly 2013, 07.-12. April 2013, Vienna, Austria
Bibliographic record
Abstract
The recovery of methane from gas hydrate layers that have been detected in several submarine sediments and permafrost regions around the world so far is considered to be a promising measure to overcome future shortages in natural gas as fuel or raw material for chemical syntheses. Being aware that natural gas resources that can be exploited with conventional technologies are limited, research is going on to open up new sources and develop technologies to produce methane and other energy carriers. Thus various research programs have started since the early 1990s in Japan, USA, Canada, South Korea, India, China and Germany to investigate hydrate deposits and develop technologies to destabilize the hydrates and obtain the pure gas. In recent years, intensive research has focussed on the capt ure and storage of carbon dioxide from combustion processes to reduce climate change. While different natural or manmade reservoirs like deep aquifers, exhausted oil and gas deposits or other geological formations are considered to store gaseous or liquid carbon dioxide, the storage of carbon dioxide as hydrate in former methane hydrate fields is another promising alternative. Due to beneficial stability conditions, methane recovery may be well combined with CO2 storage in form of hydrates. This has been shown in several laboratory tests and simulations - technical field tests are still in preparation. Within the scope of the German research project »SUGAR«, different technological approaches are evaluated and compared by means of dynamic system simulations and analysis. Detailed mathema tical models for the most relevant chemical and physical effects are developed. The basic mechanisms of gas hydrate formation/dissociation and heat and mass transport in porous media are considered and implemented into simulation programs like CMG STARS and COMSOL Multiphysics. New simulations based on field data have been carried out. The studies focus on the evaluation of the gas production potential from turbidites and their ability for carbon dioxide storage. The effects occurring during gas production and CO2 storage within a hydrate deposit are identified and described for various scenarios. The behaviour of relevant process parameters such as pressure, temperature and phase saturations is discussed and compared for different production strategies: depressurization, CO2 injection after depressurization and simultaneous methane production and CO2 injection.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.033 |
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; both teacher heads agree on what is shown here.
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".