Bibliographic record
Abstract
Technology Focus To cater to the seasonal highs and lows in demand throughout a year, natural gas is stored in underground storage facilities. The need for such facilities arose because increasing (or decreasing) gas production to mirror changes in demand is neither practical nor economical. Therefore, gas is produced and stored during low-demand times and retrieved to meet the highs during winters. This cyclic trend can be observed also in gas-storage inventories [as reported by the Energy Information Administration (EIA)]. The northeast US has the highest concentration of such storage facilities, close to the biggest market. Most of the underground gas-storage facilities are depleted reservoirs, but aquifers and leached salt caverns are also used. The US and Canada were the pioneers in realizing the potential of depleted reservoirs for gas storage and, hence, have several well-developed facilities. What makes depleted reservoirs attractive is the presence of existing wells used to produce the reservoir, plus the geologic and engineering knowledge acquired during the development of the field. All that information is leveraged when repurposing the field for gas storage. Depleted reservoirs also contain some amount of gas already in the reservoir, which decreases the requirement to inject gas as cushion gas. (Cushion gas is immobile gas that remains in the reservoir and allows the reservoir to be used for storage.) Depleted reservoirs are also the most abundant. I would be remiss not to touch upon the Aliso Canyon incident of 2015. Aliso Canyon was the second-largest storage facility in the US at the time. The industry learned a harsh lesson about the repercussions when containment is lost for such facilities. The loss of containment was because of the casing failure of one of the wells (along with having subsurface safety valves removed in the past). This led to 109,000 tonnes of methane emissions by some estimates. One measure that was proposed to reduce such risks is to use offshore reservoirs nearing abandonment for gas storage (instead of onshore ones that are close to populated urban areas). At the time of this writing, the week-to-week change in the natural gas storage inventory was –237 Bcf, and levels remained lower than the 5-year historical average, according to the EIA. This withdrawal from the inventory was less than most forecasts based on the extreme cold temperatures faced by the US. The Henry Hub natural gas prices reached highs of more than $4.5/million Btu in 2018 because of record gas demand. The price has since dipped to $2.57/million Btu because of abatement in the extreme weather leading to lower demand for heating. To learn more, visit the SPE Annual Technical Conference and Exhibition in Calgary, 30 September–2 October, and the Gastech Exhibition and Conference in Houston, 17–19 September. Recommended additional reading at OnePetro: www.onepetro.org. SPE 190838 Comprehensive Evaluation of the Dynamic Sealing Capacity of Clayey Caprocks in a Large Underground Gas Storage by Junchang Sun, PetroChina, et al. SPE 192961 Maintaining Flare-Tip Health by Venugopal Bakthavachsalam, ADNOC, et al. SPE 192783 Cutting-Edge Solutions for Sour-Gas Treatment: A Retrospective Study by Kuwait Oil Company by Fahad Al-Ghanem, Kuwait Oil Company, et al.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".