Bibliographic record
Abstract
Technology Focus This months’ Gas Production Technology feature may provide solutions to problems that have plagued our industry for years. It has always been impossible to unequivocally answer, “Where should I set the tubing tail in a long vertical completion?” or “Should I drill a horizontal well toe up or toe down?” because one cannot isolate the effect of one parameter from all others in a real well. If history is a guide, only years of keen observations in many wells will lay the foundation on which industry will postulate, test, and realize technical breakthroughs. Two papers present examples of such keen observations, while a third envisioned a long-term test in Alaska that could unlock the secrets of natural gas hydrates. In 2008, the US Minerals Management Service reported that between 11,000 and 34,000 Tcf of methane is trapped in hydrates just in the northern portion of the Gulf of Mexico; so, this resource is game-changing, even if only a small fraction is recoverable. That is the good news. The bad news is that researchers worldwide have spent more than 25 years and billions of dollars studying naturally occurring hydrates, with little progress toward demonstrating commercial scale production. Like any other natural resource, hydrates come in a variety of natural environments. A few are significantly more suitable to production than the rest. Written in 2010, paper OTC 22152 described an optimal production test location, with high permeability and hydrate-saturated sand at temperatures that would allow spontaneous methane production with a small decrease in pressure. It also happens to be in the middle of one of the largest concentrations of oilfield infrastructure in the world. In 2011, the Ignik Sikumi 1 (Inuit for “fire in the ice”) was drilled next to the gravel road near the Prudhoe Bay L-pad, and, in early 2012, the longest hydrate production test was conducted. Although the formal objective was to evaluate CO2/methane exchange in the field, the well produced intermittently for 30 days, giving a hint of what could happen on a simple long-term depressurization test. Even though this was a step forward, it will remain unclear what recoveries were a result of CO2/methane exchange, nitrogen-injection stimulation, pressure depletion, or something we have not considered. As with any complex system, only long-term and repeated tests can isolate the critical mechanisms. Because the well was plugged and abandoned as the ice pad melted with the approaching summer, we still have a long way to go to see if hydrates can live up to their potential. More details can be found at www.netl.doe.gov. Recommended additional reading at OnePetro: www.onepetro.org. SPE 151611 Taking Advantage of Fines Migration Formation Damage for Enhanced Gas Recovery by P.T. Nguyen, University of Adelaide, et al. SPE 142283 Effect of Water Blocking Damage on Flow Efficiency and Productivity in Tight Gas Reservoirs by Hassan Bahrami, Curtin University, et al. SPE 141036 Gas Well Dewatering Method Field Study by Rick D. Haydel, Altec, et al. SPE 153072 Production Data Analysis in Eagle Ford Shale Gas Reservoir by Bingxiang Xu, China University of Petroleum, Beijing, et al. SPE 153073 Cyclic Shut-In Eliminates Liquid Loading in Gas Wells by Curtis Hays Whitson, NTNU/PERA, et al. SPE 145576 Two-Phase Flow Choke Performance in High-Rate Gas/Condensate Wells by Hamid Reza Nasriani, Iranian Central Oil Fields, 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".