Bibliographic record
Abstract
When it comes to oil and gas production in the US, Texas is king. In 2022, the Lone Star state pumped out 1.83 billion bbl of crude in total—that’s over three times the next state on the list, New Mexico. Over the same period, Texas set a record with 11.2 Tcf of natural gas production, more than 3.5 Tcf higher than second place Pennsylvania. With renewables, Texas can hang its hat on being the number one wind energy producer in the US, generating about 93 TWh of electricity from wind in 2020, according to the US Energy Information Administration (EIA)—almost triple that of second-place Iowa. Texas is number two on the list when it comes to solar capacity with around 15 MW as of the second quarter of 2022. California has just over 37 MW of capacity. With leadership positions in most commercial power-generation endeavors, Texas is behind the curve when it comes to exploiting geothermal energy. In fact, most of the nation has yet to embrace geothermal power, beyond some projects in California. According to the EIA, in 2020, consumption of renewable energy in the US grew for the fifth year in a row, reaching a record high of 11.6 quadrillion British thermal units (Btu), or 12% of total US energy consumption. While wind led the way with 26% of the total, only 2% was geothermal. All of this, however, could be changing. A recent report about the future of geothermal energy in Texas—a year-long multidisciplinary, cross-collaborative effort from researchers at five Texas universities, the University Lands Office, and the International Energy Agency—found the state’s geology presents a large and promising opportunity to develop geothermal resources. The 400-page report showed the amount of heat below state lands is many thousands of times larger than what would be needed to power not only Texas, but the world. The report was funded and supported by the Cynthia and George Mitchell Foundation, The Educational Foundation of America, and Project InnerSpace, a nonprofit organization focused on expanding the use of geothermal energy globally. The heat beneath Texas varies with the geography, but most of the population is at or near good temperature environments, particularly in the eastern, coastal, and far west regions. However, all areas are hot enough if you drill deep enough. It becomes a matter of continually improving economics of drilling to deeper depths. In general, the hotter the better, but a good minimum rule of thumb is about 150°C (or 300°F) as a target temperature (Fig. 1). Houston’s subsurface temperatures in that ballpark, for instance, can be found around 4.5 km to 5.0 km deep (2.5 to 3.0 miles). Under Austin, Texas, which is more centrally located in the state, those temperatures lurk deeper—6.0 to 9.0 km (3.5 to 5.5 miles). “Rock is a great heat battery and the upper 10 km, or 6 miles, of the earth’s crust as a battery of heat holds an estimated 1000s of years’ worth of our energy needs in the form of accessible heat energy,” said Ken Wisian, associate director of the Bureau of Economic Geology at University of Texas at Austin. “That’s an immense resource to tap and one that gets us all excited.” Additional oilfield drilling has helped better define the temperatures of the rock below the state. In a recent preliminary update of the assessment of temperatures below Texas, it was found that temperatures are generally 10 to 15% hotter than were previously thought. This is compared to studies conducted over a decade ago and marks a significant improvement in the area’s geothermal potential.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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".