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Record W4367353958 · doi:10.2118/0323-0030-jpt

Geothermal Potential Runs Hot in Texas

2023· article· en· W4367353958 on OpenAlexaboutno aff
Blake Wright

Bibliographic record

VenueJournal of Petroleum Technology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsGeothermal gradientRenewable energyQuarter (Canadian coin)Geothermal energyElectricity generationEnvironmental scienceAgricultural economicsEngineeringGeologyGeographyPower (physics)ArchaeologyEconomicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.208
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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