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Record W2123169903 · doi:10.1306/06280504130

Geothermal gradient and temperature of hydrogen sulfide-bearing reservoirs, Alabama continental shelf

2005· article· en· W2123169903 on OpenAlexaff
S. Nagihara, Michael A. Smith

Bibliographic record

VenueAAPG Bulletin · 2005
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsCanada's Michael Smith Genome Sciences Centre
Fundersnot available
KeywordsGeologyGeothermal gradientHydrogen sulfideContinental shelfBearing (navigation)SulfideGeochemistryOceanographyEarth sciencePaleontologySulfur

Abstract

fetched live from OpenAlex

Abstract Present-day formation temperatures of the hydrogen sulfide (H2S)-bearing reservoirs in the James Limestone and the Norphlet Sandstone in the continental shelf off Alabama have been determined to be 138–149 and 191–217°C, respectively. Hydrogen sulfide gas in those reservoirs is generated by thermochemical sulfate reduction, a process sensitive to the ambient temperature. Bottom-hole temperature data from 135 wells in the offshore lease areas of Mobile, Main Pass East Addition, and the northern section of Viosca Knoll were examined in the estimation of formation temperatures. The bottom-hole temperatures were corrected for the thermal effect of drill-fluid circulation. Estimation of formation temperatures permitted the determination of the geothermal gradient representative for the study area, leading to a temperature range estimation for the H2S-bearing James and Norphlet reservoirs. Temperatures of offshore Norphlet reservoirs are higher than those reported previously for Norphlet reservoirs onshore. Temperatures of the James Limestone are close to the low-temperature limit for thermochemical sulfate reduction previously suggested.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.106
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2005
Admission routes1
Has abstractyes

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