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Record W2969311759 · doi:10.1190/int-2018-0204.1

Defining a supergiant petroleum system in Brazil’s Santos Basin with multidisciplinary methods: One template for exploration success

2019· article· en· W2969311759 on OpenAlexaff
William Dickson, Craig Schiefelbein, Mark E. Odegard

Bibliographic record

VenueInterpretation · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsDignitas International
Fundersnot available
KeywordsStructural basinGeologyPaleontologySource rockPetroleumMultidisciplinary approachGeographyPolitical science

Abstract

fetched live from OpenAlex

Abstract Is there a petroleum system here? How extensive and effective is it? How is it defined? Although we had presented a 2005 AAPG poster to address these questions, we now have performed an exploration look-back or case study demonstrating basin-wide presalt charge across Brazil’s Santos Basin. Santos has been a disappointing gas province with meager results compared to the adjacent Campos Basin for the past two decades. We have reviewed and expanded presentations at AAPG and SEG conferences from 1998 to 2005, which were followed 17 months later by the supergiant Tupi discovery, now Lula Field. We document the progression of analyses and revision of interpretations as a case history for multidisciplinary work in a frontier region with, at the time, scant coverage of key data types. Despite our access to a broad range of material (oil and cuttings samples, piston core extracts, slicks analysis, regional seismic lines, potential field coverages, and published literature), only a handful of point samples directly fitted our hypothesis of a mature oil-prone presalt source, supported by our inference, from leakage at the basin margins, of basin-wide migration and charge. Although the volumes of data collected across the Santos Basin are orders of magnitude larger in 2019, with a concomitant improvement in understanding the petroleum system and overall basin evolution, we take pains to limit our focus to what was known as of mid-2005 (although perhaps published later), which still sufficed to point to the future success. Because the source presence and effectiveness are the first consideration in evaluating frontier basins, our methodology provides one template for understanding a key geologic risk. We emphasize the importance of careful screening of inputs when information is scant and thus erroneous inferences are easily reached, with the need to take an exploration inference wherever data, once cross-validated, direct the explorer.

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)0.024
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.005
Science and technology studies0.0060.019
Scholarly communication0.0110.012
Open science0.0030.012
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.274
Teacher spread0.256 · 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 designNot applicable
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

Citations11
Published2019
Admission routes1
Has abstractyes

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