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Record W2006142156 · doi:10.2118/104526-ms

Revitalizing the Richard King Field: Using New Technology in a Cost-Effective Manner on Mature Fields

2006· article· en· W2006142156 on OpenAlex
Jeff Swanson, Greg Stevens, Rod Hall

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Eastern Regional Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsField (mathematics)Property (philosophy)StratigraphyValue (mathematics)Computer scienceEngineeringData scienceGeologyPaleontologyMathematicsPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract What else can be done to an old field where past operators have tried just about everything including 3-D seismic? If there is anything else, would it add quantifiable value to the property? As an acquirer of mature properties, this common challenge for Durango Resources Corp. ("Durango") was dealt with by combining old-fashioned geological and engineering methods with new technology. Some of these methods included thorough data gathering and detailed analysis of the stratigraphy and depositional environment of all well logs. Completing a detailed field study combined with new technology has resulted in new life for the old field and has added definable value to the property.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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