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Record W1977837024 · doi:10.2118/104596-ms

Identification and Analysis of Fields for Waterflood-Enhanced Recovery Efforts

2006· article· en· W1977837024 on OpenAlex
Rod Hall, Dwight Ross, Greg Stevens, Steve Muehlberger

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
KeywordsPetroleum engineeringOil productionGeologyAnticlineConglomerateProduction (economics)Identification (biology)Oil fieldPetroleumField (mathematics)Mining engineeringStructural basinPaleontology

Abstract

fetched live from OpenAlex

Abstract Changing economic conditions allow for a new examination of mature and abandoned fields to identify oil that is now economically recoverable (reserves). In mature oil producing basins there are many inactive or limited stripper production fields, all potential waterflood candidates. We present a methodology of identifying such fields, quantifying the incremental waterflood production, and creating a field development plan. Production forecasts are generated in a timely and cost effective manner for development scenarios with Time Dynamic Volumetric Balancing methods. The field reviewed and used as an example is the Belcherville Field. It is a Caddo Conglomerate, located in Montague County, Texas, discovered in 1946. Developed in the 1950's, 9 wells produced 3 mmbo of primary production before being abandoned in 1967. Production was from the channel deposits of the Caddo formation that are draped over a gentle anticline. A comparative study of 7 other fields in the same trend indicates waterflooding can recover and additional 84% of primary recoverable oil.

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 score0.342
Threshold uncertainty score0.341

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 head0.016
GPT teacher head0.255
Teacher spread0.240 · 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