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Record W1981221109 · doi:10.2118/94669-ms

New Life to an Old Field

2005· article· en· W1981221109 on OpenAlexaff
J. S. Swanson, Donald C. Swanson, M. J. Jarvis, R. Hall

Bibliographic record

VenueSPE Hydrocarbon Economics and Evaluation Symposium · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsPetroleum engineeringWorkoverWorkflowEnvironmental geologyComputer scienceFlood mythGeobiologyField (mathematics)Production (economics)Well stimulationGeologyOperator (biology)Reservoir modelingReservoir engineeringRegional geologyMetamorphic petrologyHydrogeologyPetroleumGeotechnical engineeringMathematicsPaleontologyGeographyArchaeology

Abstract

fetched live from OpenAlex

Abstract Overview and Challenge The East Tunstill Field is a tight, under-saturated sandstone reservoir with a 9% recovery factor on primary depletion. Marginal current production and a previous relatively unsuccessful waterflood did not present the potential value of the field. In order to present this value, within a limited budget, new procedures and methods were necessary. A project workflow was designed to handle the field characteristics of a high number of wells and many years of production, resulting in a large amount of data that could be used to achieve a better analysis. The operator, Penn Virginia ("PVA"), believed an improved reservoir description applied with simulation methods could yield a better understanding of previous drainage, fluid movement, and the volume and location of the remaining oil. Additionally, the operator decided to utilize a new volumetric balancing technology that allowed a fast (15 minutes) analysis of various waterflood injection/producer patterns to derive the most economic and efficient flood pattern and thereby highlighting its 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.619

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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 designSimulation or modeling
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

Citations0
Published2005
Admission routes1
Has abstractyes

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