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Record W2222865514 · doi:10.2118/0109-0046-jpt

Technology Focus: EOR Performance and Modeling (January 2009)

2009· article· en· W2222865514 on OpenAlexaboutno aff
Baojun Bai

Bibliographic record

VenueJournal of Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEnhanced oil recoveryPetroleum engineeringOil in placePulmonary surfactantSteam injectionEnvironmental scienceCarbonateGeologyChemistryChemical engineeringEngineeringPetroleum

Abstract

fetched live from OpenAlex

Technology Focus Enhanced-oil-recovery (EOR) technologies have drawn increased interested because of the decreasing number of new-field discoveries, increasing number of maturing fields worldwide, and higher oil price, with the oil price peaking above USD 145/bbl in July 2008. It is more important than ever to understand lessons learned from past EOR applications and to develop new technologies and methods. More than 200 papers on EOR were presented at various SPE conferences in 2008. The EOR feature is split this year: EOR Performance and Modeling herein and EOR Operations in June. I categorized EOR papers for this feature into the following seven technologies.Wettability-alteration methods: surfactant imbibition for carbonate reservoirs, low-salinity waterflooding for sandstone reservoirs, and "smart water" for carbonate reservoirs.Chemical-flooding methods for conventional light oil: polymer flooding, polymer/surfactant flooding, alkaline/surfactant/polymer flooding, and surfactant-micelle flooding.EOR methods for heavy oil: steamflooding, in-situ combustion, steam-assisted gravity drainage, surfactant-alternating-gas, gas-assisted gravity drainage, thermal-assisted/gas-assisted gravity drainage, and alkaline/surfactant/polymer and alkali/surfactant flooding.Gas injection and CO2 sequestration: CO2 miscible and immiscible flooding, water-alternating-gas injection, and CO2 EOR and sequestration.Conformance control: gel treatments and foam flooding.Diagnosis and evaluation of EOR applications: seismic method, tracer injection, and mathematical modeling for reservoir heterogeneity and for induced fractures caused by water injection.Other EOR methods: in-situ seismic stimulation, microbial EOR, electrical EOR for heavy oil, and others. The papers that I selected for this feature fall into the first four categories. EOR Performance and Modeling additional reading available at the SPE eLibrary: www.spe.org SPE 113304 • "Analysis of the Wettability-Alteration Process During Seawater Imbibition Into Preferentially Oil-Wet Chalk Cores" by L. Yu, University of Stavanger, et al. SPE 113417 • "Residual-Oil Saturation from Polymer Floods: Laboratory Measurements and Theoretical Interpretation" by Chun Huh, SPE, University of Texas at Austin, et al. SPE 113985 • "Evaluation of Manson Lease Oil Field for Improved-Oil-Recovery Process" by J.S. Tsau, University of Kansas, et al. SPE 117607 • "Experimental Analysis of CO2-Sequestration Efficiency During Oil Recovery in Naturally Fractured Reservoirs" by J.J. Trivedi, University of Alberta, et al. SPE 111403 • "EOR Methods To Enhance Gas/Oil Gravity Drainage" by P.M. Boerrigter, Shell, et al. SPE 115204 • "Dynamic Induced Fractures in Waterfloods and EOR" by P.J. van den Hoek, Shell, et al.

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 score0.137
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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