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Record W2996780284 · doi:10.2118/0120-0048-jpt

Technology Focus: EOR Modeling (January 2020)

2019· article· en· W2996780284 on OpenAlexaffabout
Subodh Gupta

Bibliographic record

VenueJournal of Petroleum Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsEnhanced oil recoveryPetroleum engineeringPulmonary surfactantViscosityWettingViscous fingeringDisplacement (psychology)Chemical engineeringOil in placeMaterials scienceProcess engineeringChemistryEnvironmental scienceGeologyPetroleumEngineeringOrganic chemistryComposite materialPorous medium

Abstract

fetched live from OpenAlex

Technology Focus The bulk of the literature on enhanced oil recovery (EOR) from the past year has been devoted to an improved understanding of trends started more than a decade ago with physical and numerical modeling. A quick review of the basics will be helpful for putting these developments in perspective. The concept of displacement in an EOR method requires bringing the mobility ratio of displacing fluid to that of displaced oil under unity [Kw µ o / Ko µw less than 1; Ko, Kw being relative permeabilities of displaced oil and displacing fluid (e.g., water), respectively, and µ o, µw being their respective viscosities], and increasing sweep efficiency (Es) of the displacement process. Various established EOR methods try to accomplish this in different ways, depending on the formation and oil types, aiming to keep costs and environmental impact down while obtaining high and quick recovery. Thus, polymers or foams have been used traditionally to increase viscosity of displacing fluids (µw). To reduce Kw/Ko, surfactant; alkaline; microbes, which create in-situ surfactants; or low-salinity water, which changes wettability to water, were used. Steam or hot water have also been used to reduce both Kw and µo. Miscible gas or solvent displacements were used to attack µo. Quite frequently, a combination of these mechanisms has been applied, alkaline/surfactant/ polymer being a good example. Similarly, to address improvement in sweep, blocking agents such as polymers, microgels, or microbes, or a combination of a couple of these mechanisms, have been applied. While the underlying mechanisms remain the same, major developments in EOR in recent years center on the use of engineered water or nanoparticles to reduce Kw, on the experimental development of chemicals that are less susceptible to high-temperature and high-salinity environments, or on the use of novel computational techniques and machine learning to design and monitor the flood operations better. The literature of the last year is an important reflection of that evolution, as the selected papers reflect. Recommended additional reading at OnePetro: www.onepetro.org. SPE 195553 Investigation of Pore-Scale Mechanisms of Microbial Enhanced Oil Recovery Using Microfluidics by Calvin Gaol, Clausthal University of Technology, et al. SPE 192110 Polymers and Their Limits in Temperature, Salinity, and Hardness: Theory and Practice by Eric Delamaide, IFP Technologies Canada, et al. SPE 192651 Performance Evaluation and Field Trial of Self-Adaptive Microgel Flooding Technology by Zhe Sun, China National Offshore Oil Corporation, 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.003
GPT teacher head0.203
Teacher spread0.200 · 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.

Study designBench or experimental
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
Published2019
Admission routes2
Has abstractyes

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