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Record W2916526586 · doi:10.2118/0118-0038-jpt

Technology Focus: EOR Performance and Modeling (January 2018)

2017· article· en· W2916526586 on OpenAlexaboutno aff
Omer Gurpinar

Bibliographic record

VenueJournal of Petroleum Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEnhanced oil recoveryPetroleum engineeringSteam-assisted gravity drainageOil in placeEnvironmental scienceEngineeringPetroleumGeologyOil sandsMaterials science

Abstract

fetched live from OpenAlex

Technology Focus Efforts for increasing recovery factors in all reservoir types continued with increasing momentum during the past year. Maturing fields combined with limited exploration forced operators to focus on recovering more from existing fields. Implementing enhanced oil recovery (EOR) in a depressed-oil-price environment is accepted as a challenge by our resilient industry, and, therefore, all aspects of EOR, including EOR agents, EOR physics, and EOR modeling and monitoring, improved further. We saw progress in all EOR schemes in the past year, from expanding-solvent steam-assisted gravity drainage (SAGD) to increasing recovery factors in unconventional fields in the US. While carbon-dioxide EOR continues to be used in conventional reservoirs, it is also becoming a desired EOR agent for unconventionals. EOR implementations in high-viscosity-oil reservoirs continued to see improvements with more chemistry, with heat, with improved reservoir characterization, and with advancements in SAGD implementation. Progress in chemical EOR, from polymer to alkaline/surfactant/polymer, has been significant. Not only are there new chemicals with improved recovery capabilities, but also the cost of the chemicals is being reduced continuously. All those factors led to more operators using chemical EOR. It is worth reiterating that increasing recovery factors in unconventionals will remain the top priority for many years to come. The next chapter of the exciting unconventionals journey will be more about EOR. EOR momentum on all fronts is reflected in the technical papers reviewed this year. This is the first time I have seen that all EOR schemes were covered. Half the technical papers were about fundamentals, and the other half were about field implementation. I found that to be the best indicator that EOR is becoming an integral part of managing oil fields. The selected papers provide a summary of some of the noteworthy advancements in EOR performance and modeling. Recommended additional reading at OnePetro: www.onepetro.org. SPE 187032 Nanoscale-Flow-Chip Platform for Laboratory Evaluation of Enhanced-Oil-Recovery Materials by Michael Engel, IBM Research, et al. SPE 185526 Surveillance and Initial Results of an Existing Polymer Flood: A Case History From the Rayoso Formation by L.A. Martino, YPF, et al. SPE 183165 Borehole Controlled-Source Electromagnetics for Hydrocarbon-Saturation Monitoring in the Bockstedt Oil Field, Onshore Northwest Germany by K. Tietze, GFZ Potsdam, et al. SPE 182669 Uncertainty Quantification for Foam Flooding in Fractured Carbonate Reservoirs by A. Almaqbali, Heriot-Watt University, et al. SPE 185002 ES-SAGD Relative Permeability as a Function of Temperature and Solvent Concentrations by M. Zeidani, University of Calgary, 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 score0.085
Threshold uncertainty score0.572

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 science0.0010.000
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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