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Record W4399269643 · doi:10.2118/0624-0078-jpt

Technology Focus: EOR Operations (June 2024)

2024· article· en· W4399269643 on OpenAlexaboutno aff
Kristian Mogensen

Bibliographic record

VenueJournal of Petroleum Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFocus (optics)Petroleum engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

With 2024 marking the 75th anniversary of JPT, several articles published earlier this year in the magazine reflected on how our industry has evolved over the last 25 years thanks to technology breakthroughs and entrepreneurial mindsets. Some of the fields that came into production at the turn of the century are now potential candidates for enhanced oil recovery (EOR), and current oil price levels ought to make more EOR projects economically attractive. The EOR scene has evolved as well. Low-salinity waterflooding, originally thought to apply only in certain sandstone reservoirs, appears to be able to unlock additional reserves also for some carbonate formations, although the fundamental mechanisms are different. Within chemical EOR, we now have polymers and surfactants that can tolerate higher salinity and higher temperatures. Clear synergistic effects exist with low-salinity water, which can lower the required dosage of polymer and reduce adsorption in carbonates. Advancement in chemical formulations also can benefit mobility control in gasflooding applications through foam generation, with apparent synergistic effects when adding nanoparticles. There is no shortage of human ingenuity, and people are seeking inspiration from other industries such as biotech, nanotech, aerospace, and others. Yet, despite these advancements, many factors, such as suboptimal well placement, inadequate completion for inflow control, aging facilities, and the lower cost of infill drilling, can dampen the initial enthusiasm toward EOR. This is most certainly the case in an offshore environment where we have yet to see CO2 injection beyond the piloting phase. I remain optimistic that EOR has a role to play in maintaining the supply of hydrocarbons during the energy diversification era that we have just entered. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214825 Using Natural Gas Liquid for EOR in a Huff‘N’Puff Process: A Feasibility Study by Amin Alinejad, University of Alberta, et al. SPE 216582 Alkali Polymer Flooding: Tackling Risks and Challenges From Feasibility Study to Pilot by A. Janczak, OMV, et al. IPTC 24484 Comprehensive Piloting Strategy To Derisk First CO2 EOR Development in Sultanate of Oman by Ramez Nasralla, Petroleum Development Oman OTC 34911 Pilot Tests of Steamflooding After Cyclic Steam‑N2‑CO2 Stimulation in Bohai Offshore With Large Well Spacing by Dong Liu, CNOOC, 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: none
Teacher disagreement score0.637
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.008
GPT teacher head0.266
Teacher spread0.258 · 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
Published2024
Admission routes1
Has abstractyes

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