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Record W4395008247 · doi:10.2118/218158-ms

Evaluation of Electroassisted Carbonated Water Injection (ECWI) in a Tight Reservoir: Outstanding Performance of Enhancing Oil Recovery and CO2 Storage Capacity

2024· article· en· W4395008247 on OpenAlexaff
Zejiang Jia, Zhengfu Ning, Fangtao Lyu, Daoyong Yang

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2024
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsEnhanced oil recoveryPetroleum engineeringWater injection (oil production)Environmental scienceGeology

Abstract

fetched live from OpenAlex

Summary Traditionally, carbonated water injection (CWI) finds its low injectivity in a tight reservoir with a low efficiency and slow effectiveness. By combining a direct current (DC) electric field with the CWI, i.e., the electroassisted CWI (ECWI), we conducted a series of experiments to evaluate the ECWI performance in a tight reservoir and identify the key underlying recovery mechanisms. Experimental results show that early adopting the ECWI in a tight sandstone reservoir results in the highest oil recovery up to 61.9%, compared to those of 51.2% for the CWI and 41.3% for the conventional waterflooding. During an ECWI process, a voltage of 10 V achieves the highest oil recovery, but that of 15 V has the best water injectivity and CO2 storage capacity. It is found that the underlying recovery mechanisms result from both electroosmosis and enhancement of carbonized water-rock reactions induced by a DC electric field. For a tight reservoir, the ECWI has the advantages of significantly increasing water injectivity, oil production rate, and CO2 storage capacity. By introducing two new indicators to respectively evaluate the water injection performance and energy consumption, the ECWI is found to perform moderately well at a low voltage in an energy-saving and financially viable manner.

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)0.002
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 score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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
Published2024
Admission routes1
Has abstractyes

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