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Record W4255491136 · doi:10.2118/2007-032

Clarifications on Oil/Heavy Oil Recovery Under Ultrasonic Radiation Through Core and 2D Visualization Experiments

2007· article· en· W4255491136 on OpenAlexafffundabout
Kamyar Naderi, Tayfun Babadagli

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVisualizationCore (optical fiber)Ultrasonic sensorPetroleum engineeringEnvironmental scienceComputer scienceMaterials scienceAcousticsGeologyPhysicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Our previous research on the effects of ultrasonic waves on oil recovery conducted at the University of Alberta had showed that capillarity and interfacial tension (IFT) might be responsible for the observed improvements in incremental oil recovery. To investigate this further, Hele-Shaw type experiments had been performed with the same fluid pairs, and significant alterations in the morphology of the fingers with ultrasonic waves were observed. Although the results seem encouraging, questions about the mechanism and effective parameters causing additional recovery still remain. To analyze the influence of parameters other than IFT and capillary forces, we conducted capillary imbibition experiments on cylindrical Berea sandstone core samples under ultrasonic radiation in this paper. Through this experimental scheme, we focused on (a) the effect of initial water saturation for different wettability rocks, (b) oil viscosity, and (c) matrix wettability. The cores were placed into imbibition cells where they contacted with aqueous phase. Every experiment was conducted with and without ultrasonic radiation for comparison. Different intensities of ultrasonic waves were tested as well. To profoundly investigate the acoustic interaction between rock and fluid, we further performed some visualization experiments. We used 2-D glass bead models to clarify the effects of ultrasonic waves on oil displacement process for different oil viscosities and matrix wettability through comparative analysis. The qualitative and quantitative observations and analyses are expected to shed light on the further investigations in the use of in-situ recovery of oil/heavy-oil as well as surface extraction. Introduction Primary production of petroleum by natural reservoir energy does not produce a large fraction of original oil in place. To increase the oil recovery from the reservoirs after conventional secondary recovery, enhanced oil recovery (EOR) techniques such as thermal, chemical and gas injection, should be implemented. In addition to those traditional EOR techniques, unconventional EOR methods have received a great deal of attention, especially after the recent increase in oil prices. Acoustic energy was considered as one of those unconventional EOR methods. Studies have been conducted to understand the effects of acoustic energy on oil recovery over the last four decades. Duhon and Campbell1 performed waterflood tests through cores under ultrasonic energy and showed that the ultrasonic energy improved the oil recovery and displacement efficiency in the cores. Beresnev and Johnson2 reported a critical analysis of the works done in this area by the early 1990's and provided a comprehensive review of the seismic and ultrasonic stimulation studies. They concluded that the elastic wave and seismic excitations to porous media affect permeability and production rate in most cases. Kuznetsov et al. 3 reviewed seismic techniques for enhanced oil recovery. They performed capillary pressure measurements with and without vibration and observed an increase in oil/water relative permeabilities and also oil recovery after elastic vibration. They concluded that this increase is due to fines removal by vibration. Roberts et al. 4 applied mechanical stresses to rock samples which were placed inside a core holder.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.286
Teacher spread0.259 · 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 designTheoretical or conceptual
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

Citations4
Published2007
Admission routes3
Has abstractyes

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