MétaCan
Menu
Back to cohort
Record W3033687815 · doi:10.2118/200574-ms

Dynamic Immiscible Flood Monitoring in Sand Packs Via Frequency Domain Reflectometry

2020· article· en· W3033687815 on OpenAlexaff
Ilia Kuznetcov, Apostolos Kantzas, Steven L. Bryant

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReflectometryPetroleum engineeringFrequency domainTime domainSaturation (graph theory)GeologyEconomic geologyPorous mediumEnvironmental geologyAcousticsEnvironmental sciencePorosityGeotechnical engineeringHydrogeologyComputer science

Abstract

fetched live from OpenAlex

Abstract Dynamic saturation distribution of fluids in oil reservoirs is arguably the most important piece of information petroleum engineers and geophysicists need. It allows them to better characterize oil fields, choose right enhanced oil recovery techniques and make accurate recovery forecasts. The main objective of the present paper is to develop and test a novel non-intrusive technology that enables real time in-situ monitoring of fluid saturations in porous media during immiscible floods. To achieve this goal we designed, constructed and commissioned a high pressure (up to 25MPa) and temperature (up to 200°C) core-holder with an antenna that allows for low power electromagnetic sweeps in the radio frequency range. A novel inversion algorithm was created and incorporated with our core-holder-pump system that enables dynamic acquisition of the oil and water saturations. Experiments were conducted through sand packs saturated with water and then displaced with oils of varying viscosity (drainage) followed by water flooding (imbibition). Reflection and transmission coefficients in the frequency domain were measured while flooding using a commercial vector network analyzer connected to the core holder. Recorded frequency data was processed with the novel high-resolution inversion technique to obtain impulse reflection and transmission responses in the time domain. These responses were further normalized to dynamically track the water-oil volumetric saturations within the porous media. Every displacement experiment has been performed in both vertical and horizontal positions to emulate water-oil override and underride scenarios. Material balance calculations were performed to validate fluid saturation profiles for all imbibition and drainage experiments every 5 % of the pore volume of the fluids injected. Frequency domain reflection and transmission electromagnetic responses were measured every 1 % of the fluid injected. Material balance was used to validate the measured saturation profiles. The goodness of fit was calculated between these two independent measurements for every flood experiment performed. The mean-square error was calculated to be around 1.26% of the total pore volume on average. Late breakthroughs and piston-like displacement were observed in the floods with favorable mobility ratios. In contrast, much earlier breakthroughs have been registered in all the experiments with unfavorable mobility ratios due to fingering. All our observations are in agreement with the current theory of immiscible displacement in porous media. The novel automated system paired with the inversion algorithm were developed to allow for virtually real time monitoring of the fluid saturations during imbibition and drainage displacement cycles. Our technology is shown to be a promising candidate to compliment resistive logging measurements in the field.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.264
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicGeophysical and Geoelectrical MethodsFrench-language works237,207