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Record W4256016253 · doi:10.2118/2004-168

Application of In-Depth Gel Placement for Water and Carbon Dioxide Conformance Control in Carbonate Porous Media

2004· article· en· W4256016253 on OpenAlexaff
L. Taabbodi, K. Asghari

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCitationSupercritical carbon dioxideCarbonateCarbon dioxideEnhanced oil recoveryComputer scienceChemistryWorld Wide WebLibrary scienceChemical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of an investigation on the application of gel placement in an attempt to reduce the permeability of a carbonate porous medium to water and supercritical carbon dioxide, as encountered in the CO2 flooding of carbonate reservoirs. A 7500 ppm high molecular weight polyacrylamide polymer with 300 ppm chromium (III), as crosslinker, was used for this study. Since sodium lactate is commonly used for increasing gelation time at elevated temperatures, additional experiments were conducted by adding sodium lactate to the gel solution with a ratio of polymer to sodium lactate equal to one. The other gel system tested was composed of 5% low molecular weight polyacrylamide with a ratio of 1:12 chromium to polymer. Experiments were conducted at 1200 psi and 40 °, with and without the presence of residual oil in order to investigate any role the residual oil might play in the performance of gel. Performance and stability of above gel systems for reducing the permeability of the carbonate medium to the injected water and carbon dioxide was tested in a series of flow experiments by alternatively injecting several pore volumes of water and carbon dioxide into the porous media in several cycles. The porous medium used was crushed carbonate with initial permeability of over 10 Drcies. For all experiments the presence of Sor led to lower residual resistance factors (RRF). Nevertheless, RRFs were between 100 and few thousands for all experiments conducted. The results obtained are clear indication of the effectiveness of these gel systems for conformance control purposes during carbon dioxide flooding projects in carbonate reservoirs. Introduction Different techniques have been investigated for reducing channeling through fractures or high-permeability zones. In most of these methods, attempts have been aimed at reducing the excess water/gas production problems by reducing permeability to the flow. The polymer-gel technology has been applied successfully in many reservoirs, resulting in fractured sealing, water and gas shut-off, and permeability modification. 7 The objective of gel placement and similar blocking-agent treatments is to reduce channeling through fractures or highpermeability zones of oil reservoirs without significantly damaging hydrocarbon productivity and improve the overall oil recovery from the flooding process. The goal of gel treatments is to maximize gel penetration and permeability reduction in high permeable zones while minimizing gel penetration and permeability reduction in less permeable zones or hydrocarbon producing zones. 1 However, no treatment has been found that reduces water permeability without affecting oil permeability. 2 Gel treatments are one of the most aggressive types of conformance control or profile modification techniques. The main advantages of using gels over the other methods such as cements or mechanical plugs, is their flexibility for pumping without a work-over rig, high control of setting time, a deeper penetration into the formation, ease of cleaning, and an easy removal from the well-bore by water recirculation. 3,8. The purpose of the treatment is to block the strongest flow channels from the well, thus forcing subsequent fluid flow into tighter zones.

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 score0.309
Threshold uncertainty score0.980

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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