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Record W2091791153 · doi:10.2118/07-02-04

Improving Gel Performance in Fractures: Chromium Pre-Flush and Overload

2007· article· en· W2091791153 on OpenAlexaff
Ryan R. Wilton, K. Asghari

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of ReginaSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsChromiumPenetration (warfare)Silica gelPolyacrylamideMaterials sciencePetroleum engineeringChemistryGeologyComposite materialChemical engineeringEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Abstract High water cuts during waterflood operations are a major problem encountered in mature reservoirs. Areas of the reservoir that are fractured, either naturally or hydraulically, are excellent pathways for floodwater to bypass oil-bearing pore spaces. Gel placement within fractured zones of the reservoir is a technique that has been employed to decrease water production. In order to utilize this technique more effectively, the improvement of gel placement and its performance within fractures must be investigated. For the purposes of this study, two experimental setups are developed. Initially an acrylic fracture model is developed in order to obtain qualitative information about flood fluid penetration into the placed gel. The rupture pressure of the HPAM-Cr (III) [hydrolyzed polyacrylamide-chromium (III) acetate] gel system is observed for 1x, 2x?and 3x?gel systems (multiplier refers to chromium concentration) within the fractures. The rupture pressures observed are generally higher for gel systems with greater chromium concentration. The acrylic setup also allows for visual observation of the gel's performance and behaviour during water injection. Water penetration is dominated by one major channel. Smaller channels are often observed to either branch off from the dominant channel or smaller side channels would connect and join the flow path of the major channel. Secondly, Berea sandstone slabs are cut and an experimental setup is built in order to study two main mechanisms for improved gel placement. The application of Cr (III) acetate pre-flush and overload are investigated in order to determine their effect on gel performance within fractures. Both techniques compensate for the amount of chromium lost to the matrix via molecular diffusion and the integrity of the gel is maintained. This allows for significant fracture blockage without having to place performed gel or placing the gel ant with leak-off in order to achieve a stable gel. Introduction Many reservoirs currently under production suffer from excessive water production. Water could be supplied either by a natural water source (e.g. aquifer) and/or because of waterflooding. Waterflooding is normally used in order to displace any remaining oil in the reservoir matrix after the primary stages of oil production. Presence of high permeability zones in the reservoir provides pathways for water to bypass oil-bearing regions and break through into the production wells. Areas of the reservoir that are fractured, either naturally or hydraulically, are excellent pathways for floodwater to penetrate and consequently bypass oil-bearing pore spaces. Blocking the high permeable thief zones and diverting water towards the unswept regions of the reservoir has been proposed and used by oil producers as a viable remedy for this problem. In-depth gel placement is the most widely used technique for blocking high permeable zones of reservoirs. This technique has been implemented through many field trials around the world and researchers have successfully determined the mechanisms governing this process in porous media. Although gel placement in fractures is a common practice in the field, the mechanisms controlling the performance of this technique in fractures are not well understood. This has created a challenging opportunity for researchers to study the detailed mechanisms of gel placement and performance in fractures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
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 head0.002
GPT teacher head0.187
Teacher spread0.185 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

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