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Record W2492206253 · doi:10.2118/2004-236

Water Permeability Reduction Under Flow-Induced Polymer Adsorption

2004· article· en· W2492206253 on OpenAlexafffund
A.L. Ogunberu, K. Asghari

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
FundersUniversity of Regina
KeywordsPermeability (electromagnetism)AdsorptionReduction (mathematics)PolymerFlow (mathematics)Relative permeabilityPetroleum engineeringMaterials scienceChemical engineeringChemistryPorosityComposite materialMechanicsGeologyMembraneEngineeringMathematicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The influence of induced polymer adsorption for reducing the effective permeability to water in reservoirs is investigated. Previous studies on polymer adsorption in porous media have shown that static adsorption regime exists at low shear rate of injection. This results in thin polymer layer whose capability to reduce water permeability is marginal. However, polymer injection at increasing shear rates has revealed an increase in the adsorbed polymer layer and consequently, improved water permeability reduction. In this work, experimental results from sandpacks were presented to show that at increased shear rates, there is improvement in the adsorbed polymer layer. This phenomenon is known as "flow-induced adsorption". The experiments indicate that above a critical shear rate, there is a shift in permeability-reduction-mechanism from static adsorption to flow-induced adsorption, necessitating a sharp increase in adsorbed polymer layer. All the experimental results revealed that the critical shear rate for this polymer is about 300s-1 in the absence of mechanical degradation. This critical shear rate defines the optimal rate of polymer injection for better economic viability of the process. Introduction and Background Increased water production with produced oil is a growing concern in the petroleum industry. The breakthrough of either formation or injected water, results in accelerated decline in oil production, increased operational cost of pumping, treatment and disposal of produced water. The need to curtail excess water production without affecting oil production has led to the use of polymers to reduce the permeability to water much more than the permeability to oil. This is essential in wells where water and oil is being produced from the same zone; hence, the water-bearing zones cannot easily be isolated. In many cases, direct injection of polymers in production wells has proven to be an efficient method to prevent excessive water production (1)(2). To understand the mechanisms that enable water permeability reduction, many experimental studies have investigated the resulting effect of polymer injection on two-phase flow in porous media (3)(4)(5)(6). All the studies indicate a selective action of the polymer with a significant reduction in the relative permeability to water with respect to the relative permeability to oil. Based on the fact that the wall effect is the dominant action of the polymer, polymer adsorption thus, plays a significant role in relative permeability modification, resulting in permeability reduction of porous media. The objective of polymer adsorption in production wells is to reduce water production without damaging oil productivity. This is critical to the success of near-wellbore conformance treatments in production wells if thehydrocarbon-productive zones cannot be protected during placement (7). Results of field treatments have varied widely, but often no obvious reason can be given for the success or failure of the treatment. The extent to which relative permeability is modified is governed by the degree of polymer adsorption within the porous medium under consideration. Zitha et al. (1995) reported that polymers are widely used for nearwellbore conformance control treatments to correct the permeability contrast between the layers.

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 categoriesInsufficient payload (model declined to judge)
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 score0.166
Threshold uncertainty score1.000

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)0.0010.000

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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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

Citations17
Published2004
Admission routes2
Has abstractyes

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