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Record W2009329472 · doi:10.2118/09-02-29

Influences of Different Modes of Reservoir Heterogeneity on Performance and Oil Recovery of Carbon Dioxide Miscible Flooding

2009· article· en· W2009329472 on OpenAlexaboutno aff
S. A. Shedid

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringPermeability (electromagnetism)GeologySupercritical fluidEnhanced oil recoveryPetroleum reservoirGeotechnical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Reservoir heterogeneity represents one of the most dominant factors affecting the performance of CO2 miscible flooding and its expected oil recovery. The main goal of this study is to investigate the influence of different modes of reservoir heterogeneity on oil recovery by supercritical CO2 miscible flooding. The investigated heterogeneity modes include:different single fractured reservoirs of different inclination angles,different permeability configurations of layered reservoirs, andthe sequence of permeability distributions in composite reservoirs. Complete reservoir rock and oil compositional analyses were performed. The minimum miscibility pressure (MMP) of oil-CO2 was mathematically calculated using several empirical correlations and determined experimentally using slim tube tests. The core flood tests were achieved using actual fluids injected through 12 actual reservoir rock samples. Of these, four samples were of different fracturing angles as single fractured reservoirs, four samples were of different permeability configurations as layered rocks and four samples represented composite reservoirs. The slug size of supercritical CO2 was optimized to be 0.15 PV, injected and chased by actual reservoir brine through these different simulated modes of reservoir heterogeneity. The results indicated that all different modes of reservoir rock heterogeneity have a crucial influence on oil recovery by CO2 miscible flooding in carbonate oil reservoirs. Of note, unfractured reservoirs produced higher oil recovery by CO2 miscible flooding than single fractured ones. An oil reservoir with a 30 degree inclination angle of single fracture produced the highest oil recovery, whereas, fractured rocks with a 45 degree fracture produced the minimum oil recovery in this category. The rock permeability sequences of medium-low-high (MLH) mode for composite reservoirs and medium-high-low (MHL) distribution mode for layered reservoirs are highly recommended for CO2 miscible flooding. The results have proven the suitability of the CO2 application for layered and composite heterogeneous carbonate reservoirs, however, it does not recommend this EOR process for single fractured reservoirs. The results have also shown a real impact on oil recovery of the reservoir heterogeneity mode prevailing in the reservoir under development by this EOR process. Introduction The major applications of active carbon dioxide flooding projects were found in the U.S. Permian Basin, located in West Texas and Eastern New Mexico. Other projects were also active in Colorado, Louisiana, Mississippi, Oklahoma, Utah, Wyoming, and in Canada, Turkey (Bati Raman), Trinidad and Hungary (Budafa). The number of CO2 projects have increased in the U.S. and worldwide over the last number of years(1–3). Reservoir characterization has been considered one of the most important components of reservoir development because it provides a better description and distinguishes essential features of reservoir heterogeneities affecting fluid flow in pay formations. It has been proven that a better reservoir description and/or characterization reduces the amount of oil left in oil reservoirs. The main purpose of reservoir characterization is to outline and specifically integrate different forms of heterogeneities of the reservoir oil. Forgotson(4) defined reservoir characterization as the quantitative description of the physical and chemical properties of the porous medium and its contained fluids.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
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 head0.009
GPT teacher head0.210
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 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

Citations38
Published2009
Admission routes1
Has abstractyes

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