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Record W2586508758 · doi:10.2118/184970-ms

Dynamic Behavior of Asphaltene Precipitation and Distribution Pattern in Carbonate Reservoirs During Solvent Injection: Pore-Scale Observations

2017· article· en· W2586508758 on OpenAlexafffundabout
Ali Telmadarreie, Japan Trivedi

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaCarbon Management CanadaAlberta Innovates - Technology Futures
KeywordsAsphalteneCarbonateSolventPrecipitationMicromodelDeposition (geology)Chemical engineeringHydrocarbonGeologyPetroleum engineeringPorosityPorous mediumMaterials scienceMineralogyChemistryOrganic chemistryGeotechnical engineeringStructural basinGeomorphology

Abstract

fetched live from OpenAlex

Abstract Precipitation and deposition of asphaltene in reservoir rock can cause formation damage and reduce the fluid mobility, resulting in significant loss of the hydrocarbon production. A detailed study on asphaltene deposition behavior in porous media improves the understanding of asphaltene-induced formation damage and provides the possible solutions for preventing and/or controlling formation damage. Carbonate reservoirs in Western Canadian Sedimentary Basin (WCSB) have several challenges for enhanced oil recovery process (i.e. heterogeneity, the high viscosity of oil). Asphaltene-induced formation damage makes it more difficult for any process to recover heavy oil from such complex reservoirs. For advancements in understanding asphaltene deposition in fractured carbonate formations, asphaltene deposition behavior was analyzed with the help of pore scale observation during hydrocarbon solvent injection. Three types of solvent (nC5, nC7, and nC12) were used for solvent injection for extra-heavy oil recovery (30,000 cp at 22 °C). A uniquely designed heterogeneous-fractured micromodel imitating the fractured carbonate reservoirs was used for pore scale observation. A high-quality camera along with a microscope was utilized to capture images. SEM analysis was also performed on precipitated asphaltene to visualize the difference in the structure of the asphaltenes precipitated with different solvents. Observation through this study revealed that besides the amount of asphaltene deposition, the distribution pattern of asphaltene deposition could also be different when using various types of solvent. By increasing carbon number of solvent from C5 to C12, the amount of precipitation decreased while the wider distribution of deposited asphaltene was observed in a fractured-heterogeneous porous media. Moreover, it was noted that asphaltene could be deposited in different shapes that may or may not block the pore throat. The dominant flow mechanism (either diffusion dominant or viscous dominant) will affect the shape of deposited asphaltene. Asphaltene can deposit perpendicular or parallel to the flow direction which is in the form of parallel rope-shape deposits. The perpendicular deposits (usually in dominant diffusion flow), mainly observed in small pore throat, can block the diffusion path. However, the parallel shape deposits (typically in viscous dominant flow) will not significantly impede the flow path. The results of this study improve our understanding of different aspects of asphaltene-induced formation damage in the fractured carbonate reservoirs such as; susceptible locations of asphaltene deposition, different types/shapes of deposition which may or may not result in pore blocking, and effect of flow behavior and heterogeneity on asphaltene deposition and formation damage. The more we know about the asphaltene deposition in such heterogeneous reservoirs, the better we can control the formation damage and increase the effectiveness of heavy oil recovery processes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.760

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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 designObservational
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

Citations3
Published2017
Admission routes3
Has abstractyes

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