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Record W2005529187 · doi:10.2118/2009-088

Simulation Study of Permeability Impairment Due to Asphaltene Deposition In One of the Iranian Oil Fractured Reservoirs

2009· article· en· W2005529187 on OpenAlexaff
Arash Mirzabozorg, Mohammad Bagheri, Riyaz Kharrat, Jalal Abedi, Cyrus Ghotbi

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphaltenePermeability (electromagnetism)Petroleum engineeringGeologyDeposition (geology)Relative permeabilityPetrologyGeotechnical engineeringPorosityChemistryGeomorphology

Abstract

fetched live from OpenAlex

Abstract This work concerns with simulation of permeability impairment as a result of asphaltene deposition at the field scale, which is a real situation of oil production from petroleum reservoirs. The involved processes are the initial precipitation of asphaltene from the reservoir fluid, the flocculation of these precipitates into larger particles and the deposition of these particles in porous media. The alteration of porosity and the impairment of permeability due to asphaltene deposition are also investigated. The case study reveals that the static surface deposition may occur in certain situations even without the fluid flowing. However, during flow, the dynamic surface deposition is predominant. The plugging deposition may occur under certain conditions, depending on properties of the fluid, porous media, asphaltene precipitates, injection rate and pressure conditions. In addition, the effect of fracture properties on asphaltene deposition at reservoir condition was investigated. The developed dual-porosity and dual permeability simulation model also has been used for some sensitivity analysis of parameters affecting asphaltene deposition in reservoir. The results of sensitivity analysis showed that dynamic and static parameters of asphaltene play crucial role in the field performance and formation damage. Also, the results confirmed that, the permeability reduction due to asphaltene deposition in the fractures is more considerable than in the matrix media. Introduction Asphaltenes are complex molecules, which are defined as the nonhydrocarbon molecules that are soluble in benzene but insoluble in low-molecular-weight n-alkanes, and can be derived from petroleum oil or shale oil [1]. Asphaltene precipitation from reservoir fluids during oil production is a serious problem, because it can result in plugging of the formation, wellbore and production facilities. Asphaltene precipitation can occur during primary depletion of highly undersaturated reservoirs or during hydrocarbon gas or CO2 injection [2–6]. The region where asphaltene precipitation occurs is bound by the Asphaltene Deposition Envelope (ADE) [5]. Figure 1 shows an ADE and PX saturation pressure curve for typical oil. Many thermodynamic models that describe the phase behavior of asphaltene precipitation have been reported in the literature. These include the use of a liquid solubility model [4], a thermodynamic colloidal model [7], a thermodynamic micellization model [8], a variation of a model for wax [9, 10], or a pure solid model [11, 12]. It must be pointed out that asphaltene precipitation is a result of changing in pressure, temperature and composition which leads to changes in properties such as wettability and permeability in porous media [13, 14]. In this study the effect of asphaltene precipitation and deposition in a naturally fractured Iranian oil reservoir performance, Sarvak formation, has been examined. Warren & Root dual porosity model as well as solid thermodynamic model was used for developing of compositional simulation model, and natural production from Sarvak formation was simulated. Compositional Reservoir Model Incorporating An equation of state (EOS) compositional reservoir simulator has been modified to incorporate asphaltene precipitation, flocculation and deposition. The fluid phase behavior in the simulator is represented with a standard cubic EOS. The following sections describe the solid phase behavior models.

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 score0.591
Threshold uncertainty score0.961

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 science0.0010.000
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 head0.020
GPT teacher head0.264
Teacher spread0.244 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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