Modeling and Optimization of Asphaltene Deposition in Porous Media Using Genetic Algorithm Technique
Bibliographic record
Abstract
Modeling and Optimization of Asphaltene Deposition in Porous Media Using Genetic Algorithm Technique V.. Hematfar; V.. Hematfar Petroleum University of Technology Search for other works by this author on: This Site Google Scholar R.. Kharrat; R.. Kharrat Petroleum University of Technology Search for other works by this author on: This Site Google Scholar M. H. Ghazanfari; M. H. Ghazanfari Sharif University of Technology Search for other works by this author on: This Site Google Scholar M. B. Bagheri M. B. Bagheri Sharif University of Technology Search for other works by this author on: This Site Google Scholar Paper presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, June 2010. Paper Number: SPE-130455-MS https://doi.org/10.2118/130455-MS Published: June 08 2010 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Hematfar, V.. , Kharrat, R.. , Ghazanfari, M. H., and M. B. Bagheri. "Modeling and Optimization of Asphaltene Deposition in Porous Media Using Genetic Algorithm Technique." Paper presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, June 2010. doi: https://doi.org/10.2118/130455-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Oil and Gas Conference and Exhibition in China Search Advanced Search Abstract Different models have been proposed for deposition of asphaltene on reservoir rocks that due to complexity of asphaltene nature, most of them have not been productive. Here, a reliable model is proposed which despite of previous models, considers the change in asphaltene saturation in the core. The obtained experimental data in the laboratory was used for model validation. In this work, a series of core flooding tests was carried out in presence of connate water at different solvent-oil volume ratios. Pressure drop was measured at three different terminals along the core. The obtained experimental data as well as mass balance equations, momentum equation, asphaltene deposition and permeability reduction models were employed in an iterative scheme to simulate the deposition process. Genetic algorithm (GA), which is a powerful tool, was applied for history matching, optimization and determination of the model parameters. Well match observed between the model results and experimental data confirms the accuracy of the proposed mathematical model of asphaltene deposition in porous media. Also, applied improvement on the model resulted in accurate simulation as well as determination of precipitated asphaltene saturation. Optimization shows that all deposition mechanisms, surface deposition, entrainment and pore plugging, are dominant during the permeability evolution process. Results of this work can be helpful for reliable simulation of the dynamic asphaltene deposition process during different production schemes. Keywords: asphaltene deposition, model parameter, production chemistry, artificial intelligence, hydrate remediation, scale inhibition, remediation of hydrates, paraffin remediation, optimization problem, machine learning Subjects: Production Chemistry, Metallurgy and Biology, Information Management and Systems, Inhibition and remediation of hydrates, scale, paraffin / wax and asphaltene Copyright 2010, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".