Application of asphaltenes molecular structure analysis in assessing lateral reservoir continuity: A case study in the Bangestan reservoir from a field of Dezful Embayment
Bibliographic record
Abstract
Reservoir compartmentalization is a phenomenon whereby the presence of flow barriers divides a hydrocarbon reservoir into separate zones with distinct flow behaviors. Accurate identification of these flow barriers and the various reservoir zones is crucial for optimal field management, accurate reserve estimation, proper well placement design, and, in general, for all future field development decisions. Given the significance of studying reservoir compartmentalization in Iranian oil fields, this paper investigates lateral continuity in the Bangestan reservoir in one of the fields from the Dezful Embayment. For this purpose, a novel and efficient approach was employed, utilizing the structural characteristics of asphaltenes through Fourier-transform infrared (FTIR) spectroscopy. Asphaltenes are macromolecular compounds with a structure similar to kerogen. Due to their stability against secondary processes, such as biodegradation and water washing, they serve as reliable indicators for obtaining oil fingerprints and assessing fluid composition heterogeneity within a reservoir. In this study, four crude oil samples were collected from producing wells in the Bangestan reservoir and analyzed by FTIR to determine various structural indices, including aliphatic, aromatic, branched, and substitution indices, to compare the structural characteristics of different asphaltenes. The results showed that sample S-1 exhibited significant differences in structural indices and chemical composition compared to the other samples (S-2, S-3, and S-4). This issue indicates a difference in crude oil fingerprints among the studied wells, which is attributed to the presence of a flow barrier in the Bangestan reservoir. To confirm these results, pressure data were also analyzed, which revealed a different pressure gradient for well S-1 compared to the other wells, further supporting the presence of a flow barrier. Therefore, the use of structural characteristics of asphaltenes is considered an efficient, low-cost, and straightforward method, providing results comparable to reservoir engineering data in identifying reservoir discontinuities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".