Applications of petroleum geochemistry in reservoirs
Bibliographic record
Abstract
This thesis is structured in two main parts-theoretical and practical part. The theoretical part will be mainly focused on the detailed description of the application of geochemistry in oil production and exploration, through explaining the role of geochemistry in the hydrocarbon asset management, in identifying new exploration or missed pay zones, in delineating reservoir compartmentalization by explaining the fingerprinting approach in assessing the reservoir continuity, etc. Additionally, it will be presented how the geochemistry aids in maximizing the waterflood efficiency, as well as its importance in the prediction of the flow assurance problems. In this part, the application of the reservoir geochemistry will be also discussed together with the application of the geochemistry studies in the maturity assessment, oil-oil, oil-source rock correlation. Furthermore, related to the application of the light hydrocarbons, which is the main focus in the practical part, will be discussed the postgenerative alteration processes and its characterization through the application of the light hydrocarbons. In the practical part, a set of light hydrocarbon ratios will be applied on 146 sample set of oils from Western Canada. Using the compositional data of these oil samples, the compositional changes in the samples caused by the postgenerative processes will be detaily observed.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".