Bibliographic record
Abstract
Technology Focus The last 3 years have seen an unprecedented increase in paraffin-related challenges in the world of production chemistry. The industry is producing more-challenging crude oil as the production of light, sweet, and easy-to-treat crude declines. The new crudes coming on stream contain more-complex combinations of paraffinic components. We are aware of the bimodal distribution of paraffin displayed nicely in high-temperature gas chromatography of, for example, Eagle Ford crude oil, which shows a double peak distribution of paraffin. Some of the more-recent publications highlighted are using more-advanced analytical techniques to characterize crude oil because heavier paraffin components are not detectable by classical methods. Characterization using techniques such as nuclear magnetic resonance and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry are detecting paraffin chains in excess of C100 and determining a trimodal distribution of paraffin in crude oil that is difficult to treat using today’s technology, thus requiring a change in approach and thinking for the production chemist. Such crudes are being developed today—crude oils from some of the more extreme shale plays around the world such as the Montney formation in British Colombia, Canada; Vaca Muerta in Argentina; East African crude oils of Uganda, Chad, and Kenya; and crude oils in the Far East such as those in Indonesia and Vietnam. The current chemical technology begins to reach its limit of functionality in a crude oil that contains paraffin chains of C75+ in length. The featured papers give some insights into the work under way to elucidate the structure performance relationships between inhibitors and crude oils containing these higher paraffinic species. Much work remains in order to develop more-effective solutions and strategies for these incredibly challenging and highly paraffinic crude oils. The featured papers summarize some of the state-of-the-art techniques to determine the true nature of paraffin deposition from these bi- and trimodal crude oils as well as advanced techniques to truly characterize and treat produced fluids and recovered solids samples. Readers are encouraged to research the recommended additional reading and take some time to delve into the references contained in these papers. This literature contains an extensive review of the history and current state of the art in paraffin science for the oilfield chemist and engineer. Recommended additional reading at OnePetro: www.onepetro.org. OTC 27855 Work Flow To Evaluate Wax-Deposition Risk Along Subsea Production Systems by O. Coronado, Genesis, et al. OTC 28714 Cold-Finger Benchmarking Study for Paraffin-Inhibitor Selection by Yun Peng, Shell, et al. SPE 187252 Investigating the Performance of Paraffin Inhibitors Under Different Operating Conditions by Anshul Dubey, The University of Tulsa, et al.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.411 | 0.253 |
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".