Density of High Pressure and Temperature Gas Reservoirs: Effect of Non-hydrocarbon Contaminants on Density of Natural Gas Mixtures
Bibliographic record
Abstract
Density of High Pressure and Temperature Gas Reservoirs: Effect of Non-hydrocarbon Contaminants on Density of Natural Gas Mixtures F.. Tabasinejad; F.. Tabasinejad University of Calgary Search for other works by this author on: This Site Google Scholar R. G. Moore; R. G. Moore University of Calgary Search for other works by this author on: This Site Google Scholar S. A. Mehta; S. A. Mehta University of Calgary Search for other works by this author on: This Site Google Scholar K. C. Van Fraassen; K. C. Van Fraassen University of Calgary Search for other works by this author on: This Site Google Scholar Y.. Barzin; Y.. Barzin University of Calgary Search for other works by this author on: This Site Google Scholar J. A. Rushing; J. A. Rushing Anadarko Petroleum Corp. Search for other works by this author on: This Site Google Scholar K. E. Newsham K. E. Newsham Apache Canada LTD Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Western Regional Meeting, Anaheim, California, USA, May 2010. Paper Number: SPE-133595-MS https://doi.org/10.2118/133595-MS Published: May 27 2010 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Tabasinejad, F.. , Moore, R. G., Mehta, S. A., Van Fraassen, K. C., Barzin, Y.. , Rushing, J. A., and K. E. Newsham. "Density of High Pressure and Temperature Gas Reservoirs: Effect of Non-hydrocarbon Contaminants on Density of Natural Gas Mixtures." Paper presented at the SPE Western Regional Meeting, Anaheim, California, USA, May 2010. doi: https://doi.org/10.2118/133595-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Western Regional Meeting Search Advanced Search Abstract New experimental density data are generated in this study for light and heavy dry gas mixtures. The light mixtures consist mostly of methane and small fractions of ethane, propane, and nitrogen. Normal alkanes up to C6, iso-butane and iso-pentane together with carbon dioxide form the heavier gas mixtures. For each mixture, isothermal gas density is measured from 3.45 MPa to 140 MPa at temperatures of 423.15 K and 478.15 K. Effects of CO2 and N2 as two non-hydrocarbon contaminants, on density of gas mixtures are examined in steps of 5 mol%, 10 mol%, and 20 mol%. In addition, water vapor influence on gas phase density of water-saturated gas mixtures is also investigated. Different correlations for sweet and sour gas critical properties are combined with the most widely used equations of state (Hall-Yarborough and Dranchuk-Abou-Kassem) to predict density data for comparison with 450 experimental measurements. The most important results demonstrated from this study are:Among all correlations, the combination of the Hall-Yarborough equation with the pseudo-critical properties correlated by Sutton generates the lowest average absolute deviation (AAD) between predicted and experimental density data.The correction term developed by Wichert and Aziz to modify the pseudo-critical properties due to the presence of non-hydrocarbon compounds in the gas mixture, drastically improves the prediction of density data.At very high pressure and temperature conditions, effect of water vapor becomes more significant on gas phase density and it should be considered in density related correlations. Keywords: correlation, gas phase density data, wichert, upstream oil & gas, fluid modeling, hydrocarbon gas mixture, gas compressibility factor, phase density, density data, correction term Subjects: Fluid Characterization, Fluid modeling, equations of state 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".