Heavy-Oil Fluid Testing With Conventional and Novel Techniques
Bibliographic record
Abstract
Heavy Oil Fluid Testing With Conventional and Novel Techniques Nina Goodarzi; Nina Goodarzi Search for other works by this author on: This Site Google Scholar Jonathan Luke Bryan; Jonathan Luke Bryan Search for other works by this author on: This Site Google Scholar An Thuy Mai; An Thuy Mai Search for other works by this author on: This Site Google Scholar Apostolos Kantzas Apostolos Kantzas U. of Calgary Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium, Calgary, Alberta, Canada, November 2005. Paper Number: SPE-97803-MS https://doi.org/10.2118/97803-MS Published: November 01 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Goodarzi, Nina, Bryan, Jonathan Luke, Mai, An Thuy, and Apostolos Kantzas. "Heavy Oil Fluid Testing With Conventional and Novel Techniques." Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium, Calgary, Alberta, Canada, November 2005. doi: https://doi.org/10.2118/97803-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 International Thermal Operations and Heavy Oil Symposium Search Advanced Search AbstractIn this paper, we propose the combined utilization of x-ray tomography and magnetic resonance techniques for quantification of heavy oil fluid properties. The design of these systems is presented along with preliminary results combined with conventional measurements. The objective is to understand the PVT behavior of a viscous heavy oil from a reservoir that has undergone primary production. Methane is dissolved into the oil at ambient temperature and elevated pressure. The pressure is later slowly depleted and the oil PVT properties are recorded. Specifically, this paper details measurements of oil density, formation value factor, and solution gas-oil-ratio as a function of pressure. The incremental benefit of the proposed nucleonic techniques is that they provide more detailed information about that oil, compared to conventional PVT measurements. This improves our understanding of the foamy oil response.IntroductionUnderstanding fluid behavior of heavy oils is important for reservoir simulation and production response predictions. In heavy oil reservoirs, the oil viscosity and density are commonly reported, but there is little experimental data in the literature reporting how oil properties change with pressure. This information would be especially useful for production companies seeking to understand and improve their primary (cold production) response.It is already widely known that foamy oil behavior is a major cause for increased production in cold heavy oil reservoirs along with sand production. Therefore it would be valuable to first study the bulk fluid properties of live heavy oil prior to sand pack depletion experiments. If the response of these properties to incremental pressure reduction can be established, this can be compared with fluid expansion during pressure depletion in a sand pack.Computer Assisted Tomography (CT) scanning is useful in studying high-pressure PVT relationships. Images of a pressure vessel filled with live oil can be taken as the volume of the vessel is expanded and used to calculate bulk densities and free gas saturation. Also, CT images allow us to visually see how the gas comes out of solution and where it is located in the vessel. For example, CT scanning can be used to provide an indication of whether or not small bubbles nucleate within the oil and then slowly coalesce into a gas cap, or if free gas forms straight away.CT scanning provides much more information than conventional PVT cells. Uncertainties about where gas is forming in the oil, its effect on oil properties and transient behavior cannot be solved in conventional PVT cells. However, from CT images the formation of micro bubbles could be inferred based on the density of the oil with the dissolved gas. If the oil density decreases as the pressure drops, then it is likely that gas has come out of solution but remains within the oil, hence the resulting mixture is less dense than the original live oil. However, if oil density increases as the gas evolves then the oil does not contain small gas bubbles, and gas has separated from the oil.Also, the free gas saturation growth with time, and comparison of images at equilibrium vs. immediately after the expansion of the vessel, will provide mass transfer information about gas bubble growth, supersaturation and gravity separation.When characterizing heavy oil and bitumen fluid properties, oil viscosity is one of the most important pieces of information that has to be obtained. The high viscosities of heavy oil and bitumen present a significant obstacle to the technical and economic success of a given EOR option. As a result, in-situ oil viscosity measurement techniques would be of considerable benefit to the industry. Keywords: free gas, pvt measurement, cylinder, reservoir, oil viscosity, kantza, information, bubble point, viscosity, live oil Subjects: Fluid Characterization, Formation Evaluation & Management, Phase behavior and PVT measurements This content is only available via PDF. 2005. SPE/PS-CIM/CHOA International Thermal Operations and Heavy Oil Symposium 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".