A New Method for Measuring Solvent Diffusivity in Heavy Oil by Dynamic Pendant Drop Shape Analysis (DPDSA)
Bibliographic record
Abstract
A New Method for Measuring Solvent Diffusivity in Heavy Oil by Dynamic Pendant Drop Shape Analysis (DPDSA) Chaodong Yang; Chaodong Yang University of Regina Search for other works by this author on: This Site Google Scholar Gu Yongan Gu Yongan University of Regina Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84202-MS https://doi.org/10.2118/84202-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Yang, Chaodong, and Gu Yongan. "A New Method for Measuring Solvent Diffusivity in Heavy Oil by Dynamic Pendant Drop Shape Analysis (DPDSA)." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84202-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractThis paper presents a new experimental method and its computational scheme for measuring the diffusion coefficient of solvent in heavy oil under the practical reservoir conditions by dynamic pendant drop shape analysis (DPDSA). In the experiment, a see-through windowed high-pressure cell is filled with the test solvent at desired pressure and temperature. Then a heavy oil sample is introduced slowly through a syringe delivery system to form a pendant drop inside the pressure cell. The subsequent diffusion process of solvent into the pendant oil drop causes its shape and volume to change until an equilibrium state is reached. The sequential digital images of the dynamic pendant drop are acquired and digitized by applying computer-aided digital image processing techniques. Physically, variations of the shape and volume of the pendant drop are attributed to the interfacial tension reduction and the well-known oil swelling effect as solvent gradually dissolves into heavy oil. Theoretically, the dynamic pendant drop profile is governed by the Laplace equation of capillarity and the molecular diffusion process of solvent into the pendant oil drop is described by the mass diffusion equation. An objective function is constructed to express the discrepancy between the experimentally observed and the numerically predicted profiles of the dynamic pendant drop. The solvent diffusivity in heavy oil and the mass-transfer Biot number are used as adjustable parameters and thus determined once the minimum objective function is achieved. This novel experimental technique is tested to measure diffusivities of carbon dioxide in a brine sample and carbon dioxide in a heavy oil sample, respectively. It should be noted that, with the present technique, a single diffusivity measurement can be completed within an hour and only a small amount of oil sample is required. The interface mass-transfer coefficient at the solvent-heavy oil interface can also be determined. In particular, this new technique allows the measurement of solvent diffusivity in an oil sample at constant pre-specified high pressure and temperature. Therefore, it is especially suitable for studying the mass transfer process of injected solvent into heavy oil during solvent-based post-cold heavy oil production (CHOP).IntroductionWestern Canada contains tremendous heavy oil and bitumen resources1,2. Approximately 80% to 95% of the original-oil-in-place (OOIP) is still left behind at the economic limit after the cold production2. This is a large oil-in-place target for follow-up enhanced oil recovery (EOR) processes. After the primary production, most Canadian heavy oil reservoirs cannot be further exploited economically by thermal recovery processes because reservoir formations are thin and/or there is active bottom water. In the literature, some studies have been conducted to evaluate the other recovery methods for these heavy oil reservoirs2–5. Among these methods, vapor extraction (VAPEX) and other solvent-based post-CHOP processes are probably the most promising EOR techniques. In practice, the solvent can be carbon dioxide, flue gas, nitrogen, and light hydrocarbon gases, such as natural gas, methane, ethane, propane and butane. Keywords: coefficient, complex reservoir, interfacial profile, interface, optimization problem, upstream oil & gas, co 2, oil interface, enhanced recovery, heavy oil Subjects: Improved and Enhanced Recovery, Unconventional and Complex Reservoirs, Information Management and Systems, Oil sand, oil shale, bitumen, Artificial intelligence This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".