A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images
Bibliographic record
Abstract
A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images Kulada Karmaker; Kulada Karmaker University of Calgary Search for other works by this author on: This Site Google Scholar Brij B. Maini Brij B. Maini University of Calgary 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-84198-MS https://doi.org/10.2118/84198-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Karmaker, Kulada, and Brij B. Maini. "A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84198-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 Annual Technical Conference and Exhibition Search Advanced Search SummaryIn vapor extraction (Vapex) process, the dispersional mixing between injected solvent vapor (propane) and in-situ bitumen occurs along the oil-solvent interface during the oil drainage process. The solvent dispersion coefficient is a key parameter that governs the oil dilution efficiency as well as the rate of production. To predict the field performance of the Vapex process it is vital to accurately estimate the value of the dispersion coefficient at the field conditions of interest. Currently, there are no factual data available in the literature and there is no proven empirical methodology for estimating the dispersion coefficients that would be pertinent to the Vapex process.Recently, the Magnetic Resonance Imaging (MRI) tools have been used to gain insights into the Vapex process. The MRI technique can generate 2-dimensional (2-D) images during the progress of a laboratory-scale Vapex experiment. Both the original bitumen and the solvent vapor are virtually invisible in these MRI generated 2-D images. However, the propane saturated bitumen is clearly visible and in the diluted oil zone, the signal intensity is a function of the dissolved solvent concentration.This paper describes a new technique to extract the net dispersion coefficients pertinent to the Vapex process from 2-D MRI images captured during a test. A new mathematical model has been developed for analyzing such 2-D images. The model portrays the unique context of mass transfer mechanisms and the interface propagation in the Vapex process. The technique has been used on a previously published MRI image and found to be very effective and straightforward.IntroductionThe Vapex process can be described as a solvent analogue of the steam assisted gravity drainage (SAGD) process1, which involves a drastic reduction of oil viscosity by diluting the in-situ bitumen with vaporized hydrocarbon solvents (HCS). The process uses a horizontal well pair with the production well located near the bottom of the pay zone and the injection well completed right above the production well as schematically shown in Figure-1. The injected solvent vapor, such as propane, dissolves into the bitumen and reduces its viscosity to a low value so that the diluted bitumen can drain down, under the gravity force, into the production well. The pore space around the injection well in the swept zone remains filled with solvent vapor and is called "vapor chamber". The diluted oil flow occurs in a thin film (drainage edge) adjoining the interface as illustrated in Figure-1. In the progress of oil drainage, the vapor chamber grows and the vapor-bitumen interface moves laterally at a certain velocity depending on the rate of interfacial mass transfer of solvent into bitumen.There are several mechanisms that enhance the interfacial mass transfer process in porous media, as explained by Das and Butler2. However, the mixing of solvent and bitumen occurs mainly by a combined mechanism of molecular diffusion and convective dispersion that takes place mostly within the drainage edge. While history matching the results of Vapex experiments using reservoir simulation software, Nghiem3 has shown that the convective dispersion is more important than the molecular diffusion in the mixing process. For a satisfactory history match, he needed transverse dispersion coefficients that were at least an order of magnitude larger than the molecular diffusion coefficient. Keywords: experiment, equation, enhanced recovery, dispersion coefficient, successive image, concentration, signal strength profile, propane, interface velocity, upstream oil & gas Subjects: Improved and Enhanced Recovery This content is only available via PDF. 2003. 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".