A New Methodology To Match Heavy-Oil Long-Core Primary Depletion Experiments
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Bibliographic record
Abstract
Abstract When considering cold production from heavy oil fields, one of the main issues is to define a reliable recovery factor. Many field simulations are performed with black oil simulators, and predictions are very often below the observed production levels. One explanation is the difficulty of such simulators in representing the physical behavior of this type of "bubbling" oil : as depletion reaches the bubble point, small bubbles of gas appear; they grow according to the solution gas drive process, then connect to create a free gas phase. A series of experiments has been performed under reservoir conditions, by applying different depletion rates, on two long cores of consolidated sandstone with similar characteristics : Berea (0. 8 bar/d and 8 bar/d) and Clashach -Scotland- (0. 5 bar/d and 2. 5 bar/d). Two recombined oils from the Zuata field -Orinoco- were tested. Reservoir oil and gas production, in-situ gas saturation profiles, critical gas saturations were measured and compared. A black oil simulator had been used to match these experiments whereby a separate gas relative permeability (krg) curve was derived for each experiment. Simulations were also run with a chemical-like simulator. It considers three forms of gas : the solution gas, the dispersed gas (bubble gas) and the free gas. This simulator applies one krg for free gas and one for dispersed gas. It applies specific kinetic factors for each transformation from dissolved gas to dispersed gas, then from dispersed gas to free gas. The simulations give a set of krg curves and kinetic factors that match all experiments. A methodology is described giving a set of parameters which are independent of the depletion rate. The simulations provide production profiles for each type of gas. As, during experiments, only the total gas is measured, some observations are made which could help to distinguish between dispersed and free gas during the experiment. Introduction This work aims at studying and improving the understanding of the solution gas drive mechanism in heavy oils below the saturation pressure. This phenomenon is often called "foamy oil" behavior, likely due to the appearance of the mixture produced at the wellhead, even though the term of "bubbling oil" would seem to be more appropriate. Recently, studies have been carried out to explain the advantages - higher recovery and production rate - of this process in heavy oil reservoirs (mainly in Canada and Venezuela - de Mirabal et al). Nevertheless, this complex mechanism still remains controversial according to the different assumptions reported :high critical gas saturation (Maini et al, 1993).lubrication effect caused by nucleation of gas bubbles on the pore wall (Shen and Batycky, 1996);low gas mobility (Pooladi-Darvish and Firoozabadi, 1999; Tang and Firoozabadi, 1999; Kumar et al, 2000). As regards numerical simulation, in order to tackle the original behavior of bubbling oils, the first modeling attempts have tried to modify the Darcy's law, implemented in the existing simulators to describe the two phase flow of oil and gas :Kraus et al (1993) introduced the ‘pseudo-bubble point model’ assuming that all the gas produced remains entrained in the oil phase as long as the pressure does not reach the pseudo-bubble point, lower than the actual saturation pressure measured in a PVT cell.Lebel (1994) developed the concept of both ‘modified fractional flow’ - to represent the entrainment of part of the gas by the oil - and ‘reduced foamy oil viscosity’ - above a certain gas saturation corresponding to the liberation of the gas.Claridge and Prats (1995) proposed the ‘reduced viscosity model’, assuming that the oil viscosity decreases when the asphaltenes content in oil is reduced, while the asphaltenes would contribute to stabilize the smaller gas bubbles.
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| 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) | 0.001 | 0.000 |
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