Steam-assisted gravity drainage (SAGD) is one of the most successful thermal enhanced oil recovery (EOR) methods for cold viscose oils. Several analytical and semi-analytical models have been theorized, yet the process needs more studies to be conducted to improve quick production rate predictions. Following the exponential geometry theory developed for finding the oil production rate, an upgraded predictive model is presented in this study. Unlike the exponential model, the current model divides the steam-oil interface into several segments, and then the heat and mass balances are applied to each of the segments. By manipulating the basic equations, the required formulas for estimating the oil drainage rate, location of interface, heat penetration depth of steam ahead of the interface, and the steam required for the operation are obtained theoretically. The output of the proposed theory, afterwards, is validated with experimental data, and then finalized with data from the real SAGD process in phase B of the underground test facility (UTF) project. According to the results, the model with a suitable heat penetration depth correlation can produce fairly accurate outputs, so the idea of using this model in field operations is convincing.
The present research carries out an in-detail study of the VAPEX process as one of the most recent solvent-based heavy oil recovery techniques in fractured reservoirs to evaluate the effect of fracture parameters on process performance. To achieve this purpose, several fractured patterns with distinct features were designed and engraved on glass pieces to manufacture state-of-the-art microfluidic models mimicking a typical Canadian heavy oil reservoir. A heavy oil sample of viscosity 1514 cP was utilized during the conducted experiments with pure propane and pure carbon dioxide as the injection solvents. A thorough image analysis operation was carried out over the experimental models to determine heavy oil produced, residual oil saturation, ultimate recovery factors, and monitor solvent chamber expansion. Numerical simulations of the same experiments were carried out for history matching and predicting other designed scenarios. Error analysis revealed average absolute errors of below 8%, showing convincing precision. Together with the simulation outcomes, a comprehensive data bank was obtained from the 30 scenarios designed and 18 VAPEX experiments conducted. The effects of fracture orientation, length, width, intensity, and position on process performance were identified and numerically evaluated. It was observed that all fractures, regardless of their properties, enhanced heavy oil recovery in comparison to the base case (no fractures) scenario. Moreover, propane proved more efficient owing primarily to its higher solubility and effective dispersion. The highest recovery factor, 65.81%, was obtained when implementing two wide vertical fractures on either side of the well pair. Almost equal to that, 64.93% was the process efficiency by positioning two long horizontal fractures between the wells.
Hot steam preparation for so long as of in the Steam Assisted Gravity Drainage (SAGD) process has proved very costly. The "Fort McMurray" reservoir in Alberta, as a large Canadian heavy oil resource, has undergone SAGD, to reach acceptable production rates. The published data was used to investigate new methods, such as that introduced in this paper, for further enhancing this popular heavy oil recovery method. Lack of a uniform steam distribution along the horizontal injection well has demonstrated itself as a major challenge since a considerable portion of hot steam enters the reservoir through perforations near the heel. Thus, regions connected to the toe section of the well do not receive the heat required for equally reducing the heavy oil viscosity. On the other hands, Inflow Control Devices (ICDs) have been used successfully to create a uniform pressure distribution along horizontal wells by providing an extra pressure against the fluid flow near the wellbore. This paper is aimed at conducting the simulation of injection/production systems in the SAGD process on the "Fort McMurray" reservoir. Three scenarios were defined to accurately investigate the effect of employing ICDs on the cumulative oil production as the objective function. The first scenario was designed and run with only one production well to highlight reservoir's poor ability in naturally delivering the oil to the surface. In the second scenario, an injection well was introduced through which hot steam was to be injected, to simulate the SAGD process and in the final case; this injection well was equipped with ICDs. Finally, results indicated a dramatic rise from 3.96 to 6.39 MMSTB in the cumulative oil production as a consequence of implementing ICDs in the injection well in the end of the injection/production period. Moreover, the recovery factor, initially was equal to 40.5%, was improved to 65.2%. Toe temperature, as another parameter to be monitored, experienced an increase of almost 75 °F and reached 400 °F following the use of these devices. In this study, ICDs that had been mostly used in production wells, were utilized to make a smart injection well in the SAGD process which according to simulation results, were successful. Moreover, an integrated production modeling approach saw the reservoir, wells and surface facilities taken into account simultaneously to monitor the effect of interaction of different parts of the production system on eachother.
An analytical model for predicting the oil production rate in the steam-assisted gravity drainage (SAGD) process is presented in this article. The suggested correlation is found based on Butler's original work. It considers the most effective parameters of the process that emphasize the influence of gravity drainage and that are grouped together in the form of the Rayleigh's number. The present model introduces three coefficients (i, j, and k) into the equation, which are determined by minimizing an objective function based on the difference between the six experimental SAGD datasets and the calculated results. The tool chosen for the minimization is the genetic algorithm (GA). After the initial evaluation, the same approach is used for other reservoir characteristics to ensure the robustness of the new equation. Having considered various simulation outcomes with an average error of 8.9% makes this model a credible one for predicting the SAGD production rates. The novelty of the new predictive model lies within its unique approach, making it quite fast and applicable to a wide range of reservoirs with low associated estimation inaccuracies.
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