Effect of Temperature on Bitumen/Water Relative Permeability in Oil Sands

Abstract

The steam-assisted gravity drainage (SAGD) process is among the most applicable thermal enhanced oil recovery (TEOR) methods in North American heavy oil reservoirs. In addition to improving oil mobility through viscosity reduction, the high-temperature condition that a TEOR method causes may significantly alter the fluid flow performance and behavior, as manifested by the relative permeability to fluids in the reservoir. Therefore, in a SAGD and cyclic steam stimulation (CSS), the relative permeabilities can change within the temperature-gradient region developed around the SAGD chamber or between two cycles of CSS processes. However, there is no unique two-phase relative permeability model, especially for bitumen systems to cover a wide range of temperatures. Therefore, the primary objective of this study is to history-match the results of the recent study done by Esmaeili et al. using a new methodology and to conduct limited steady-state relative permeability measurements at different temperatures to confirm the validity of displacement-based relative permeability curves and then to propose a reliable general relative permeability model as a function of temperature. Twelve history-matched bitumen/water relative permeability sets are obtained in a wide range of temperatures from 70 to 220 °C under a confining pressure higher than 1000 psi and the test pressure in the range of 350−400 psi. An in-house developed reservoir simulator was used to obtain relative permeability curves from isothermal displacement data using the history-matching approach. The results demonstrated that both production and pressure-drop data can be matched using the optimized values of parameters of the generalized Corey relative permeability model. Furthermore, a more reliable value for actual residual oil saturation was determined by applying our new methodology in history matching. A comparative analysis with the steady-state relative permeability data allowed us to determine the extent to which the unsteady-state relative permeability is affected by viscous fingering. A considerable shift in both oil and water relative permeability at higher temperatures is noticed. Furthermore, it is also noted that the endpoint relative permeability to oil and water is also altered significantly. The implicit relative permeability data obtained from this study are consistent with the results of the JBN method that were reported in our previous study. By making the parameters of the Corey relative permeability model dependent on temperature, a new model of temperature-dependent bitumen/water relative permeability in sand was developed. It is anticipated that this model can facilitate inclusion of temperature-dependent relative permeability in reservoir simulation studies.

A Critical Review of Reservoir Simulation Applications in Key Thermal Recovery Processes: Lessons, Opportunities, and Challenges

“…Questions remain such as the impacts of high-temperature operations on relative permeability, emulsion, and aquathermolysis. Polikar et al in 1986 and 1990 reported that after the temperature was raised from 125 to 250 °C, no change in relative permeability was noticed, while a modified relative permeability curve was observed under high temperature by Maini and Okazawa in 1987 and Esmaeili et al in 2020, which could be caused by a wettability change in oil sands . Future work is needed to confirm the wettability changes during SAGD operation, which needs to be realized in future simulation studies as well.…”

Two-phase Flow Model and Productivity Evaluation of Gas and Water for Dual-Medium Carbonate Gas Reservoirs

“…As for the relative permeability of gas and water, Esmaeili et al (2020) use a newmethodology and conduct limited steady-state relative permeability measurements at different temperatures to confirm the validity of displacement-based relative permeability. Fang et al (2015) study the pattern of the relative permeability curves of gas and water in high-temperature, high-pressure tight gas reservoirs.…”

Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations

“…In the oil industry, machine learning techniques were used to predict pressure, volume, and temperature (PVT) properties of crude oil [26,27], crude oil price [28,29], and enhanced oil recovery [30,31]. In oil sands operations, machine learning methods were applied to analyse incident reports and increase process safety [32,33], and predict crude oil production from in situ oil sands extraction [34,35]. To the best of our knowledge, data mining techniques have not been applied to the Canadian oil and gas data warehouse or, more broadly, to any oil and gas data warehouse.…”