Publications

The search for conventional oil is getting more and more difficult, while heavy oil and extra heavy oil have recently been considered as alternative energy sources to fulfill world energy demand (Meyer et al., 2007). However, it is technically challenging to produce heavy and extra heavy oil due to its high viscosity. Many technologies have been applied in the field to recover heavy oil, such as steam injection, in-situ combustion and hot-water flooding and electromagnetic heating. Amongst them all, electromagnetic heating is being introduced as an effective technology to recover heavy oil since it can handle thermal aqueous limitations. Thus, electromagnetic heating is shifting the paradigm in supplying energy in the new reality. Electromagnetic heating is an alternative way to heat the reservoir fluids without much loss of heat to the surroundings (Chakma and Jha, 1992). It uses electromagnetic radiation to heat the reservoir to reduce the oil viscosity. There are two types of electromagnetic heating: low-frequency heating and high-frequency heating. In low-frequency heating (< 60 Hz), resistance heating dominates the process (Chakma and Jha, 1992, Carrizales et al., 2008). In high-frequency heating (3 kHz to 300 GHz), dielectric heating dominates the process (Sierra et al., 2001, Carizzales et al., 2008). Furthermore, high-frequency heating is more applicable and it is commonly used in the field (Sresty et al., 1986, Davison, 1991, Sierra et al., 2001, Hascakir and Akin, 2009). The development of optimum heating condition in electromagnetic heating leads to the identification of several key parameters that affect heating condition the most. Through series of mathematical simulations (Carizzales et al., 2008) and laboratory experiments (Hascakir and Akin, 2009, Chakma and Jha, 1992), the parameters are identified and classified into three groups: target parameters, designed parameters and economic parameters. Firstly, target parameters describe reservoir conditions to be altered to increase production, they are oil viscosity, specific heat of oil, hydrocarbon composition, reservoir thickness, oil reserve and oil saturation. Secondly, designed parameters refer to nanofluid properties to be used in increasing the thermal properties of the reservoir, they are attenuation constant, induced frequency, nanofluid thermal properties and compatibility. Finally, economic parameters are used to confirm whether the project is executable, they are net present value, internal rate of return and payout time. Target parameters (oil viscosity, oil reserve, etc) and economic parameters (net present value, internal rate of return, etc) become our first consideration before applying electromagnetic heating. When reservoir properties meet the criteria and the operation is economically implemented, designed parameters will be critical. Designed parameters which are related to nanofluid properties become the key to optimize the production and minimize the cost. Through several investigations, attenuation constant and induced frequencies are critical in designing the nanofluid properties. Attenuation constant is defined as the cross-section combination of some physical and chemical interactions involved by the presence of external magnetic field at a material (Chantler et al, 2005). It is preferred to have a high value attenuation constant so that reservoir temperature will increase faster. This attenuation constant value depends on type of nanoparticles and induced frequency. Various frequencies will give different effects towards movement of nanoparticles. Commonly, there are three types of nanoparticles movements: translation, rotation and vibration. If the induced frequency can cause the right nanoparticles movement, optimum heat generation rate can be achieved, therefore, the project will be profitable. Finally, it can be said that the selection of nanoparticles and induced frequency is critical towards heating condition in the reservoir. A case study was presented to prove the significance of nanoparticles type towards profit per hour heating. Using reservoir and economic data as shown in Table 1 and 2, simulations were conducted using three different nanoparticles (iron oxide, titanium oxide, and zinc oxide) at 1 GHz induced frequency. The simulations were conducted numerically using heat transfer model with assumption of transient-no flow during heating process (Abernethy, 1976) coupled with Darcy equation. Profit per hour simulations show that ZnO nanoparticles gives the highest profit per hour heating because it has the highest attenuation value at 1 GHz induced frequency, presented at Table 3. ZnO has the smallest dielectric value and the highest tangent loss compared to the other nanoparticles at 1 GHz. Therefore, ZnO easily absorbs the radiation energy, which later produces the highest temperature. From the simulation, we could also see that the selection of nanoparticles to be used at a certain working frequency is very important. When we cannot change the working frequency of an existing tool, changing the type of nanoparticles is the best way to achieve an optimum condition of heating. New composite nanoparticles could be created to get a high attenuation constant. Finally, it can be said that in achieving optimum condition of heating, there are three groups of parameters that we need to consider before executing electromagnetic heating in the field: target, designed and economic parameters. Attenuation constant is a significant parameter to be designed which depends on the type of nanoparticles. Therefore, selection of nanoparticles will lead to the best heat generation. It is recommended to use ZnO nanoparticles at microwave. New composite nanoparticles can also be an alternative.