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Microbial Enhanced Oil Recovery (MEOR) is a promising EOR technique. Feeding bacteria so that they can be stimulated to produce metabolites is a good way to increase recovery factors. In this paper, we present a mathematical model which describes a MEOR process and can be applied to estimate the recovery factor.A one-dimensional isothermal model, comprising displacement of oil by water containing bacteria and nutrients, is studied. The model is composed by a hyperbolic system of four partial differential equations with source terms and appropriate initial and boundary conditions, solved numerically by a fractional step method. We analyse the case when the produced metabolites are biopolymers which increase water viscosity, and then, improve sweep efficiency.The required parameters used in this model are not always known, therefore, to better investigate their importance, a sensitivity analysis is run and the impact in the recovery factor observed. The sensitivity analysis was performed according to the following steps: 1) three values for the maximum specific growth rate were assumed and their impact in the recovery factor is analyzed, to demonstrate the importance of bacteria screening; 2) water viscosity dependence on biopolymer concentration is described by three functions and the resulting recovery factor of each one of them is compared; 3) three different models describing bacteria growth and their effects in the recovery factor are also presented. Maximum specific growth rate was the parameter that has caused the major impact in the recovery factor. When a small value was adopted, there was no additional oil recovery in comparison to water injection. This sensitivity analysis has shown the importance of laboratory tests to improve the prediction of recovery factor. It was also noticed that a significant incremental oil recovery can be achieved with this process of MEOR for different oil viscosities.
Microbial Enhanced Oil Recovery (MEOR) is a promising EOR technique. Feeding bacteria so that they can be stimulated to produce metabolites is a good way to increase recovery factors. In this paper, we present a mathematical model which describes a MEOR process and can be applied to estimate the recovery factor.A one-dimensional isothermal model, comprising displacement of oil by water containing bacteria and nutrients, is studied. The model is composed by a hyperbolic system of four partial differential equations with source terms and appropriate initial and boundary conditions, solved numerically by a fractional step method. We analyse the case when the produced metabolites are biopolymers which increase water viscosity, and then, improve sweep efficiency.The required parameters used in this model are not always known, therefore, to better investigate their importance, a sensitivity analysis is run and the impact in the recovery factor observed. The sensitivity analysis was performed according to the following steps: 1) three values for the maximum specific growth rate were assumed and their impact in the recovery factor is analyzed, to demonstrate the importance of bacteria screening; 2) water viscosity dependence on biopolymer concentration is described by three functions and the resulting recovery factor of each one of them is compared; 3) three different models describing bacteria growth and their effects in the recovery factor are also presented. Maximum specific growth rate was the parameter that has caused the major impact in the recovery factor. When a small value was adopted, there was no additional oil recovery in comparison to water injection. This sensitivity analysis has shown the importance of laboratory tests to improve the prediction of recovery factor. It was also noticed that a significant incremental oil recovery can be achieved with this process of MEOR for different oil viscosities.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA specific area under water injection in the Carmópolis field, Brazil, is been considered a candidate area for a polymer pilot project for mobility control. A reservoir characterization and an evaluation of the polymer performance in this high heterogeneous reservoir were required. For this purpose, radioactive, fluorescent and chemical tracers were applied associated with polymer in a reduced area. The tracer technology has an enormous potential use in Petrobras scenario and this Carmópolis field application was an opportunity to obtain know-how. This paper describes the basic steps from the laboratory tests to the final application including design and programming of field operation. The interpretation of the results using a new approach is also addressed.
Polymer injection in petroleum reservoirs aiming at the enhancement of oilrecovery has been used worldwide for decades. Polymers act basically increasingthe injected water viscosity and reducing the porous media permeability, thenimproving the vertical and areal sweep efficiency. Pilot polymer injection projects for mobility correction are currentlyunderway in three Brazilian fields operated by Petrobras, namely Carmópolis(Sergipe state), Buracica (Bahia state) and Canto do Amaro (Rio Grande do Nortestate), the first and the second in full operation and the third in its initialstage. With these projects, Petrobras has acquired know-how actuating in allprocess steps such as the laboratory tests, the dimensioning and programming ofthe field operation, the field implementation and finally the analysis andinterpretation of the results. The present article describes Petrobras experience during the several phasesof field projects implementation. The laboratory data for relevant parametersare presented and discussed. These data were essential to the polymer floodingprojects design and serve as input data for simulation with IMEX™, which willbe useful for the economical and technical evaluation of these projects. Introduction The purpose of polymer injection is to reduce water-oil mobility ratio. Themechanism of this process is to increase the viscosity of the water phase. Witha reduced mobility ratio, the sweep efficiency is increased and, as aconsequence, the oil recovery is enhanced [1,2]. Depending on the polymer thisincrease in the viscosity can cause also a reduction in the water effectivepermeability in the swept zone [2]. This reduction acts favorably as asecondary effect, restoring part of the reservoir pressure after the polymerpassage (residual resistance factor). This fact may cause a correction of theinjection profile of the wells by means of the rearrangement of the residentfluids. Instead of lowering the residual oil saturation, as usual in othermethods, polymer injection improves oil recovery beyond water flooding byincreasing the contacted volume of the reservoir (sweep efficiency). Thisprocess has its greatest potential in reservoirs that are moderatelyheterogeneous, contain viscous oil, and have adverse water-oil mobility ratio[1–4]. Polymer flooding is the best established of the chemical enhanced oilrecovery methods. Laboratory and field application have shown that the polymerflooding process is most effectively applied in the early stage of a waterflooding project when the mobile oil saturation is still high. However, it hasbeen applied with positive results to reservoirs in a rather mature stage ofwaterflooding like the three reservoirs studied here. The present paper shows in a chronological order the steps followed byPetrobras for the implementation of three polymer projects for mobilitycontrol:Reservoir/pilot area selectionWater injection analysisPolymer selection (laboratory tests)Design of the polymer slugImplementation and evaluation On the correct choice of the reservoir, followed by the specification anddesign of the polymer bank to be injected, depend the technical and economicalsuccess of the process [5–10]. The reservoirs were chosen based on literature parameters [8–10]. Theselection of the most suitable polymer for a specific reservoir, defined inlaboratory tests, is a critical point and involves the intrinsic properties ofthe product along with the porous media characteristics (polymerretention/adsorption, residual resistance factor, inaccessible pore volume, shear degradation, etc.).
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