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Optimization of the polymer flooding injection strategy in a heavy oil reservoir considering integration between reservoir and production systems

Grant number: 22/02231-9
Support type:Scholarships in Brazil - Master
Effective date (Start): April 01, 2022
Effective date (End): December 31, 2023
Field of knowledge:Engineering - Mechanical Engineering - Transport Phenomena
Cooperation agreement: Equinor (former Statoil)
Principal researcher:Denis José Schiozer
Grantee:Lorena Cardoso Batista
Home Institution: Centro de Estudos de Energia e Petróleo (CEPETRO). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Company:Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica (FEM)
Associated research grant:17/15736-3 - Engineering Research Centre in Reservoir and Production Management, AP.PCPE

Abstract

Polymer flooding is an EOR recovery technique widely used in heavy-oil reservoirs to enhance the sweep efficiency due to modification of the mobility ratio. However, the charges present in the polymer chains interact with the rock formation leading to additional effects in porous media, like adsorption causing the reduction of the polymer concentration in the aqueous solution, breakage of polymer chains due to the shear rate in the porous medium, alteration of the residual oil saturation curve, and polymer retention in porous media causing injectivity reduction (LIU, ZHEYU et al., 2017). Commercial numerical simulators can simulate several of these effects. However, the quality of the results for a practical application depends on integrating models with the whole development field project (LAMAS, L. F. et al., 2021). Each part of the process brings uncertainties that may affect the reliability of the simulations and, thus, the final decision. In this context, boundary conditions are fundamental from a numerical simulation perspective, which, at same time, need to represent the flow behaviorin the reservoir from a geological perspective. Furthermore, from an operational aspect,simulations must also capture how pressure and flow rates are changing in the producer and injectors wells. Focusing on the operational perspective of polymer flooding, we have some challenges, for example, the fluids produced vary with time. First it is expected to produce with a higher oil/water ratio, but with the breakthrough of the polymer bank, the polymer becomes produced. The existence of a gas phase makes the problem more complex. When analyzing the integration between the production systems with the reservoir production, the deliverable by each well will affect the other. Moreover, the flowline system provides additional and different pressure losses of each flow way possible. In this context, we are interested to study if the conditions applied in the producers and injectors can be optimized to the maximum production as a function of the fluid produced in each stage.Another important aspect during polymer injection is regarding the condition in the near wellbore region. Most commercial numerical simulators use the Peaceman strategy to calculate the pressure in the cells. Once the cells are generally much larger than the well diameter, the effects of injectivity losses in the near wellbore region is not well represented. Therefore, another aspect that leads to uncertainty is the strategy to compute the injectivity losses and how to integrate this effect with the boundary conditions previously discussed. It is clear that manyvariables are involved when optimizing the polymer injection flooding. Thus, a well-definedmethodology is necessary.To assist decision makers, SCHIOZER et al. (2019) described twelve steps, which were divided into: (a) data collection, reservoir characterization and model construction under uncertainty; (b) data assimilation and uncertainty reduction; (c) life-cycle production optimization and decision analysis under uncertainty based on simulation models and other technique to seepup the process; and (d) implementation of long-term decisions and short-term reservoir control.This project will be based on this methodology and will probably have the cooperation of RL2 -Artificial Lift and Flow Assurance. (AU)

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