Waterflooding is among the most common oil recovery methods which is implemented in the most of oil-producing countries. The goal of a waterflooding operation is pushing the low-pressure remained oil of reservoir toward the producer wells to enhance the oil recovery factor. One of the important objects of a waterflooding operation management is understanding the quality of connection between the injectors and the producers of the reservoir. Capacitance resistance model (CRM) is a data-driven method which can estimate the production rate of each producer and the connectivity factor between each pair of wells, by history matching of the injection and production data. The estimated connectivity factor can be used for understanding the quality of connection between the wells. In the waterflooding operation, the injected water always has the potential of causing formation damage by invasion of foreign particles deep bed filtration (DBF), mobilization of indigenous particles (fines migration), scale formation, etc. The formation damage can weaken the quality of connection (connectivity factor), between the injectors and producers of the field, increasing the skin of injection well. In this paper, DBF is used for creation of formation damage in synthetic reservoir models. Then, it has been tried to find the existence and amount of formation damage by evaluating the connectivity factor of CRM. Finally, the results of that have been used for prediction of skin variation in a real case by using the connectivity factor of CRM.
Particle invasion in porous media is an important phenomenon that could lead to formation damage during different operations, such as waterflooding, workover, and drilling. In this paper, a 3D pore network model coupled with a particle tracking method was developed to investigate particle retention and permeability reduction of a pore network system. The proposed model considers the effect of hydraulic drag, gravity, and friction forces. Three mechanisms, including surface deposition, straining, and bridging, have been considered in the development of the proposed pore network model. The results of the proposed model show good agreement with experimental data. A sharp permeability reduction is observed in the early time of the injection, which indicates the blockage of the small radius throats by particles, as well as unstable fluid flow due to the distribution of the particles. Moreover, the number of throats with a small radius and different contributing mechanisms cause the discontinuous decrease of the porous media permeability. The proposed pore network modeling demonstrates that a small section of the pore network can reproduce the results of the experiment, and a big pore network that is too time and cost consuming is not required.
SiO 2 , NiO, and Fe 3 O 4 nanoparticles are used for absorb asphaltene and prevent their precipitation. For the experiment, water and nanoparticle featured water are injected into micromodel that contained the synthetic oil. The synthetic oil includes asphaltenic components and n-heptane, and volume percentage of each one differs in every experiment. The results show that when n-heptane volume percentage is higher, asphaltene aggregation is more when water is injected. Although, during nanoparticle featured water injection when there is higher n-heptane volume percentages, asphaltene are absorbed on nanoparticle surfaces, which prevents precipitation. Also, it was obtained that SiO 2 is the most efficient nanoparticle for this purpose that leads to the maximum recovery.
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