Recently, discontinuous polymer flooding has been proposed and successfully applied in some offshore oilfields. The performance of discontinuous polymer flooding depends on various operational parameters, such as injection timing, polymer concentrations, and crosslinker concentrations of four types of chemical slugs. Because the number of the operational parameters are large and they are nonlinearly related, the traditional reservoir numerical simulation might not simultaneously obtain the optimal results of these operational parameters. In this study, to simulate the discontinuous polymer flooding processes, a simulation model was built using a commercial reservoir simulator (CMG STARS), in which the mechanisms of the four types of chemical slugs were considered, such as polymer viscosification, adsorption, and degradation. Then, a PSO–ICA algorithm was developed by using the PSO algorithm to improve the exploration ability of the ICA algorithm. The codes were written with MATLAB and linked to CMG STARS to perform optimization processes. Finally, the PSO–ICA algorithm was compared with the ICA and PSO algorithms on benchmark functions to verify its reliability and applied to optimize a discontinuous polymer flooding process in a typical offshore oilfield in Bohai Bay, China. The results showed that the developed PSO–ICA algorithm had lower iteration numbers, higher optimization accuracy, and faster convergence rate than these of PSO and ICA, indicating that it was an effective method for optimizing the operational parameters of discontinuous polymer flooding processes. Compared to the continuous polymer flooding, the discontinuous polymer flooding had a higher oil production rate, a lower water cut, and a lower residual oil saturation. The net present value of the optimal scheme of discontinuous polymer flooding reached 7.49 × 10⁸ $, which is an increase of 6% over that of the scheme of continuous polymer flooding. More research including selecting more reasonable parameters of the PSO–ICA algorithm to increase its optimization accuracy and convergence rate, comparing with other available optimization algorithms, and verifying the performance of the optimal scheme of discontinuous polymer flooding in the practical offshore oilfield will be required in the future.

Amphiphilic-polymer flooding, which can increase water viscosity, decrease oil viscosity, and improve oil displacement efficiency, is a promising oil exploitation method for heavy oil. Due to oil–water emulsification, shear-thinning, and changes in oil viscosity when determining the relative permeability data of new amphiphilic polymers, the conventional J.B.N. method is not accurate. This paper presents a new method called the iso-permeability point trial method to determine the relative permeability curve by combining the J.B.N. method, the Corey model, and the relationship between water saturation and the relative permeability ratio. To avoid using polymer viscosity, a mathematical equation was derived based on the characteristics of the relative permeability curve. The results indicate that the new method is feasible and the obtained curve is more reasonable and smooth. The influence of concentration, permeability, and oil viscosity on amphiphilic-polymer displacement relative permeability was also analyzed, demonstrating that under the same water saturation, the water relative permeability is lower than that of water flooding but the oil relative permeability is bigger, which manifests as the iso-permeability point moves to the right and results in a lower residual oil saturation. In addition, the aforementioned trends are more obvious when the amphiphilic-polymer concentration is high, formation permeability is low, and oil viscosity is low.

Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved Archimedes optimization algorithm with a Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize infill well placement for polymer flooding, an objective function that considers the economic influence of infill drilling was developed. The novel optimization algorithm (HS-AOA) for infill well placement was subsequently developed by combining the AOA with the Halton sequence. The codes were developed in MATLAB 2023a and connected to a commercial reservoir simulator, Computer Modeling Group (CMG) STARS, Calgary, AB, Canada to carry out infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into the AOA effectively increased the diversity of the initial objects in the AOA and prevented the HS-AOA from becoming trapped in the local optimal solutions. The HS-AOA outperformed the AOA. This approach was effective for optimizing the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically improve the polymer flooding performance in offshore oil reservoirs. The net present value (NPV) of the polymer flooding case with infill wells determined by HS-AOA reached USD 3.5 × 10⁸, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for optimizing infill well placement for polymer flooding processes. It can also serve as a valuable reference for other optimization problems in the petroleum industry, such as joint optimization of well control and placement.

Infill drilling is one of the most effective methods to improve the performance of poly-mer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved archimedes optimization algorithm with Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize the infill well placement for polymer flooding, an objective function that considered the economic influence of infill drilling was developed. Then, the novel optimization algorithm (HS-AOA) for infill well placement was developed by combining the AOA with the Halton se-quence. The codes were developed in MATLAB and connected to a commercial reservoir simula-tor CMG STARS to carry out the infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into AOA effectively increased the diversity of the initial ob-jects in the AOA and avoided the HS-AOA trapping into the local optimal solutions. The HS–AOA outperformed the AOA. It was an effective approach to optimize the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically im-prove the polymer flooding performance in offshore oil reservoirs. The NPV of the polymer flooding case with infill wells determined by HS-AOA reached 3.5 × 108 $, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for opti-mizing infill well placement for polymer flooding processes. It also can serve as a valuable refer-ence for other optimization problems in the petroleum industry, such as joint optimization of well control and placement.

The complex dynamics of fluid and particles flowing through pore space demands some relaxation time for particles to catch up with fluid velocity, which manifest themselves as non-equilibrium (NE) effects. Previous studies have shown that NE effects in particle transport can have significant consequences when relaxation time is comparable to the characteristic time associated with the fluid flow field. However, the existing models are lacking to account for this complicated relation between particles and fluid. In this paper, we adapt the general form of the harmonic oscillation equation to describe NE effects in the particulate flow system. The NE effect is evaluated by solving coupled mass balance equations with computational fluid dynamic (CFD) techniques using COMSOL Multiphysics^®. A simplified straight-tube model, periodic converging–diverging tube model, and SEM image of a real pore network are applied in NE analyses. The results indicate that the time variation of the NE effect complies with the theory of stability. Two key parameters of the oscillator equation are amplitude (A) and damping ratio (ζ), where the former represents the magnitude of NE and the latter is an indication of flow path geometry. The velocity equations for particle transport in different flow path geometries are derived from the proposed NE equation, offering a quick estimation of particle velocity in the particulate flow. By conducting simulation on the SEM image of a real pore structure, the equivalent radii of the pores where particles move through were obtained. The outcome of this work can shed light on explaining the complex NE effects in porous media. The generalized equation to model NE can help temporarily decouple particle transport equations from fluid equations, facilitating much advanced particulate flow modeling problems in the large-scale problems.