The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

The profile control that employs viscoelastic particles for enhancing oil recovery is a novel technology that has attracted widespread interest in recent years. Viscoelastic particles can selectively plug high-permeability zones and enlarge the sweep volume of low-permeability zones, which can better improve the effect of development. Based on the extensive investigation of existing literature, the research status of experiments and simulations of viscoelastic particles on profile control is introduced in this paper. First, the properties of viscoelastic particles are summarized. Thereafter, the injection ability, migration ability, profile control ability, and damage effects of viscoelastic particles are expounded. Moreover, the simulation methods for studying the profile control, including classical percolation theory, size exclusion theory, and lattice Boltzmann method–discrete element method, are generalized. The characteristics and applications of different simulation methods are discussed. Finally, the problems that hinder the implementation of experiments and simulations are identified, and suggestions for clarifying the focus and directions of future research are presented.

Suitable elastic modulus and particle size of preformed particle gel are the keys to both diverting water flow and avoiding permanent impairment to reservoirs. Therefore, the paper aims at finding the best matched preformed particle gel for given reservoirs using sand-pack displacement experiments. The results show that the injection pressure of preformed particle gel with excessively small size and elastic modulus is relatively low, indicating poor capacity to increase flow resistance and reduce water channeling. On the other hand, if the particle size and elastic modulus of preformed particle gel are excessively large, the reservoir may be plugged and irreversibly damaged, affecting oil development performance. In fact, the best matched particle size and elastic modulus of preformed particle gel increase with the increase in reservoir permeability. Furthermore, the paper establishes a quantitative logarithmic model between the particle size of preformed particle gel and reservoir permeability. Finally, the established matching relationship is validated via microscopic visualization oil displacement experiments using a glass etching model. The validation experiments indicate that the preformed particle gel (60–80 mesh; 2–4 Pa) selected according to the matching relationship can effectively reduce water channeling and increase sweeping efficiency by as much as 55% compared with water flooding in the glass etching model with an average permeability of 2624 × 10⁻³ μm². Therefore, the established matching relationship can provide an effective guide when selecting the best suitable preformed particle gel for a given reservoir in more future applications.

The preformed particle gel (PPG) has been proved to be an effective chemical agent to reduce fluid channeling and increase the sweeping efficiency. However, we still lack a clear understanding of the field-scale matching relationship between PPG size, elastic modulus and a heterogeneous reservoir. In this respect, the paper carried out various sand pack displacement experiments. The results indicated that an excessively large PPG or elastic modulus would plug a low-permeability sand pack and even increase the severity of fluid channeling. On the contrary, an excessively small PPG or elastic modulus allowed a certain degree of profile control, but the PPG could easily migrate out of high-permeability sand packs with water. If the elastic modulus remained unchanged, the suitable PPG size increased as the reservoir permeability ratio increased. On the other hand, the suitable elastic modulus increased with the increase of the reservoir permeability ratio when the PPG size was kept the same. By using regression analysis, quantitative expressions were established in order to determine the best suitable PPG size for a certain heterogeneous reservoir. When the elastic modulus was fixed, the best suitable PPG mesh exhibited a linear relation with the permeability ratio. This paper provides a useful reference to select the most convenient PPG size and elastic modulus for a potential heterogeneous reservoir, suitable to enhance oil recovery.

Heterogeneous composite flooding has performed well with regard to enhanced oil recovery after polymer flooding in recent years. In order to significantly increase oil recovery, the development parameters should be designed differently for each well. However, it is difficult to rapidly allocate development parameters through the lowering of computational costs. Therefore, the authors of this paper carried out research to clarify the main controlling factors of parameter allocation. Firstly, the numerical simulation domain was separated into several regions, with injection wells and production wells at the center of each region. The statistical parameters of each region were calculated. Then, the water injection rate, liquid production rate, and chemical agent concentration were allocated based on the proportion of statistical parameters in each region. A large number of development schemes were designed by combining different injection and production allocations that were calculated based on each statistical parameter. Finally, the development performance of each scheme was simulated and analyzed. The statistical parameters corresponding to the best performance scheme were regarded as the main controlling factors of heterogeneous composite flooding after polymer flooding. These results showed that the main controlling factors for the allocation of the water injection rate were pore volume and permeability variation coefficient. The main controlling factors for liquid production rate were the remaining oil saturation, formation coefficient, and reservoir pressure. The main controlling factors for chemical agent concentration were pressure and permeability variation coefficient. These findings concerning the main factors controlling development parameter allocation were validated by practical application in several well groups of an actual reservoir model. This study provides references for improving heterogeneous composite flooding performance for post-polymer flooding reservoirs in the future.