Because of the problems, such as the lack of an electric power diagram atlas under different working conditions and the difficulty in intelligent diagnosis of variable torqued pumping unit wells, this paper proposes a diagnosis model of working conditions based on feature recognition. The mathematical relationship model between the polished rod load and motor output power is derived based on the analysis of geometric structure, motion law, and process of energy transformation and transfer of the variable torque pumping unit. It can calculate the electric power diagram based on a dynamometer card. On this basis, the electric power diagram atlas is created, and the feature analysis and eigenvalue extraction of the electric power diagrams under different working conditions are carried out to realize the direct diagnosis of the working conditions in the variable torque pumping unit wells. The application and analysis of examples show that the electric power diagram atlas created in this paper has good practicability, and the working condition diagnosis model accuracy is high. It can provide a theoretical basis and technical support for the intelligent diagnosis of oil production working conditions and improve the intellectual management level of the oilfield, which is conducive to reducing production management costs and improving the oilfield’s production efficiency and benefits.

Most of the oil reservoirs in China are fluvial deposits with firm reservoir heterogeneity, where differences in fluid flow capacity in individual directions should not be ignored; however, the available commercial reservoir simulation software cannot consider the anisotropy of the relative permeability. To handle this challenge, this paper takes full advantage of the parallelism of the multi-scale finite volume (MsFV) method and establishes a multi-scale numerical simulation approach that incorporates the effects of reservoir anisotropy. The methodology is initiated by constructing an oil–water black-oil model considering the anisotropic relative permeability. Subsequently, the base model undergoes decoupling through a sequential solution, formulating the pressure and transport equations. Following this, a multi-scale grid system is configured, within which the pressure and transport equations are progressively developed in the fine-scale grid domain. Ultimately, the improved multi-scale finite volume (IMsFV) method is applied to mitigate low-frequency error in the coarse-scale grid, thereby enhancing computational efficiency. This paper introduces two primary innovations. The first is the development of a multi-scale solution method for the pressure equation incorporating anisotropic relative permeability. Validated using the Egg model, a comparative analysis with traditional numerical simulations demonstrates a significant improvement in computational speed without sacrificing accuracy. The second innovation involves applying the multi-scale framework to investigate the impact of anisotropy relative permeability on waterflooding performance, uncovering distinct mechanisms by which absolute and relative permeability anisotropy influence waterflooding outcomes. Therefore, the IMsFV method can be used as an effective tool for high-resolution simulation and precise residual oil prediction in anisotropic reservoirs.

The long-term stability of bulk nanobubbles is crucial for their functional applications; however, understanding the evolution of their size distribution remains a significant challenge. While conventional characterization methods, such as Dynamic Light Scattering and Nanoparticle Tracking Analysis, provide size information, they are often sample-intensive and expensive, making them ill-suited for high-throughput or long-term dynamic monitoring of size distribution polydispersity. This research validated UV–Vis spectrophotometry as a simple, powerful tool for tracking these dynamic changes. Air nanobubbles generated via pressure oscillation-hydraulic cavitation were systematically monitored over 30 days using correlative DLS, NTA, and UV–Vis spectroscopy. A distinct two-stage evolution was identified: an initial “purification” phase marked by the dissolution of unstable bubbles, followed by a long-term “maturation” phase governed by Ostwald ripening. The Ångström exponent (n), derived from the full extinction spectrum, is a highly sensitive descriptor of this process. The evolution of n traced a unique V-shaped trajectory, which resulted in a pronounced hysteresis loop when plotted against the mean diameter from DLS. This hysteresis reveals that systems with identical mean diameters can possess vastly different distribution morphologies, which are inaccessible through traditional sizing methods alone. This research establishes full-spectrum UV–Vis analysis as a robust methodology, enabling rapid and efficient assessment of nanobubble stability and providing a deeper mechanistic understanding of their complex evolution.

A mathematical simulation model of a beam pumping system with frequency conversion control is established, considering the influence of the real-time frequency variation on the motion law of a pumping unit, the longitudinal vibration of a sucker rod string, the crankshaft torque, and the motor power. On this basis, the key links such as state space, action space, and reward function are defined by using deep reinforcement learning theory, and an intelligent model to optimize the frequency modulation for a beam pumping system based on deep reinforcement learning is constructed. The simulation and field application results show that the frequency optimization model can significantly reduce the fluctuation amplitude of the polished rod load, crankshaft torque, motor power, and input power of the system, making the operation of the pumping system more stable and energy-saving. More importantly, the model can realize the independent learning and control of the corresponding parameters without manual intervention to ensure the normal operation of the system and improve the level of information and intelligent management of oil wells.

Water well pressure-driven reconstruction can replenish reservoir energy in a short time, significantly enhancing water injection capacity, forming effective displacement pressure differentials, and increasing oil well liquid production capacity. It is an effective means to resolve contradictions in developing low-permeability reservoirs. Addressing the complex fracture zones formed during pressure-driven processes involves conducting pressure-driven core experiments to ascertain the law of effective stress and permeability changes in target reservoirs, and establishing constitutive equations for dynamic permeability changes. The fitting coefficient of the equation to the experimental data reaches 0.99. The main fractures formed during the pressure drive control the direction of the modified zone. The simulation results show that the length of the main fractures ranges from 32 to 256 m, and a microfracture zone of 0 to 50 m is formed along the sides of the main fractures. Compared to conventional water injection pressure drives, the injection capacity is enhanced by 4 to 8 times. simulations show that employing dynamic permeability equations for pressure-driven reconstruction zones achieves good fitting of bottom-hole pressures under high injection conditions.