The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 parameters that may affect the occurrence of complex downhole accidents, the mud overflow and leakage are predicted. The prediction results of the model are compared with the prediction results of a conventional BP neural network, and verified by the real drilling data. The results show that the MAE, MSE, and RMSE of the GA-BP neural network model are improved by 2.91%, 4.48%, and 10.93%, respectively, compared with the BP neural network model, and the prediction quality is higher. Moreover, the amount of mud overflow and leakage predicted by using the GA-BP neural network matches well with the pattern of mud overflow and leakage data in real drilling, which proves the effectiveness and accuracy of the GA-BP neural network in overflow and leakage prediction.

In deep unconventional oil and gas development, the problem faced is that PDC bits are eroded by solid-liquid high-speed fluids, resulting in damage. It has led to serious damage to the stability of the drill bit, a decrease in the service life of the drill bit, and an increase in the difficulty in efficient drilling. The essence is that the surface hardness and erosion resistance of the drill bit are not strong enough. Therefore, improving the stability of drill bits is a crucial and urgent problem to be solved. In this paper, Ni60A + 20% WC + 0.3% graphene composite coatings were prepared on a Q235 steel substrate, which is a new type of high hardness coating. Moreover, the effects of microstructure and microhardness of the composite coatings at different laser powers (800 W, 1200 W, 1600 W, and 2000 W) were investigated. The results show that the laser power can significantly affect the microstructure of the coating. The phase composition of the composite coatings is essentially the same at different laser powers. However, there are significant differences in the content of each phase. When the laser power is higher than 1200W, the content of M₂₃C₆, Cr₃C₂ and Fe₃C in the composite coating increases and the microhardness of the coating decreases. When the laser power is below 1200 W, the dilution rate of the substrate is low and a metallurgical bond cannot be formed between the composite coating and the substrate.

The intelligent operation and maintenance of gas injection and production in salt-cavern compressed air energy storage means adjusting or optimizing the gas injection and production plan in real-time according to the volume of injected and produced gas under the premise of safety and economy. The key is how to optimize the wellbore allocation plan for gas injection and production. Therefore, based on the improved random forest algorithm and the gas injection and production conditions, this paper establishes an improved random forest algorithm to obtain a set of wellbore allocation plans with a priority order. In combination with the cutting particle motion model and the pressure-flow prediction model for gas injection and production, constraints are set on the cutting-carrying size, erosion flow rate, and minimum injection and production flow rate. An intelligent allocation solution algorithm for injection and production wellbores of salt-cavern compressed air energy storage under the dual constraints of safety and economy is established, and a case analysis is conducted to prove the necessity of the model. Furthermore, the impacts of economic constraints, total gas injection and production volume, wellbore diameter, and injection and production media on the wellbore allocation for injection and production are explored. The research results show that ① The wellbore allocation results obtained from the model established in this paper can avoid safety hazards and economic losses during the gas injection and production process, enabling the gas storage reservoir to operate more stably and efficiently. ② The ground cutting treatment capacity has a significant impact on the injection and production wellbores, and it is necessary to select appropriate equipment for ground cutting treatment. ③ As the total planned volume of gas injection and production increases, more injection and production wells are required. The maximum flow rate of a single wellbore in the overall gas injection condition decreases, and the maximum flow rate of a single wellbore in the overall gas production condition also decreases. Moreover, the larger the size of the injection and production wellbores, the higher the priority of their allocation. The wellhead pressure is not affected by the above factors. ④ Gases with a relatively low gas density as the injection and production medium will require more injection and production wells during the injection and production process. The flow rate of a single injection and production well is larger; the cutting-carrying size is larger, and a higher wellhead pressure is needed. The research results can provide theoretical guidance for the accurate prediction of the intelligent allocation method of injection and production wellbores in salt-cavern compressed air energy storage and contribute to ensuring the safe, economic, and efficient operation of salt-cavern gas storage reservoirs in China.

Multi-gradient drilling is a new offshore drilling method. The accurate calculation of the related wellbore temperature is of great significance for the prediction of the gas hydrate formation area and the precise control of the wellbore pressure. In this study, a new heat transfer model is proposed by which the variable mass flow is properly taken into account. Using this model, the effects of the main factors influencing the wellbore temperature are analyzed. The results indicate that at the position where the separation injection device is installed, the temperature increase of the fluid in the drill pipe is mitigated due to the inflow/outflow of hollow spheres, and the temperature drop of the fluid in the annulus also decreases. In addition, a lower separation efficiency of the device, a shallower installation depth and a smaller circulating displacement tend to increase the temperature near the bottom of the annulus, thereby helping to reduce the hydrate generation area and playing a positive role in the prevention and control of hydrates in deepwater drilling.