The rate of penetration (ROP) is an important indicator affecting the drilling cost and drilling performance. Accurate prediction of the ROP has important guiding significance for increasing the drilling speed and reducing costs. Recently, numerous studies have shown that machine learning techniques are an effective means to accurately predict the ROP. However, in petroleum engineering applications, its robustness and generalization cannot be guaranteed. The traditional empirical model has good robustness and generalization ability. Based on the quantification of data similarity, this paper establishes a hybrid model combining a machine learning method and an empirical method, which combines the high prediction accuracy of the machine learning method with the good robustness and generalization of the empirical method, overcoming the shortcomings of any single model. The AE-ED (the Euclidean Distance between the input data and reconstructed data from the autoencoder model) is defined to measure the data similarity, and according to the data similarity of each new piece of input data, the hybrid model chooses the corresponding single model to calculate. The results show that the hybrid model is better than any single model, and all the evaluation indicators perform better, making it more suitable for the ROP prediction in this field.

Under different temperatures and pressures, the physical parameters of drilling fluid will change, resulting in inaccurate drilling hydraulic calculations. Aiming to address the problem of the traditional rheological prediction method needing to first determine the rheological model, this paper proposed a method for first predicting the readings of the rheometer and then determining the rheological model. The model established in this paper adopted a parameter-free method, which expands the application range of the model. Rheology experiments were carried out on the three types of oil-based drilling fluids collected at the well site. The model in this paper was verified based on the experimental data. The results showed that, compared with the traditional drilling fluid rheological prediction method, the model established in this paper had a better prediction effect, with an average error of 4.85%, and the average error reduction ranges from 3.8% to 8.3%. The model established in this paper is able to provide theoretical support for accurate hydraulic calculation.

Continuous-wave mud generator is widely adopted for real-time downhole data transmission of deep wells in the oil industry. There have been many studies on rotor structure optimization of continuous rotary valve tools but few studies on the different optimization mechanisms of oscillating shear valve tools. In this paper, a theoretical model of pressure wave generation for oscillating shear valve tools was presented, and the reason for the non-sinusoidal waveform was analyzed theoretically for the first time. A series of rotor rotation strategies were proposed to improve the sinusoidal properties of pressure waves without changing the mechanical structure of the rotor. Finally, CFD with different parameters was conducted to verify the feasibility of the optimization scheme. The numerical results give the recommended range of parameters under different control schemes. In addition, the relationship between throttle pressure drop and pressure wave waveform is found through comparison with previous research of the team, which provides a new research idea for the optimization of the pressure wave in the future.

Accurate identification of overflow fluid types facilitates timely and effective handling of onsite overflow accidents. Research into identifying the type of overflow fluid is limited, and there are only simple calculation models that do not consider enough effects; additionally, accuracy needs to be improved and the identification method is not perfect. If there is no drilling data, it is impossible to identify the overflow fluid. Therefore, this paper modifies the density calculation model of overflow fluid by considering the influence of the temperature, pressure field, and two-phase flow model, making the calculation result more accurate and universal, and puts forward a comprehensive method for auxiliary identification based on gas logging interpretation. This paper uses the gas state equation to verify the accuracy of the overflow density model; after verification using data from more than 20 overflowing wells, the method was found to be practical and had an accuracy rate of more than 90%. Thus, this study and the proposed method can provide guidance for dealing with overflow accidents in the field and any follow-up research.

With the continuous growth in global energy demand, the exploration and development of hydrates has been the focus of increasing attention, and the accurate evaluation of the mechanical properties of hydrate layers has become particularly important. In this study, using a self-developed hydrate sample preparation device and hydrate triaxial seepage test platform, triaxial shear tests were carried out using the in situ synthesis method for hydrate sediment in the laboratory, and the stress–strain curves of hydrate sediment with different levels of saturation were obtained. By analyzing the stress–strain curve, the mechanical parameters of hydrate sediment were calculated and simulated using ABAQUS (2021, Dassault systemes, Vélizy Villacoublay France) finite element software. Several p-y curves were calculated and compared with the simulation results, and the p-y curve correction method of the hydrate layer in a shallow seabed was obtained. It was found that the strength of the hydrate sediment increased with an increase in saturation. At the same time, an increase in confining pressure and a decrease in temperature also increased the strength of hydrate deposits. Through comparison with the existing API (American Petroleum Institute) standard p-y curve, it was found that its strength is low because the existence of the hydrate improves the formation strength.