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.

Since pressure while drilling (PWD) has the disadvantages of single-point measurement and high cost of application, a micro-measurer based on MEMS (micro-electromechanical systems) sensor technology, which can measure downhole temperature, pressure, magnetic field, and dynamic signal, has been developed to achieve real-time, efficient, and accurate measurement of multiple parameters in the well. The kernel circuit system is the core of measurement and control, and the shell plays the role in protecting the kernel circuit. The shell of the micro-measurer is made of preferably selected materials with high-temperature and high-pressure resistance, corrosion resistance, small size, and low density, which can adapt to working in drilling fluid for a long time. The micro-measurer uses integrated interfaces on the shell to enable communication between the host computer and measuring machine. Based on the field test, both the functional integrity and data measurement accuracy of the micro-measurer are verified. Through analysis of the measured data, the profile of the downhole temperature field is constructed. The physical phenomena reflected by the measured magnetic field signal and dynamic signal are consistent with the actual working conditions observed in the test. Hence, as a new microchip measuring device, the micro-measurer can better serve the drilling engineering field and provide technical support for real-time measurement of downhole parameters in the future.