A high tide velocity makes it difficult for the riser to enter the borehole during its running after the riser section is drilled. A finite?element fluid?solid coupling model of fluid (seawater) and solid (casing) interaction is built with the finite?element analysis software ADINA. The following observations can be made from the analysis. A higher velocity results in a larger lateral offset in the final equilibrium state when the riser enters the borehole.Due to the flow blocking effect of the riser, seawater near the front end of the riser produces a strong detouring flow, while a local vortex is observed in the area near the back end of the riser.Under four working conditions of different tide velocities (0.60,0.70,0.80 and 0.90 m/s), both the Reynolds number and the equivalent flow resistance coefficient are in reasonable ranges.When the borehole diameter reaches 889.0 mm after borehole enlargement, a 508.0 mm riser is run in. No obstruction is encountered when the tide velocity is less than 0.59 m/s, whereas difficult borehole entering occurs when the tide velocity is higher than 0.83 m/s. It is suggested that the riser will be run in when the tide velocity reduces to below 0.59 m/s. The simulation analysis has a great guiding significance for the running of the riser during the drilling of offshore oil fields.

Accurately predicting the power consumption of crude oil pipelines is conducive to controlling the energy consumption level of such pipelines and fully tapping the energy saving potential of crude oil pipeline transportation systems. Actual operation data of such pipelines have the characteristics of large fluctuation range serious noise interference, and information redundancy, which affect the accurate prediction of pipeline power consumption. To solve these problems, this paper proposes a power consumption prediction model based on a hybrid neural network. The daily operation data of crude oil pipelines are decomposed by complete ensemble empirical mode decomposition with adaptive noise. Principal component analysis is performed to reduce the dimensions of the decomposed data. The improved particle swarm optimization algorithm is applied to adjust the structural parameters of the neural network. The proposed model is applied to predict the power consumption of a crude oil pipeline and compared with some common prediction models. The results show that the decomposition algorithm can improve the prediction accuracy of the model. The hybrid neural network model has the highest prediction accuracy. The average absolute error of the test set is 5.394%, which is 39.200% lower than that before the decomposition algorithm is used.

Middle?deep strata of Bohai oilfield is the focus of further exploration and development, of which Bozhong X is a typical mid?deep oil and gas field. The main reservoir with dense lithology and strong abrasiveness is stored for more than 4 000 m depth, resulting in much more complex situations during the early drilling stage, which seriously restricted the reservoir exploration process. In order to solve the problems of complex pressure, serious tool choke and slow penetration rate in the deep?medium strata, this work figured out TorkBuster and Ninja teeth PDC bit efficiency improvement technology and its corresponding applications, using stratigraphic features of Bozhong X oil and gas field as a research sample. The rock breaking mainly involves combination of crushing and rotary shearing, which is mainly used to improve the drilling speed while ensuring the well quality. The TorkBuster with special Ninja teeth PDC bit in BZ?E well can properly overcome the high hardness and compact of granite gneiss, which can increase the drilling speed and prolong the service life of the bit at the same time. It provides a new method for improving the drilling speed in the deep?medium strata of Bohai oilfield and saving drilling cost.