The Application Of Internet Of Things Technology In Intelligent Monitoring Of Offshore Equipments

Advanced offshore equipment is essential for efficient exploration and production of offshore oil and gas,and offshore drilling system and offshore platform are two important ones mostly relevant to oil production.Due to the development of the sensor technology and the data transfer technology,on-site monitoring of offshore equipments has been realized,but it is still at the preliminary stage of intelligent monitoring research.Aiming at the technical development of monitoring of major offshore equipments,this thesis applies the network layer and expert layer technologies of Internet of Things to realize the automation of drilling monitoring and the intelligentization of platform monitoring.Rotary steerable drilling system is the key technique in the steering control of the bottom hole assembly for offshore gas and oil drilling at present.Drilling fluid pulse is the main communication mode of rotary steering drilling.Transmission code and its synchronization are demanding due to the transmission characteristics,such as low speed,propagation delay and high bit error rate.Furthermore,an effective ground monitor system is required for on-site engineers to master the conditions of guide bit and the control of trajectory.Focusing on the problems above,the network layer technology of Internet of Things is applied to design a communication protocol between upper and lower computer,and downward instruction coding scheme is proposed.The corresponding communication and control system is developed,which can accomplish transmission of control instructions and real-time display of drilling parameters,and the network interconnection between upper and lower computers is established.The offshore platform is the traditional marine equipment for offshore oil and gas production.Based on a large number of prototype monitoring data of the platform,applying expert decision-making of the application layer of the Internet of Things to evaluate the state of the platform structure,timely detect structural anomalies or damages,is of great significance to the structural health monitoring(SHM)of the platform.Aiming at the dynamic characteristics analysis and damage identification of nonlinear offshore platforms excited by sea ice,a method to structurally identify dynamic characteristics is proposed based on long-term monitored acceleration data of anti-ice jacket platforms in Bohai Sea.Not all the inherent characteristics of the platform can be excited due to the randomness of ice loading and its limited bandwidth.However,the long-term monitored data can reflect most of the conditions of sea-ice excitation.The change of natural characteristics of the platform under ice loads can be identified by analyzing of long-term monitored data.A nonlinear system of two-degree-of-freedom(2DOF)is simulated to identify dynamic characteristics and verify the effectiveness of the proposed method.The method is applied to analyze the acceleration data caused by ice-induction at a jacket platform in Liaodong Bay.The inherent characteristics of the structure can be identified and its variation under different ice-load intensities is summarized.After the extraction of structural characteristics,structural damages can be diagnosed according to changes in dynamic characteristics.Based on the experimental model of a jacket platform,an intelligent damage identification method based on structural response data is proposed.The extracted structural dynamic features are input into a SVM classifier to be classified and compared,and an intelligent classification model is established.A numerical simulation verify the extracted features can reflect the changes of the inherent characteristics of the system,and the method is applied to the damage diagnosis of a jacket platform model,which comes out to be able to identify structural changes in the experimental model.