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Control Strategy For Subsea Mudlift Drilling System

Posted on:2012-03-11Degree:MasterType:Thesis
Country:ChinaCandidate:J L YanFull Text:PDF
GTID:2131330338493729Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
Deep-sea drilling has to face some probloms due to its narrow window of mud dentisy. Dual-gradient-drilling can solve these probloms properly. Its basic idea is to create two different mud gradient in the same size of borehole in order to increase the mud density margin. So with the same plume pressure, the depth of drilling can be increase greately.The present paper is sponsored by"Development of large scale oil&gas field and coalbed gas"National science and technology major special project, research is carried on the strategy and design of monitoring and control system for subsea mudlift drilling(SMD) system, which is a mature program of dua-gradient-drilling.Based on the study of the related technology abroad, a systematic study on works, processes, working conditions of SMD is carried. Considering with the actual working conditions and processes, an program of system operation of pump, valve and its experimental verification on the SMD experimental platform is proposed.Combining the basic funciton and composition of SMD experimental platform, an complete monitoring system for SMD experimental platform is developed. Motion configuration for subsea mudlift system of SMD experimental platform is given, which provides a reference for motion configuration of actual system.Fuzzy algorithm is studied emphatically. The process of design, effect of application of the controller based on the algorithm for subsea mudlift system of SMD experimental platform is given.As subsea mudlift system works in a harsh environment, with a lot of uncertain and nonlinear factors, it's hard to model it directly and accurately. So the modeling method using PID neural network which doesn't need much mathmatic model is selected to model the system.
Keywords/Search Tags:subsea mudlift drilling(SMD), monitoring and controlling system, fuzzy-control algorithm, PID neural network(PIDNN), system identification
PDF Full Text Request
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