An improved genetic algorithm optimization fuzzy controller applied to the wellhead back pressure control system

Abstract The throttle valve is the core equipment to managed pressure drilling (MPD) technology. PID controller is the most widely used throttle valve control algorithm. However, in the wellhead back pressure control system, the control of the throttle valve has strong nonlinearity and time variability. This makes precise closed-loop control of wellhead back pressure a challenge. The traditional controller needs to be improved in terms of control speed, stability and robustness. To overcome these shortcomings, this paper proposes an improved genetic algorithm optimization fuzzy controller. Firstly the wellhead back pressure control model is established and transfer function is calculated. Secondly, an improved genetic algorithm to optimize the highly nonlinear fuzzy control rules between the input and response in the fuzzy PID controller is designed. Finally, four traditional controllers are compared with the developed model to prove the method is optimal. The proposed controller has excellent performance in terms of time response parameters (such as rise time, adjustment time, overshoot, and steady-state error). The controller exhibits great advantages in terms of speed, stability, and robustness, which significantly improves the performance of the wellhead back pressure control system.

[1]  Randy Lovorn,et al.  Automated Drilling Systems for MPD C-The Reality , 2012 .

[2]  Guorong Wang,et al.  Mathematical Modeling and Validation on a New Valve Core of the Throttle Valve in MPD , 2013 .

[3]  Asad Mehmood,et al.  Automated MPD Control System Helps Navigate Through Drilling Problems, Meets Objectives in Shakal Block, North Iraq , 2016 .

[4]  Haoyong Yu,et al.  Efficient PID Tracking Control of Robotic Manipulators Driven by Compliant Actuators , 2019, IEEE Transactions on Control Systems Technology.

[5]  Y. H. Al-Naumani,et al.  Gas Phase Train in Upstream Oil & Gas Fields: PART-III Control System Design , 2017 .

[6]  Feng Ruan,et al.  Optimal Process Design for Minimum Springback Based on RBF Network and Evolutionary Strategy , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[7]  Meysam Naderi,et al.  Fuzzy logic coupled with exhaustive search algorithm for forecasting of petroleum economic parameters , 2019, Journal of Petroleum Science and Engineering.

[8]  Don M. Hannegan,et al.  MPD - Uniquely Applicable to Methane Hydrate Drilling , 2004 .

[9]  Seyed Mohammad Hossein Mousakazemi,et al.  Robust tuned PID controller with PSO based on two-point kinetic model and adaptive disturbance rejection for a PWR-type reactor , 2019 .

[10]  Farshid Torabi,et al.  Big Data analytics in oil and gas industry: An emerging trend , 2018 .

[11]  A. Hoffmann.  EOS lumping optimization using a genetic algorithm and a tabu search , 2019, Journal of Petroleum Science and Engineering.

[12]  Tong Li,et al.  Risk Assessment and Online Forewarning of Oil & Gas Storage and Transportation Facilities Based on Data Mining , 2017, KES.

[13]  Muawia A. Magzoub,et al.  Managed Pressure Drilling System Using Adaptive Control , 2013, 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation.

[14]  Gang Hu,et al.  Performance study of erosion resistance on throttle valve of managed pressure drilling , 2017 .

[15]  Yaming Wang,et al.  Sliding mode control based on a hybrid grey-wolf-optimized extreme learning machine for robot manipulators , 2019, Optik.

[16]  Dan Li,et al.  A study on wellhead backpressure automatic control system of coalbed methane drilling , 2011, 2011 International Conference on Electrical and Control Engineering.

[17]  Shengnan Wu,et al.  Dynamic risk analysis of hydrogen sulfide leakage for offshore natural gas wells in MPD phases , 2019, Process Safety and Environmental Protection.

[18]  Saurabh Tewari,et al.  Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs , 2019, Comput. Ind. Eng..

[19]  Jialing Zou,et al.  An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System , 2019, IEEE Access.

[20]  Tayfun Babadagli,et al.  Development of mature oil fields — A review , 2007 .

[21]  S. Yasunobu,et al.  A general practical design method for fuzzy PID control from conventional PID control , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[22]  Xiumei Liu,et al.  Investigation of flow characteristics in the U-shaped throttle valve , 2019, Advances in Mechanical Engineering.

[23]  Keith K. Millheim,et al.  The Planning and Drilling of Hot Ice #1 - Gas Hydrate Exploration Well in the Alaskan Arctic , 2005 .

[24]  Henrik Anfinsen,et al.  Bottom hole pressure control for oil well drilling using model reference adaptive control , 2017, 2017 17th International Conference on Control, Automation and Systems (ICCAS).

[25]  Guang-Hong Yang,et al.  Data-Driven Approach to Accommodating Multiple Simultaneous Sensor Faults in Variable-Gain PID Systems , 2019, IEEE Transactions on Industrial Electronics.

[26]  Stephen Butt,et al.  Safety and risk analysis of managed pressure drilling operation using Bayesian network , 2015 .

[27]  Jun Yu Li,et al.  A new two-phase model to simulate sour gas kicks in MPD operations with Water Based Mud , 2017 .

[28]  Bhola Jha,et al.  Moth-Flame Optimization-Based Fuzzy-PID Controller for Optimal Control of Active Magnetic Bearing System , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[29]  Chang-Huang Chen.  A revised bare bone particle swarm optimizer and its variant , 2013, 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY).

[30]  N. Ishak,et al.  Comparison between optimally-tuned PID with self-tuning PID for steam temperature regulation , 2012, 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012).

[31]  Venkata Reddy Kota,et al.  Direct instantaneous torque control of Brushless DC motor using firefly Algorithm based fractional order PID controller , 2020 .

[32]  Ole Morten Aamo,et al.  Control of heave-induced pressure fluctuations in managed pressure drilling , 2012, 2012 American Control Conference (ACC).

[33]  Michael Nikolaou,et al.  Evaluation of control methods for drilling operations with unexpected gas influx , 2013 .

[34]  Naveen K. Chilamkurti,et al.  Ant colony optimization algorithm with Internet of Vehicles for intelligent traffic control system , 2018, Comput. Networks.

[35]  Jing Zhou,et al.  Switched Control for Pressure Regulation and Kick Attenuation in a Managed Pressure Drilling System , 2011, IEEE Transactions on Control Systems Technology.

[36]  Robert K. Perrons,et al.  Data as an asset: What the oil and gas sector can learn from other industries about “Big Data” , 2015 .

[37]  Muhammad Junaid Khan,et al.  Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm , 2019, Future Gener. Comput. Syst..

[38]  Yanjie Liu,et al.  Burner-electrode position control of calcium carbide furnace based on BP-PID controller , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).