An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling
Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeat...
Ausführliche Beschreibung
Autor*in: |
Zhang, He [verfasserIn] Yuan, Xiru [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 32(2019), 6 vom: 13. Apr., Seite 1581-1592 |
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Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:6 ; day:13 ; month:04 ; pages:1581-1592 |
Links: |
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DOI / URN: |
10.1007/s00521-019-04192-y |
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Katalog-ID: |
SPR038969440 |
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245 | 1 | 3 | |a An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling |
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520 | |a Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. | ||
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650 | 4 | |a Throttle valve |7 (dpeaa)DE-He213 | |
700 | 1 | |a Yuan, Xiru |e verfasserin |4 aut | |
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10.1007/s00521-019-04192-y doi (DE-627)SPR038969440 (SPR)s00521-019-04192-y-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zhang, He verfasserin aut An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. Managed pressure drilling (dpeaa)DE-He213 Wellhead back pressure (dpeaa)DE-He213 Pressure control (dpeaa)DE-He213 Throttle valve (dpeaa)DE-He213 Yuan, Xiru verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 6 vom: 13. Apr., Seite 1581-1592 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:6 day:13 month:04 pages:1581-1592 https://dx.doi.org/10.1007/s00521-019-04192-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 6 13 04 1581-1592 |
spelling |
10.1007/s00521-019-04192-y doi (DE-627)SPR038969440 (SPR)s00521-019-04192-y-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zhang, He verfasserin aut An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. Managed pressure drilling (dpeaa)DE-He213 Wellhead back pressure (dpeaa)DE-He213 Pressure control (dpeaa)DE-He213 Throttle valve (dpeaa)DE-He213 Yuan, Xiru verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 6 vom: 13. Apr., Seite 1581-1592 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:6 day:13 month:04 pages:1581-1592 https://dx.doi.org/10.1007/s00521-019-04192-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 6 13 04 1581-1592 |
allfields_unstemmed |
10.1007/s00521-019-04192-y doi (DE-627)SPR038969440 (SPR)s00521-019-04192-y-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zhang, He verfasserin aut An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. Managed pressure drilling (dpeaa)DE-He213 Wellhead back pressure (dpeaa)DE-He213 Pressure control (dpeaa)DE-He213 Throttle valve (dpeaa)DE-He213 Yuan, Xiru verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 6 vom: 13. Apr., Seite 1581-1592 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:6 day:13 month:04 pages:1581-1592 https://dx.doi.org/10.1007/s00521-019-04192-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 6 13 04 1581-1592 |
allfieldsGer |
10.1007/s00521-019-04192-y doi (DE-627)SPR038969440 (SPR)s00521-019-04192-y-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zhang, He verfasserin aut An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. Managed pressure drilling (dpeaa)DE-He213 Wellhead back pressure (dpeaa)DE-He213 Pressure control (dpeaa)DE-He213 Throttle valve (dpeaa)DE-He213 Yuan, Xiru verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 6 vom: 13. Apr., Seite 1581-1592 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:6 day:13 month:04 pages:1581-1592 https://dx.doi.org/10.1007/s00521-019-04192-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 6 13 04 1581-1592 |
allfieldsSound |
10.1007/s00521-019-04192-y doi (DE-627)SPR038969440 (SPR)s00521-019-04192-y-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zhang, He verfasserin aut An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. Managed pressure drilling (dpeaa)DE-He213 Wellhead back pressure (dpeaa)DE-He213 Pressure control (dpeaa)DE-He213 Throttle valve (dpeaa)DE-He213 Yuan, Xiru verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2019), 6 vom: 13. Apr., Seite 1581-1592 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2019 number:6 day:13 month:04 pages:1581-1592 https://dx.doi.org/10.1007/s00521-019-04192-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 32 2019 6 13 04 1581-1592 |
language |
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Zhang, He @@aut@@ Yuan, Xiru @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR038969440</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110191242.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-019-04192-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR038969440</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00521-019-04192-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, He</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Managed pressure drilling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wellhead back pressure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pressure control</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Throttle valve</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yuan, Xiru</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">London : Springer, 1993</subfield><subfield code="g">32(2019), 6 vom: 13. 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Zhang, He |
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Zhang, He ddc 004 bkl 54.72 misc Managed pressure drilling misc Wellhead back pressure misc Pressure control misc Throttle valve An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling |
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004 ASE 54.72 bkl An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling Managed pressure drilling (dpeaa)DE-He213 Wellhead back pressure (dpeaa)DE-He213 Pressure control (dpeaa)DE-He213 Throttle valve (dpeaa)DE-He213 |
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ddc 004 bkl 54.72 misc Managed pressure drilling misc Wellhead back pressure misc Pressure control misc Throttle valve |
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improved particle swarm algorithm to optimize pid neural network for pressure control strategy of managed pressure drilling |
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An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling |
abstract |
Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. |
abstractGer |
Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. |
abstract_unstemmed |
Abstract The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. |
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container_issue |
6 |
title_short |
An improved particle swarm algorithm to optimize PID neural network for pressure control strategy of managed pressure drilling |
url |
https://dx.doi.org/10.1007/s00521-019-04192-y |
remote_bool |
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author2 |
Yuan, Xiru |
author2Str |
Yuan, Xiru |
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doi_str |
10.1007/s00521-019-04192-y |
up_date |
2024-07-03T21:03:22.430Z |
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score |
7.3993483 |