导管架平台设计中的海洋水文气象参数的统计计算
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摘要
边际油田的开发是集海洋油气资源之钻井、采油工程及海洋工程为一体的系统工程。如何提供既安全又经济的海洋环境条件设计标准,对于这一系统中的任何一种工程结构型式,都是至关重要的。由于海洋环境条件的复杂性、随机性和多样性,传统的海洋环境条件设计标准无法计算多种环境条件同时出现的概率,往往过高地估计环境条件设计参数,造成不必要的投资浪费,甚至使得部分边际油田不适于开采。目前,在如何降低边际油田开发建设成本成为制约海上油田能否开发建设的核心问题。
本论文则从海洋钻采平台所处环境条件出发,以我国渤海某区1970年至1993年风暴过程的后报资料为基础,针对该区域风暴过程的特点,考虑风暴发生频次的影响,应用联合分布理论,尝试引入二维最大熵分布模式来进行海洋环境荷载的统计分析,并应用几种不同的推算方法来确定风浪参数设计标准,比较择优,合理降低工程设计参数,从而达到降低投资成本,最终实现边际油田开采的目的。
本论文将在统计分析渤海某区域风、浪边缘分布的基础上,构造二维联合分布,提出既能保证边际油田开发结构物的安全、又可降低经济投资的设计标准。主要研究内容如下：
(1)基于年极值法和过阈法的风、浪边缘分布的长期统计分析；
(2)单一因素极值分布拟合法探讨；
(3)二维等效最大熵分布模式的提出；
(4)边际油田开发中平台结构风浪设计标准的提出与比选。
Oil is important resource for social and economic development in every country. Marginal oil field exploitation is a system engineering including drilling operation, oil extraction, and platform construction. It plays an important role to propose ocean environmental conditions criteria for structural safety and investment reduction. As the complexity randomness and diversity of the marine environment, the probability of variety environmental conditions can not be calculated by the traditional design standards for the marine environment. Traditional design standards often overestimated the design parameters, resulting in unnecessary waste of investment. At present, how to reduce the costs of marginal oil field development and construction is the core issues. Based on observed and hindcast data, Bivariate Poisson maximum entropy distribution is adopted to calculate the probability of extreme wind velocity and wave height. Different statistical models are compared. Calculation results are given as decision-making references for departments of government, planning, and design. The main content of this study is as follows:
(1) The long-term statistical analysis of marginal distribution is used in this paper to predict the values of extreme wind and wave surge by both the Annual Maximum Method and the Peak Over Threshold Method;
(2) Calculate and compare method of single factor extreme value distribution;
(3) Proposed bivariate Poisson maximum entropy distribution;
(4) Bivariate Poisson maximum entropy distribution is used to estimate the return values of wind velocity and wave height hindcasted in offshore area of bohai. Different criteria of design parameters are compared for marginal oil field exploitation.
本论文则从海洋钻采平台所处环境条件出发,以我国渤海某区1970年至1993年风暴过程的后报资料为基础,针对该区域风暴过程的特点,考虑风暴发生频次的影响,应用联合分布理论,尝试引入二维最大熵分布模式来进行海洋环境荷载的统计分析,并应用几种不同的推算方法来确定风浪参数设计标准,比较择优,合理降低工程设计参数,从而达到降低投资成本,最终实现边际油田开采的目的。
本论文将在统计分析渤海某区域风、浪边缘分布的基础上,构造二维联合分布,提出既能保证边际油田开发结构物的安全、又可降低经济投资的设计标准。主要研究内容如下：
(1)基于年极值法和过阈法的风、浪边缘分布的长期统计分析；
(2)单一因素极值分布拟合法探讨；
(3)二维等效最大熵分布模式的提出；
(4)边际油田开发中平台结构风浪设计标准的提出与比选。
Oil is important resource for social and economic development in every country. Marginal oil field exploitation is a system engineering including drilling operation, oil extraction, and platform construction. It plays an important role to propose ocean environmental conditions criteria for structural safety and investment reduction. As the complexity randomness and diversity of the marine environment, the probability of variety environmental conditions can not be calculated by the traditional design standards for the marine environment. Traditional design standards often overestimated the design parameters, resulting in unnecessary waste of investment. At present, how to reduce the costs of marginal oil field development and construction is the core issues. Based on observed and hindcast data, Bivariate Poisson maximum entropy distribution is adopted to calculate the probability of extreme wind velocity and wave height. Different statistical models are compared. Calculation results are given as decision-making references for departments of government, planning, and design. The main content of this study is as follows:
(1) The long-term statistical analysis of marginal distribution is used in this paper to predict the values of extreme wind and wave surge by both the Annual Maximum Method and the Peak Over Threshold Method;
(2) Calculate and compare method of single factor extreme value distribution;
(3) Proposed bivariate Poisson maximum entropy distribution;
(4) Bivariate Poisson maximum entropy distribution is used to estimate the return values of wind velocity and wave height hindcasted in offshore area of bohai. Different criteria of design parameters are compared for marginal oil field exploitation.
引文
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[4]Mises R V. La Distribution De la Plus Grand de n waleurs. Rev Math, Berlin:Union Interbalk,1936
[5]B GNEDENKO Sur. la Distribution Limite Du terme Dune Serie Aleatoire. Ann Math,1943,44:423-453
[6]GUMBEL E J. Statistics of Extreme Limite, New Yok:Columbia University Press,1958
[7]Resnick S I. Extreme values, regular variation, and point processes. New York:Springer,1987
[8]Bourgund U, Buche C G. Importance sampling procedure using design point(ISPUD). Research Report of University of Innsburch,1986,1-158
[9]Liu P-L, Kiureghian D A. Multivariate distribution models with prescribed marginals and covariances. Probabilitic Engineering Mechanics,1986,1(2):105-112
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[11]Zzchary S. Feld G. Ward G. Wolfram J. Multivariate extrapolation in the offshore environment. Applied Ocean Research,1998,20:237-295
[12]Pickands. Multivariate extreme value distribution. In Proc.43rd Session I.S.I, (Buenos Aires),1981,859-878
[13]Coles S G, Tawn J A. Modelling extreme multivariate events. J. R. Statist. Soc. B,1991,53(2):377-392
[14]Gumbel E J. Distributions de values extremes en plusieurs dimensions. Publ. Inst. Statist. Paris,1960,9: 171-173
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[24]Tawn J A. Bivariate extreme value theory models and estimation. Biometric,1988,75(3):397-415
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[34]俞聿修.随机波浪及其工程应用.大连：大连理工大学出版社,2000
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[37][苏]EΓ勃洛希诺夫.河川径流量的概率分布(张永胜,陈宝,蒋蓉,译者).武汉：长江出版社,2005
[38]董胜.工程设计Weibull分布参数拟合的改进方法.青岛海洋大学学报,1999,29(1)：135-141
[39]Weibull W. A statistical distribution of wide applicability. Journal of Applied Mechanics,1951,18:293-297
[40]董胜,刘德辅,孔令双.极值分布参数的非线性估计及其工程应用.海洋工程,2000,18(1)：50-55
[41]金光炎.广义极值分布及其在水文中的应用.水文,1998,(2)：9-15
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[46]董爱红.广义Pareto分布及其在水文频率分析中的应用：[博士学位论文].南京：河海大学水资源环境学院,2005
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[49]中华人民共和国行业标准.海港水文规范(JTJ213-98).北京：人民交通出版社,2000
[50]史道济.实用极值统计方法.天津：天津科学技术出版社,2006
[51]Howard R, Charles A, Klimko L A. Maximum Likelihood Estimation with the Weibull Model. Journal of the American Statistical Association March.1974,169(345):246-249
[52]王蓉华,徐晓岭.对数正态分布参数的极大似然估计.上海师范大学学报,2000,29(1)：39-43
[53]魏宗叙译.统计学数学方法.上海：上海科技出版社,1966：184-197
[54]刘德辅,施建刚,周志刚.蒙特-卡洛方法在海洋工程设计中的应用.海洋学报,1990,12(2)：236-243
[55]宗德敦.概率权重矩法及其在P-Ⅲ型分布中的应用.水利学报,1988(3)：25-32
[56]董双林Gumbel分布函数形式的选取对参数似然估计的影响.科技通报,1987,3(4)：205-213
[57]Landwehr J M, et al. Probability Weighted Moments Compared with Some Traditional Technique in Estimating Gumbel Parameters and Quantiles. Water Resources Research,1979,15(5)
[58]曲延碌等.三参数Weibull分布的参数估计.气象学报,1987,45(3)：274-278
[59]石原健二.论气象极值的重现期.陆家机译.海洋译丛.北京：海洋出版社,1984
[60]刘桂海等.我国近海波浪要素的长期分布.港口工程,1989(6)：11-15
[61]葛明达.连云港波高周期统计分布.海洋工程,1984,(1)：48-49
[62]Ochi M K. On long-term statistics for ocean and coastal waves. Proc 16th Coastal Engrg Conf.1978,2:59-75
[63]Gumbel E J. Multivariate extreme distributions. Bulletin of the International Statistical Institute,1960,39(2): 471-475
[64]Gumbel E J, Mustafi C K. Some analysis properties of bivariate extreme distribution. J Am Stat Assoc,1967, 62:569-588
[65]周道成,段忠东.耿贝尔逻辑模型在极值风速和有效波高联合概率分布中的应用.海洋工程,2003,21(2)：45-51
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[2]American Petroleum Institue (API). Planning, designing and constructing fixed offshore platforms-Load and resistance factor design.1995,127-128
[3]Bortkiewicz L V. Variationsbreite and Mittlerer Fehler, Sitzungsber Berlin Math. Ges,1992,21:3-11
[4]Mises R V. La Distribution De la Plus Grand de n waleurs. Rev Math, Berlin:Union Interbalk,1936
[5]B GNEDENKO Sur. la Distribution Limite Du terme Dune Serie Aleatoire. Ann Math,1943,44:423-453
[6]GUMBEL E J. Statistics of Extreme Limite, New Yok:Columbia University Press,1958
[7]Resnick S I. Extreme values, regular variation, and point processes. New York:Springer,1987
[8]Bourgund U, Buche C G. Importance sampling procedure using design point(ISPUD). Research Report of University of Innsburch,1986,1-158
[9]Liu P-L, Kiureghian D A. Multivariate distribution models with prescribed marginals and covariances. Probabilitic Engineering Mechanics,1986,1(2):105-112
[10]刘德辅,杨永春,王超.海洋设计标准的不确定性及联合分布概率分析,海洋学报,1996,18(5)：110-116
[11]Zzchary S. Feld G. Ward G. Wolfram J. Multivariate extrapolation in the offshore environment. Applied Ocean Research,1998,20:237-295
[12]Pickands. Multivariate extreme value distribution. In Proc.43rd Session I.S.I, (Buenos Aires),1981,859-878
[13]Coles S G, Tawn J A. Modelling extreme multivariate events. J. R. Statist. Soc. B,1991,53(2):377-392
[14]Gumbel E J. Distributions de values extremes en plusieurs dimensions. Publ. Inst. Statist. Paris,1960,9: 171-173
[15]Tawn J A. Modeling multivariate extreme value distribution. Biometrika,1990,75:397-415
[16]Joe H. Families of min-stable multivariate exponential and multivariate extreme value distributions. Statist. Probab. Lett,1989,9:75-82
[17]Smith R L. Tawn J A. Yuen H K. Statistics of multivariate extremes. Int. Statist. Inst. Rev,1990,58(1):47-58
[18]McFadden D. Modelling the choice of residential location. In Spatial Interaction Theory and Planning Models, Amsterdam:North-Holland.1978,75-96
[19]Coles S G, Tawn J A. Statistical methods for extreme values A course presented at the 1998 RSS conference. Strathdyde,1998.9
[20]Shi Dao-ji. Multivariate extreme value distribution and Fisher information matrix. Acta mathematical application Sinica,1995a,11(4):421-428
[23]Shi Dao-ji, Lai C D. Fisher information for Downton's bivariate exponential distribution. J. Statist. comput. Simul.,1998,60:123-127
[24]Tawn J A. Bivariate extreme value theory models and estimation. Biometric,1988,75(3):397-415
[25]史道济,阮明恕,多元极值分布随机向量的抽样方法.应用概率统计,1997,13(1)：75-80
[26]Natural Environment Research Council. Flood Studies Report.1975, 1:London:NERC
[27]陈上及,马继瑞.海洋数据处理分析方法及其应用.北京：海洋出版社,1991.349-480
[28]刘德辅,王超.关于海洋工程设计波高标准研究.海洋学报,1984,6(3)：399-407
[29]刘德辅,王超.基于马尔可夫链的极值波高预测.中国造船,1996,(2)：7-15
[30]文圣常,宇宙文.海浪理论与计算原理.北京：科学出版社,1984
[31]徐德伦,于定勇.随机海浪理论.北京：高等教育出版社,2001
[32]严恺,梁其荀.海港工程.北京：海洋出版社,1996
[33]严恺,梁其荀.海岸工程.北京：海洋出版社,2002
[34]俞聿修.随机波浪及其工程应用.大连：大连理工大学出版社,2000
[35]中华人民共和国交通部.海港水文规范(JTJ213-98).北京：人民交通出版社,2000
[36]Weibull W. A statistical distribution of wide applicability. Journal of Applied Mechanics,1951,18:293-297
[37][苏]EΓ勃洛希诺夫.河川径流量的概率分布(张永胜,陈宝,蒋蓉,译者).武汉：长江出版社,2005
[38]董胜.工程设计Weibull分布参数拟合的改进方法.青岛海洋大学学报,1999,29(1)：135-141
[39]Weibull W. A statistical distribution of wide applicability. Journal of Applied Mechanics,1951,18:293-297
[40]董胜,刘德辅,孔令双.极值分布参数的非线性估计及其工程应用.海洋工程,2000,18(1)：50-55
[41]金光炎.广义极值分布及其在水文中的应用.水文,1998,(2)：9-15
[42]Castillo E. Extreme Value Theory in Engineering. San Diego:Academic Press,1988
[43]Kotz S. Nadara J'S. Extreme vale distributions-theory and applications.2000
[44]Coles S. An introduction to statistical modeling of extreme values.2001
[45]董胜,焦桂英,郑天立.水文极值Ⅰ型分布参数拟合方法的探讨.中国港湾建设,1999(2)：6-9
[46]董爱红.广义Pareto分布及其在水文频率分析中的应用：[博士学位论文].南京：河海大学水资源环境学院,2005
[47]Taynes E T. Prior probability. IEEE Trans. System Sci. Cybern.1968,4:227-241
[48]Zhang L Z and Xu D L. A new maximum entropy probability function for the surface elevation of nonlinear sea waves, China Ocean Engineering,2005,19(4):637-646
[49]中华人民共和国行业标准.海港水文规范(JTJ213-98).北京：人民交通出版社,2000
[50]史道济.实用极值统计方法.天津：天津科学技术出版社,2006
[51]Howard R, Charles A, Klimko L A. Maximum Likelihood Estimation with the Weibull Model. Journal of the American Statistical Association March.1974,169(345):246-249
[52]王蓉华,徐晓岭.对数正态分布参数的极大似然估计.上海师范大学学报,2000,29(1)：39-43
[53]魏宗叙译.统计学数学方法.上海：上海科技出版社,1966：184-197
[54]刘德辅,施建刚,周志刚.蒙特-卡洛方法在海洋工程设计中的应用.海洋学报,1990,12(2)：236-243
[55]宗德敦.概率权重矩法及其在P-Ⅲ型分布中的应用.水利学报,1988(3)：25-32
[56]董双林Gumbel分布函数形式的选取对参数似然估计的影响.科技通报,1987,3(4)：205-213
[57]Landwehr J M, et al. Probability Weighted Moments Compared with Some Traditional Technique in Estimating Gumbel Parameters and Quantiles. Water Resources Research,1979,15(5)
[58]曲延碌等.三参数Weibull分布的参数估计.气象学报,1987,45(3)：274-278
[59]石原健二.论气象极值的重现期.陆家机译.海洋译丛.北京：海洋出版社,1984
[60]刘桂海等.我国近海波浪要素的长期分布.港口工程,1989(6)：11-15
[61]葛明达.连云港波高周期统计分布.海洋工程,1984,(1)：48-49
[62]Ochi M K. On long-term statistics for ocean and coastal waves. Proc 16th Coastal Engrg Conf.1978,2:59-75
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