油价系统模拟及石油企业的最优策略
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摘要
近几年由于能源需求的爆炸式增长,导致原油价格一路攀升,石油企业盈利及资金收益都达到了历史最好水平,但同时也使石油生产的可持续性发展受到了严重挑战。一方面,国内一些石油生产企业(油田企业)为了片面地追求原油产量,甚至出现了“破坏式”的开采现象,这不仅是石油资源的浪费,更重要的是威胁到石油生产企业的生存与发展;另一方面,石油生产具有初期投资大、见效周期长、投资风险大等特点,一些石油生产企业对此认识不够,特别是受近两年原油价格不断创历史新高的影响,出现了一些石油生产项目一拥而上的现象。假如5-7年后原油价格回落至两年前的水平,这些石油生产企业及石油生产项目将面临怎样的困境呢?
我国石油生产企业作为特大型国有企业,尽管目前改制已经完成,油田公司也与国外油公司基本接轨,但受中国石油工业长期作为垄断产业的影响,油田公司在制定油田开发策略或油田开发规划过程中对油价因素考虑不够。本文的主要目的是通过研究中国石油生产企业在充分考虑油价因素下的最优开发策略,丰富和完善油田开发规划理论体系;研制油田开发规划“决策支持系统”,为中国石油企业油田开发的最优决策提供必要的理论及技术支撑。
从系统、信息、控制的角度研究油价系统的模拟与预测,并通过计算机实现油价的功能模拟与随机模拟,以此为基础研究石油企业应对油价变化的最优策略,建立石油生产企业在确定油价和随机油价下的各类优化决策模型。以计算机为工具求解这些优化模型获得石油生产企业应对油价变化的最优开发策略。
油田开发规划是油田企业开发策略的直接体现,建立并求解油田开发规划的各类优化模型是获得油田最优开发策略不可或缺的手段。油田开发规划包含动态分析,储量标定,产量配置,措施、成本、工作量投入优化等多方面的内容。
本文研究内容关系结构图:
目前国内各大油田(油田公司)在制定油田开发年度规划及五年规划过程中,对动态分析考虑较多,对优化模型考虑不够,对油价更是几乎没有考虑。本文重点研究油田公司在确定油价与随机油价下油田开发规划中的产量配置、措施、成本、工作量投入等各类优化问题。通过建立并求解油田开发规划年度、多年、单目标、多目标,单层与多层优化模型获得油田的最优开发规划方案或最优开发策略。
已有的研究基础表明油田的最优开发规划方案或最优开发策略是与油价密切相关的,所以为了获得这些最优方案与策略,本文首先研究油价的动态模拟及预测,然后将油价作为参数建立油田企业各类优化决策模型,再求解优化模型获得油田企业的最优开发策略。
在研究油价的模拟与预测过程中,本文打破传统的时间序列与因果分析方法预测模式。从系统输入、输出角度,将油价作为系统的输出,油价的所有影响因素作为系统的输入。基于系统、信息、控制的观点,从历史的输入输出信息实现功能的同构。首先研究了油价系统的微分模拟与神经网络模拟,基于两种方法的结合与改进,设计了基于时变系统的油价功能模拟系统,建立了系统的输入输出关联关系,这种输入输出关联关系不仅可以用于本文下面研究所需的油价预测,而且为将来研究油价的控制,即驱动油价奠定了必要的理论基础。在油价功能模拟的基础上,本文还利用蒙特卡洛方法,进一步研究设计了油价功能预测的随机模拟器,对油价进行了随机预测,使油田企业在随机油价下最优开发策略的获得得以实现。
全文共分为六章:
第一章,引言:首先阐述本文研究的必要性;然后明确了本文的研究目的及意义,追踪了研究过程及现状;再根据研究现状拟定了本文研究内容;所采用的方法、原理及技术路线;最后给出了本文的创新点。
第二章,油价及其影响因素:本章在分析国际、国内油价形成与发展的基础上,将油价的影响因素分成了三类:供给、需求、非供给和需求因素。油价的非供给与需求因素主要指国际政治、军事、经济综合形势、石油期货市场等。其中,供给与需求又受其它诸多因素的影响,如探明石油储量、技术进步因子、石油储备(库存)、GDP值、替代能源的价格等。反之油价的影响因素又受油价的影响,这就使它们的关系错综复杂。基于油价与其影响因素的这种复杂关系,用传统的分析方法研究油价及其影响因素受到很多局限,导致很难对油价将来的变化做出准确的预测,这正是本文引出第三章研究的原因所在。为油价预测研究做准备,本章还研究了油价非定量化影响因素的量化方法;
第三章,油价系统模拟及预测:本章基于系统输入输出理论将油价的所有影响因素,不管是供给、需求还是通过供给与需求影响油价的因素都作为油价系统的输入(多输入),将油价作为系统的输出,若只预测一种油价即为单输出,若同时预测多种油价即为多输出。基于系统、信息控制的角度研究油价及其影响因素。首先建立了油价系统的微分模拟和神经网络模拟模型,再基于两种方法的结合与改进,设计了基于时变系统的油价功能模拟系统,建立了系统的输入输出关联关系,根据系统模拟模型的预测功能得到油价的预测。由于油价的许多影响因素带有明显的随机特征,所以本章又将油价的影响因素作为随机过程,在任一时间点即为一随机变量,油价在该时间点也是一随机变量。再利用功能模拟的结论,从随机变量函数的角度研究油价,由油价影响因素的概率分布特征,用蒙特卡洛方法进行随机模拟,获得将来任一时间点油价的概率分布,进而对未来油价的分布特征(如均值、方差等)进行预测,预测得到的均值即可作为该时间点的油价预测值。本章还以确定性模拟及随机模拟为基础,详细设计了油价模拟系统,并用VB.NET为开发工具实现了油价系统的模拟与预测;最后给出了软件系统的运行实例,其中基于时变系统油价模拟预测得到的油价与历史拟合精度明显高于其它方法对油价的预测。
第四章,石油企业优化决策模型:油价系统模拟及油价预测不是本文的最终目的,本文的最终目的是通过模拟得到的油价预测值,再将油价作为可以改变的参数来获得油田企业未来的最优决策(策略),特别是最优开发规划。为此,本章首先分析了油田企业(油田公司)的生产经营活动,重点分析了效益产量和无效益产量、成本构成等,通过这些分析展示了石油这种特殊的产品在产量、成本、效益方面与一般产品的重要区别:对于油田企业而言,即使是市场有足够的需求,当油田的产量达到一定的技术极限时,要增加产量,单位成本就会成倍的增长,对于确定油价,这部分产量就将成为无效益产量。正是基于这一原因,本章建立了确定油价与随机油价下油田企业的各类优化决策模型:从层次上分,有“单层优化模型与多层优化模型”;从目标上分,有“单目标优化模型与多目标优化模型”;从时间上分,有“年度优化模型与多年优化模型(主要是五年,因为油田开发规划与国家发展规划对应,通常是五年规划)”;从决策准则上分,又有“产量最大优化模型”、“成本最低优化模型”、“效益最好优化模型”。
第五章,石油企业优化决策模型算法研究:本章首先阐述了求解第四章油田企业几类基本优化模型的算法及原理,含SUMT方法和多目标优化的评价函数法;然后设计了适合其中几类特殊优化模型的遗传算法、改进遗传算法及Powell遗传退火算法;再用VB.NET为开发工具,用软件实现了这些算法;最后,以国内H-B油田的实际数据,用软件求解获得了在不同油价下(油价作为参数可以改变)几类主要的油田企业优化模型对应的最优策略:各分项产量、各分项产量对应的成本、各分项产量对应的工作量等的最优构成(这也是油田企业中长期开发规划的最主要内容)。
第六章,结论及有待进一步研究的工作:本章首先阐述了本文的主要结论,并对进一步的研究方向给出了建议。
本文的创新点:
(1)通过研究中国石油企业在充分考虑油价因素下的最优开发策略,丰富和完善了油田开发规划理论体系;研制油田开发规划“决策支持系统”,为中国石油生产企业油田开发的最优决策提供必要的理论及技术支撑。
(2)油价系统功能模拟与预测(带控制的预测)。基于系统输入与输出的思想,在对油价影响因素定量化描述的基础上,将变步长学习训练的BP网络引入到微分模拟的参数识别中,设计了基于时变系统的油价功能模拟系统。该模拟系统既保留了神经网络对历史的高精度拟合特征,又使其在预测过程中具有微分模拟思想充分考虑输出变量自身变化趋势的特点。建立的系统模拟输入输出关联关系不仅能实现对油价的预测,而且为研究油价的控制(驱动油价)奠定了基础,也为经济学中同时对多个指标构成的体系进行预测的类似问题研究开辟了一条新的途径。基于模块化设计思想,利用嵌入式原理,在VB.NET环境下设计并开发了“油价预测软件系统”。进一步分析油价影响因素的随机特征,随机模拟了油价的分布及特征,在此基础上设计了油价随机模拟器,对油价进行随机功能模拟预测。
(3)确定性油价与随机油价下油田企业各类优化模型的建立。基于中国石油企业所特有的上、下层结构特征,作者首次提出了“产量构成”、“产量分配”、“措施产量结构”等油田开发规划中的新概念;第一次将二层规划应用于油田开发规划。建立了油田开发规划的各类优化决策模型,含年度优化及多年优化、单目标及多目标优化、单层规划与二层规划模型。这些优化模型成功地描述了中国石油企业的油田开发规划决策问题,特别是中长远战略规划决策问题,丰富和完善了油田开发规划理论体系。
(4)具有特殊结构的油田企业优化决策模型算法研究。首先在遗传算法中定义Powell算子,得到一种求无约束优化问题全局最优解的混合遗传算法,再通过自适应的退火因子和罚函数来处理约束条件,使算法逐渐收敛于全局可行最优解。这种基于Powell遗传退火精确罚函数法可以有效的克服Powell方法只能搜索到局部最优,而由人为给出多个初始点进行多次计算来求解最优解时,成功概率不高的缺陷,又能显著提高遗传算法收敛到最优解的概率。这一思想在具有多目标、多层结构、非解析约束的非线性优化模型算法设计中的应用,将对非线性优化理论中的全局寻优带来有益启迪;基于二层规划模型的遗传算法与退火算法的改进与计算机实现,将对进化算法的理论分析及应用产生重要的影响。
With the explosive growth of energy demand and the constant increase of oil price, the profitability and financial benefit of oil enterprises has reached the highest level in history in the recent years. However, the sustainable development of oil production has been neglected. On one hand, the phenomenon of "undermining" exploitation appears because some domestic oil production enterprises (oilfield companies) only pursue the output of crude oil. It is not only a waste of oil resources, but also brings negative effects to the sustainable production of oil, even threatens the survival and development of oil production enterprises. On the other hand, some oil production enterprises can’t deeply realize the characteristics of oil production such as big initial investment, long period of effective time, high investment risks and so on;a great number of oil-production projects have been blindly proposed at the time that the influence of high oil price which has rocketed to the top in the last two years. There is a serious problem what difficulties the oil companies and their oil-production projects will be faced with if the oil price falls to the lever of two years before in the future 5-7 years.
Though the reconstruction of it has been completed and it is basically in harmony with foreign companies, China's oil enterprises, as large state-owned enterprises, can’t consider all the influencing factors of oil price in an all round way for the long-term impact of monopolistic policies of China's petroleum industry when they carry out the development strategies or plans of oilfield. The main purpose of this paper is to built and perfect the theory of oilfield development programming by researching the optimal decision of china’s oil production enterprises to develop the“Decision Support System”of oilfield development programming, to provide the optimal decision of china’s oil production enterprises with necessary theory and technology support, to study the simulation and forecasting of oil price from the perspectives of system theory, information theory and control theory, to investigate the optimal decision of oil enterprises with the variable oil price by the realization of the functional simulation and stochastic simulation of oil price system by computer, to establish various kinds of optimization models of oil production enterprises with deterministic oil price and stochastic oil price, and to obtain the optimal development decision of oil production enterprises with variable oil price through the solution of these models by means of computer.
The Oilfield development programming can embody the strategies of oil enterprises (oilfield companies), while to build and to solve various optimization models of it is an indispensable mean to obtain the optimal oilfield development strategies. The oilfield development programming includes dynamic analysis, recovery calibration, production allocation, measures, cost, workload input and so on.
The relational structure chart of the research contents of this paper:
At present, the leading oilfield (oilfield company) considers much in dynamic analysis, `a little in the optimization models, but little in oil price when it establishes the annual or five-year programming of oilfield development. This paper mainly focuses on the research of various optimization models of Oilfield Company such as production allocation, measures, cost, workload input considering the deterministic or stochastic oil price. Obtain the optimal development programming or strategy of oilfield by establishing and solving various optimization models including a year or many years, single target or multi-target, single level or multi-level optimization models.
According to the existing researches, oilfield optimal development programming or strategy is closely linked with oil price. Therefore, in order to get the optimal programming or strategy, at first it is necessary to investigate the dynamic simulation and forecasting of oil price, then to establish various optimization models of oilfield development according to the forecasting results, at last to solve these optimization models.
This paper lets oil price be the system output and all influencing factors of oil price be the system input in the input-output perspective. It achieves the isomorphic function based on historical input- output information, and establishes an input-output associated relationship of the system by the methods of differential simulation and neural network. This input-output associated relationship can not only be used for the oil price forecasting, but also can provide a necessary theoretical foundation for the studying of controlling and driving oil price. In addition, this paper designs a stochastic simulator by Monte Carlo method which can stochastic forecast the oil price, finally obtains the risk decision, oilfield development programming with stochastic oil price and the optimal strategy of oil enterprises.
There are six chapters in this paper.
In Chapter 1, there is a general introduction of this paper. At first the importance, necessity, purpose and meaning of this paper is set forth. Then the processes and current situation of this research are reviewed, and the methods, principles and techno-way used in this paper are drawn according to the current research situation. Finally, the innovation points of this paper are enumerated.
In Chapter 2, the oil price and its influence factors are analyzed. The influence factors of oil price are divided into there kinds, i.e., supply, demand and non-supply-demand factors based on the thorough analysis of the formation and development of oil price at home and abroad. Supply-demand factors mainly refers to the international politics, military, economic integrated situation, oil futures market and so on. There, supply and demand will be affected by many factors such as proven oil reserves, technological progress factor, oil reserve(stocks), GDP, the price of alternative energy sources and so on, while oil price will affect its influencing factors too, thus the relationship between oil price and its influencing factors is intricate and complex. Basing on the complex relationship between oil price and its influencing factors that using traditional analysis methods to study oil price is limited in many aspects, and it is difficult to accurately forecast the change of oil price in the future. That is just the reason why the researches in Chapters 3 are developed. For the preparations of oil price prediction, the chapter also studies the quantitative methods of oil price influencing factors;
In Chapter 3, the functional simulation and forecasting of oil price system is investigated. Based on the input- output theory ,it lets all influencing factors (including all supply, demand and oil price influencing factors by supply and demand ) of oil price be the inputs (i.e., multiple-input) of oil price system and oil price be the output (i.e., single output) of oil price system. This chapter studies on oil price and its influencing factors from the perspectives of system theory, information theory and control theory. At first, a differential simulation model and a neural network model of oil price system are established. Based on the combination and improvement of the two methods it designs functional simulation system of the oil price system with time-varying systems, establishes an input-output associated relationship of the system, obtains oil price forecasting depending on simulation model of the system. Because of many influencing factors of oil price with great stochastic characteristics, the paper makes these as the stochastic processes. They are random variables at any point of time while the time unit is a year in this paper, and oil price (i.e., the function of random variables) is a random variable too. This chapter researches oil price from perspective of the function of random variables, obtains the probability distribution of oil price at any point of time in future and its software development by the stochastic simulation with Monte Carlo method according to the probability distribution characteristics of the influencing factors of oil price, and gains the forecasting oil price at that point of time by forecasting distributional characteristics(such as typical value, variance)of oil price in future. Based on deterministic or stochastic simulation, a detail design of software for oil price simulation system is given, and the simulation and forecasting of oil price system is realized by the VB.NET development tool. Finally, some operation examples of software system are given which indicate the forecasting oil price obtained by our method is much more accurate than that obtained by other methods on precision of history matching.
In Chapter 4, various optimization decision models of oil enterprises are established. The ultimate purpose of this paper is not to simulate the oil price system or to forecast oil price, but to obtain the optimal programming (strategy) especially the optimal production of oil enterprises with forecasting oil price got by simulation. Therefore, this chapter investigates the production and operating activities of oil enterprises at first. Then it selective analyzes the output and its influencing factors, benefit output and non-benefit output, component of cost and so on. Finally, according to these analyses above, the important distinction between oil and other commonly productions in output, cost, benefit is shown. For commonly productions, the benefit increases with the increment of production as long as there has adequate market demand where the unit will not increase for the batch production. However, for oil enterprises, the unit cost will increase several times with the increment of production when the production of oilfield reaches a certain limit level even if there has adequate market demand, and this increased production will turn into non-benefit production. Therefore, various optimal decision models of oil enterprises under the deterministic oil price are established.
Considering structure, they can be divided into single-layer optimization models and multiplayer optimization models from structure. Considering objective, they can be divided into“single-objective optimization models and multi-objective optimization models”from objective. Considering time, they can be divided into“yearly optimization models and multi-year optimization models”. Considering decision rule, they can be divided into“maximal-output optimization models”,“lowest-cost optimization models”and“best-benefit optimization models”.
In Chapter 5, it studies algorithms of various optimization decision models of oil enterprises .At first; some algorithms and their principles for the optimization models of oil enterprises which are proposed in Chapter 4 are set forth, including SUMT method and the evaluation function method of multi-objective optimization. Then several kinds of genetic algorithms modified genetic algorithms and POWLL genetic annealing algorithms which can be applied to these especial optimization models are designed, and their software developments are realized by the VB.NET development tool. Finally, based on the actual data of domestic H-B oil field, the corresponding optimal decisions of several primary optimization models of oil enterprises with the deterministic oil price (the oil price can be altered as parameter) are obtained by our software, including the optimal construction of subentry production , the optimal construction of the corresponding cost of subentry production and the optimal construction of corresponding workload of subentry production, (which are also the most important part of the medium and long periods of development of oil field).
In Chapter 6, the conclusion and further research: At first, the main conclusion is set forth and some suggestion of further direction of this research is given.
The innovation of this paper is as follows:
1. Investigate the optimal development decision of china’s oil production enterprises considering sufficiently the influencing factors of oil price, built and perfect the theory of oilfield development programming, develop the“Decision Support System”of oilfield development programming, and provide the optimal decision of china’s oil production enterprises with necessary theory and technology support.
2. The functional simulation and forecasting (with control) of oil price system. Based on the input-output thought of system and the quantitative description of the influencing factors of oil prices and variable step trained of BP network be introduced to differential simulation of parameter identification, it designs prices functional simulation system based on time-varying system. The simulation system not only has retained the characteristics of neural network’s high precision fitting to the history, but also has the idea of differential simulation in the process of forecasting, fully thinking trending characteristics of output variables. This input-output associated relationship can not only realize the forecasting of oil price, but also can lay foundation for the control of oil price (or the driving of oil price). Even more, it opened up a new way for the research of the similar problems in ecnomics. Software called“oil price forecasting software system”is designed and developed in the VB.NET environment based on modularization design idea and embedded principles. A stochastic simulator of oil price is designed to simulate and forecast oil price randomly based on the further analysis of the stochastic characteristics of the influencing factors of oil price and the simulation of distribution and characteristics of oil price.
3. The establishment of various optimization models of oil production enterprises with deterministic and stochastic oil prices. Taking account into the unique upper and lower structural feature of China's oil production enterprises, the author firstly puts forward some new concepts of oil development programming such as“output constitution”,“output allocation”and“the structure of measure output”and so on, firstly applies bi-level programming for oil development programming, and builds s various optimization models of oil development programming including yearly and multi-year, single target and multi-target, single level and bi-level optimization models, which will successfully describe the problem of the planning and decision-making of China's oil production enterprises, especially the problem of the long-term strategic planning.
4. A study of algorithms with special structure for the optimization models of oil development programming. At first, Powell Operator is defined in Genetic Algorithm and a hybrid genetic algorithm for solving the global optimal solutions of unconstrained optimization problems is obtained. Then the constrained optimization problems are processed by adaptive annealing penalty factors and penalty function, and it is proved the present algorithm is a promising approach for solving global optimization problems. The Powell algorithm can only get a local optimal solution, and it has low success probability in obtaining optimal solution by the repeated calculations of many given initial points. However, the new Powell genetic algorithm based on adaptive annealing penalty function can not only overcome above defects of Powell algorithm, but also significantly improve the probability of the genetic algorithm convergence to optimal solution. It will throw some light on the global optimization of nonlinear optimization theory by the application of the thought proposed above in the algorithms design of nonlinear optimization models which are multi-objective, multi-level and non-differentiable constraint optimizations. Further, it will have profound impact on the theoretical analysis and application of evolutionary algorithms, based on the improvement of genetic algorithm and annealing algorithm for bi-level models and the computer realization of those algorithms.
我国石油生产企业作为特大型国有企业,尽管目前改制已经完成,油田公司也与国外油公司基本接轨,但受中国石油工业长期作为垄断产业的影响,油田公司在制定油田开发策略或油田开发规划过程中对油价因素考虑不够。本文的主要目的是通过研究中国石油生产企业在充分考虑油价因素下的最优开发策略,丰富和完善油田开发规划理论体系;研制油田开发规划“决策支持系统”,为中国石油企业油田开发的最优决策提供必要的理论及技术支撑。
从系统、信息、控制的角度研究油价系统的模拟与预测,并通过计算机实现油价的功能模拟与随机模拟,以此为基础研究石油企业应对油价变化的最优策略,建立石油生产企业在确定油价和随机油价下的各类优化决策模型。以计算机为工具求解这些优化模型获得石油生产企业应对油价变化的最优开发策略。
油田开发规划是油田企业开发策略的直接体现,建立并求解油田开发规划的各类优化模型是获得油田最优开发策略不可或缺的手段。油田开发规划包含动态分析,储量标定,产量配置,措施、成本、工作量投入优化等多方面的内容。
本文研究内容关系结构图:
目前国内各大油田(油田公司)在制定油田开发年度规划及五年规划过程中,对动态分析考虑较多,对优化模型考虑不够,对油价更是几乎没有考虑。本文重点研究油田公司在确定油价与随机油价下油田开发规划中的产量配置、措施、成本、工作量投入等各类优化问题。通过建立并求解油田开发规划年度、多年、单目标、多目标,单层与多层优化模型获得油田的最优开发规划方案或最优开发策略。
已有的研究基础表明油田的最优开发规划方案或最优开发策略是与油价密切相关的,所以为了获得这些最优方案与策略,本文首先研究油价的动态模拟及预测,然后将油价作为参数建立油田企业各类优化决策模型,再求解优化模型获得油田企业的最优开发策略。
在研究油价的模拟与预测过程中,本文打破传统的时间序列与因果分析方法预测模式。从系统输入、输出角度,将油价作为系统的输出,油价的所有影响因素作为系统的输入。基于系统、信息、控制的观点,从历史的输入输出信息实现功能的同构。首先研究了油价系统的微分模拟与神经网络模拟,基于两种方法的结合与改进,设计了基于时变系统的油价功能模拟系统,建立了系统的输入输出关联关系,这种输入输出关联关系不仅可以用于本文下面研究所需的油价预测,而且为将来研究油价的控制,即驱动油价奠定了必要的理论基础。在油价功能模拟的基础上,本文还利用蒙特卡洛方法,进一步研究设计了油价功能预测的随机模拟器,对油价进行了随机预测,使油田企业在随机油价下最优开发策略的获得得以实现。
全文共分为六章:
第一章,引言:首先阐述本文研究的必要性;然后明确了本文的研究目的及意义,追踪了研究过程及现状;再根据研究现状拟定了本文研究内容;所采用的方法、原理及技术路线;最后给出了本文的创新点。
第二章,油价及其影响因素:本章在分析国际、国内油价形成与发展的基础上,将油价的影响因素分成了三类:供给、需求、非供给和需求因素。油价的非供给与需求因素主要指国际政治、军事、经济综合形势、石油期货市场等。其中,供给与需求又受其它诸多因素的影响,如探明石油储量、技术进步因子、石油储备(库存)、GDP值、替代能源的价格等。反之油价的影响因素又受油价的影响,这就使它们的关系错综复杂。基于油价与其影响因素的这种复杂关系,用传统的分析方法研究油价及其影响因素受到很多局限,导致很难对油价将来的变化做出准确的预测,这正是本文引出第三章研究的原因所在。为油价预测研究做准备,本章还研究了油价非定量化影响因素的量化方法;
第三章,油价系统模拟及预测:本章基于系统输入输出理论将油价的所有影响因素,不管是供给、需求还是通过供给与需求影响油价的因素都作为油价系统的输入(多输入),将油价作为系统的输出,若只预测一种油价即为单输出,若同时预测多种油价即为多输出。基于系统、信息控制的角度研究油价及其影响因素。首先建立了油价系统的微分模拟和神经网络模拟模型,再基于两种方法的结合与改进,设计了基于时变系统的油价功能模拟系统,建立了系统的输入输出关联关系,根据系统模拟模型的预测功能得到油价的预测。由于油价的许多影响因素带有明显的随机特征,所以本章又将油价的影响因素作为随机过程,在任一时间点即为一随机变量,油价在该时间点也是一随机变量。再利用功能模拟的结论,从随机变量函数的角度研究油价,由油价影响因素的概率分布特征,用蒙特卡洛方法进行随机模拟,获得将来任一时间点油价的概率分布,进而对未来油价的分布特征(如均值、方差等)进行预测,预测得到的均值即可作为该时间点的油价预测值。本章还以确定性模拟及随机模拟为基础,详细设计了油价模拟系统,并用VB.NET为开发工具实现了油价系统的模拟与预测;最后给出了软件系统的运行实例,其中基于时变系统油价模拟预测得到的油价与历史拟合精度明显高于其它方法对油价的预测。
第四章,石油企业优化决策模型:油价系统模拟及油价预测不是本文的最终目的,本文的最终目的是通过模拟得到的油价预测值,再将油价作为可以改变的参数来获得油田企业未来的最优决策(策略),特别是最优开发规划。为此,本章首先分析了油田企业(油田公司)的生产经营活动,重点分析了效益产量和无效益产量、成本构成等,通过这些分析展示了石油这种特殊的产品在产量、成本、效益方面与一般产品的重要区别:对于油田企业而言,即使是市场有足够的需求,当油田的产量达到一定的技术极限时,要增加产量,单位成本就会成倍的增长,对于确定油价,这部分产量就将成为无效益产量。正是基于这一原因,本章建立了确定油价与随机油价下油田企业的各类优化决策模型:从层次上分,有“单层优化模型与多层优化模型”;从目标上分,有“单目标优化模型与多目标优化模型”;从时间上分,有“年度优化模型与多年优化模型(主要是五年,因为油田开发规划与国家发展规划对应,通常是五年规划)”;从决策准则上分,又有“产量最大优化模型”、“成本最低优化模型”、“效益最好优化模型”。
第五章,石油企业优化决策模型算法研究:本章首先阐述了求解第四章油田企业几类基本优化模型的算法及原理,含SUMT方法和多目标优化的评价函数法;然后设计了适合其中几类特殊优化模型的遗传算法、改进遗传算法及Powell遗传退火算法;再用VB.NET为开发工具,用软件实现了这些算法;最后,以国内H-B油田的实际数据,用软件求解获得了在不同油价下(油价作为参数可以改变)几类主要的油田企业优化模型对应的最优策略:各分项产量、各分项产量对应的成本、各分项产量对应的工作量等的最优构成(这也是油田企业中长期开发规划的最主要内容)。
第六章,结论及有待进一步研究的工作:本章首先阐述了本文的主要结论,并对进一步的研究方向给出了建议。
本文的创新点:
(1)通过研究中国石油企业在充分考虑油价因素下的最优开发策略,丰富和完善了油田开发规划理论体系;研制油田开发规划“决策支持系统”,为中国石油生产企业油田开发的最优决策提供必要的理论及技术支撑。
(2)油价系统功能模拟与预测(带控制的预测)。基于系统输入与输出的思想,在对油价影响因素定量化描述的基础上,将变步长学习训练的BP网络引入到微分模拟的参数识别中,设计了基于时变系统的油价功能模拟系统。该模拟系统既保留了神经网络对历史的高精度拟合特征,又使其在预测过程中具有微分模拟思想充分考虑输出变量自身变化趋势的特点。建立的系统模拟输入输出关联关系不仅能实现对油价的预测,而且为研究油价的控制(驱动油价)奠定了基础,也为经济学中同时对多个指标构成的体系进行预测的类似问题研究开辟了一条新的途径。基于模块化设计思想,利用嵌入式原理,在VB.NET环境下设计并开发了“油价预测软件系统”。进一步分析油价影响因素的随机特征,随机模拟了油价的分布及特征,在此基础上设计了油价随机模拟器,对油价进行随机功能模拟预测。
(3)确定性油价与随机油价下油田企业各类优化模型的建立。基于中国石油企业所特有的上、下层结构特征,作者首次提出了“产量构成”、“产量分配”、“措施产量结构”等油田开发规划中的新概念;第一次将二层规划应用于油田开发规划。建立了油田开发规划的各类优化决策模型,含年度优化及多年优化、单目标及多目标优化、单层规划与二层规划模型。这些优化模型成功地描述了中国石油企业的油田开发规划决策问题,特别是中长远战略规划决策问题,丰富和完善了油田开发规划理论体系。
(4)具有特殊结构的油田企业优化决策模型算法研究。首先在遗传算法中定义Powell算子,得到一种求无约束优化问题全局最优解的混合遗传算法,再通过自适应的退火因子和罚函数来处理约束条件,使算法逐渐收敛于全局可行最优解。这种基于Powell遗传退火精确罚函数法可以有效的克服Powell方法只能搜索到局部最优,而由人为给出多个初始点进行多次计算来求解最优解时,成功概率不高的缺陷,又能显著提高遗传算法收敛到最优解的概率。这一思想在具有多目标、多层结构、非解析约束的非线性优化模型算法设计中的应用,将对非线性优化理论中的全局寻优带来有益启迪;基于二层规划模型的遗传算法与退火算法的改进与计算机实现,将对进化算法的理论分析及应用产生重要的影响。
With the explosive growth of energy demand and the constant increase of oil price, the profitability and financial benefit of oil enterprises has reached the highest level in history in the recent years. However, the sustainable development of oil production has been neglected. On one hand, the phenomenon of "undermining" exploitation appears because some domestic oil production enterprises (oilfield companies) only pursue the output of crude oil. It is not only a waste of oil resources, but also brings negative effects to the sustainable production of oil, even threatens the survival and development of oil production enterprises. On the other hand, some oil production enterprises can’t deeply realize the characteristics of oil production such as big initial investment, long period of effective time, high investment risks and so on;a great number of oil-production projects have been blindly proposed at the time that the influence of high oil price which has rocketed to the top in the last two years. There is a serious problem what difficulties the oil companies and their oil-production projects will be faced with if the oil price falls to the lever of two years before in the future 5-7 years.
Though the reconstruction of it has been completed and it is basically in harmony with foreign companies, China's oil enterprises, as large state-owned enterprises, can’t consider all the influencing factors of oil price in an all round way for the long-term impact of monopolistic policies of China's petroleum industry when they carry out the development strategies or plans of oilfield. The main purpose of this paper is to built and perfect the theory of oilfield development programming by researching the optimal decision of china’s oil production enterprises to develop the“Decision Support System”of oilfield development programming, to provide the optimal decision of china’s oil production enterprises with necessary theory and technology support, to study the simulation and forecasting of oil price from the perspectives of system theory, information theory and control theory, to investigate the optimal decision of oil enterprises with the variable oil price by the realization of the functional simulation and stochastic simulation of oil price system by computer, to establish various kinds of optimization models of oil production enterprises with deterministic oil price and stochastic oil price, and to obtain the optimal development decision of oil production enterprises with variable oil price through the solution of these models by means of computer.
The Oilfield development programming can embody the strategies of oil enterprises (oilfield companies), while to build and to solve various optimization models of it is an indispensable mean to obtain the optimal oilfield development strategies. The oilfield development programming includes dynamic analysis, recovery calibration, production allocation, measures, cost, workload input and so on.
The relational structure chart of the research contents of this paper:
At present, the leading oilfield (oilfield company) considers much in dynamic analysis, `a little in the optimization models, but little in oil price when it establishes the annual or five-year programming of oilfield development. This paper mainly focuses on the research of various optimization models of Oilfield Company such as production allocation, measures, cost, workload input considering the deterministic or stochastic oil price. Obtain the optimal development programming or strategy of oilfield by establishing and solving various optimization models including a year or many years, single target or multi-target, single level or multi-level optimization models.
According to the existing researches, oilfield optimal development programming or strategy is closely linked with oil price. Therefore, in order to get the optimal programming or strategy, at first it is necessary to investigate the dynamic simulation and forecasting of oil price, then to establish various optimization models of oilfield development according to the forecasting results, at last to solve these optimization models.
This paper lets oil price be the system output and all influencing factors of oil price be the system input in the input-output perspective. It achieves the isomorphic function based on historical input- output information, and establishes an input-output associated relationship of the system by the methods of differential simulation and neural network. This input-output associated relationship can not only be used for the oil price forecasting, but also can provide a necessary theoretical foundation for the studying of controlling and driving oil price. In addition, this paper designs a stochastic simulator by Monte Carlo method which can stochastic forecast the oil price, finally obtains the risk decision, oilfield development programming with stochastic oil price and the optimal strategy of oil enterprises.
There are six chapters in this paper.
In Chapter 1, there is a general introduction of this paper. At first the importance, necessity, purpose and meaning of this paper is set forth. Then the processes and current situation of this research are reviewed, and the methods, principles and techno-way used in this paper are drawn according to the current research situation. Finally, the innovation points of this paper are enumerated.
In Chapter 2, the oil price and its influence factors are analyzed. The influence factors of oil price are divided into there kinds, i.e., supply, demand and non-supply-demand factors based on the thorough analysis of the formation and development of oil price at home and abroad. Supply-demand factors mainly refers to the international politics, military, economic integrated situation, oil futures market and so on. There, supply and demand will be affected by many factors such as proven oil reserves, technological progress factor, oil reserve(stocks), GDP, the price of alternative energy sources and so on, while oil price will affect its influencing factors too, thus the relationship between oil price and its influencing factors is intricate and complex. Basing on the complex relationship between oil price and its influencing factors that using traditional analysis methods to study oil price is limited in many aspects, and it is difficult to accurately forecast the change of oil price in the future. That is just the reason why the researches in Chapters 3 are developed. For the preparations of oil price prediction, the chapter also studies the quantitative methods of oil price influencing factors;
In Chapter 3, the functional simulation and forecasting of oil price system is investigated. Based on the input- output theory ,it lets all influencing factors (including all supply, demand and oil price influencing factors by supply and demand ) of oil price be the inputs (i.e., multiple-input) of oil price system and oil price be the output (i.e., single output) of oil price system. This chapter studies on oil price and its influencing factors from the perspectives of system theory, information theory and control theory. At first, a differential simulation model and a neural network model of oil price system are established. Based on the combination and improvement of the two methods it designs functional simulation system of the oil price system with time-varying systems, establishes an input-output associated relationship of the system, obtains oil price forecasting depending on simulation model of the system. Because of many influencing factors of oil price with great stochastic characteristics, the paper makes these as the stochastic processes. They are random variables at any point of time while the time unit is a year in this paper, and oil price (i.e., the function of random variables) is a random variable too. This chapter researches oil price from perspective of the function of random variables, obtains the probability distribution of oil price at any point of time in future and its software development by the stochastic simulation with Monte Carlo method according to the probability distribution characteristics of the influencing factors of oil price, and gains the forecasting oil price at that point of time by forecasting distributional characteristics(such as typical value, variance)of oil price in future. Based on deterministic or stochastic simulation, a detail design of software for oil price simulation system is given, and the simulation and forecasting of oil price system is realized by the VB.NET development tool. Finally, some operation examples of software system are given which indicate the forecasting oil price obtained by our method is much more accurate than that obtained by other methods on precision of history matching.
In Chapter 4, various optimization decision models of oil enterprises are established. The ultimate purpose of this paper is not to simulate the oil price system or to forecast oil price, but to obtain the optimal programming (strategy) especially the optimal production of oil enterprises with forecasting oil price got by simulation. Therefore, this chapter investigates the production and operating activities of oil enterprises at first. Then it selective analyzes the output and its influencing factors, benefit output and non-benefit output, component of cost and so on. Finally, according to these analyses above, the important distinction between oil and other commonly productions in output, cost, benefit is shown. For commonly productions, the benefit increases with the increment of production as long as there has adequate market demand where the unit will not increase for the batch production. However, for oil enterprises, the unit cost will increase several times with the increment of production when the production of oilfield reaches a certain limit level even if there has adequate market demand, and this increased production will turn into non-benefit production. Therefore, various optimal decision models of oil enterprises under the deterministic oil price are established.
Considering structure, they can be divided into single-layer optimization models and multiplayer optimization models from structure. Considering objective, they can be divided into“single-objective optimization models and multi-objective optimization models”from objective. Considering time, they can be divided into“yearly optimization models and multi-year optimization models”. Considering decision rule, they can be divided into“maximal-output optimization models”,“lowest-cost optimization models”and“best-benefit optimization models”.
In Chapter 5, it studies algorithms of various optimization decision models of oil enterprises .At first; some algorithms and their principles for the optimization models of oil enterprises which are proposed in Chapter 4 are set forth, including SUMT method and the evaluation function method of multi-objective optimization. Then several kinds of genetic algorithms modified genetic algorithms and POWLL genetic annealing algorithms which can be applied to these especial optimization models are designed, and their software developments are realized by the VB.NET development tool. Finally, based on the actual data of domestic H-B oil field, the corresponding optimal decisions of several primary optimization models of oil enterprises with the deterministic oil price (the oil price can be altered as parameter) are obtained by our software, including the optimal construction of subentry production , the optimal construction of the corresponding cost of subentry production and the optimal construction of corresponding workload of subentry production, (which are also the most important part of the medium and long periods of development of oil field).
In Chapter 6, the conclusion and further research: At first, the main conclusion is set forth and some suggestion of further direction of this research is given.
The innovation of this paper is as follows:
1. Investigate the optimal development decision of china’s oil production enterprises considering sufficiently the influencing factors of oil price, built and perfect the theory of oilfield development programming, develop the“Decision Support System”of oilfield development programming, and provide the optimal decision of china’s oil production enterprises with necessary theory and technology support.
2. The functional simulation and forecasting (with control) of oil price system. Based on the input-output thought of system and the quantitative description of the influencing factors of oil prices and variable step trained of BP network be introduced to differential simulation of parameter identification, it designs prices functional simulation system based on time-varying system. The simulation system not only has retained the characteristics of neural network’s high precision fitting to the history, but also has the idea of differential simulation in the process of forecasting, fully thinking trending characteristics of output variables. This input-output associated relationship can not only realize the forecasting of oil price, but also can lay foundation for the control of oil price (or the driving of oil price). Even more, it opened up a new way for the research of the similar problems in ecnomics. Software called“oil price forecasting software system”is designed and developed in the VB.NET environment based on modularization design idea and embedded principles. A stochastic simulator of oil price is designed to simulate and forecast oil price randomly based on the further analysis of the stochastic characteristics of the influencing factors of oil price and the simulation of distribution and characteristics of oil price.
3. The establishment of various optimization models of oil production enterprises with deterministic and stochastic oil prices. Taking account into the unique upper and lower structural feature of China's oil production enterprises, the author firstly puts forward some new concepts of oil development programming such as“output constitution”,“output allocation”and“the structure of measure output”and so on, firstly applies bi-level programming for oil development programming, and builds s various optimization models of oil development programming including yearly and multi-year, single target and multi-target, single level and bi-level optimization models, which will successfully describe the problem of the planning and decision-making of China's oil production enterprises, especially the problem of the long-term strategic planning.
4. A study of algorithms with special structure for the optimization models of oil development programming. At first, Powell Operator is defined in Genetic Algorithm and a hybrid genetic algorithm for solving the global optimal solutions of unconstrained optimization problems is obtained. Then the constrained optimization problems are processed by adaptive annealing penalty factors and penalty function, and it is proved the present algorithm is a promising approach for solving global optimization problems. The Powell algorithm can only get a local optimal solution, and it has low success probability in obtaining optimal solution by the repeated calculations of many given initial points. However, the new Powell genetic algorithm based on adaptive annealing penalty function can not only overcome above defects of Powell algorithm, but also significantly improve the probability of the genetic algorithm convergence to optimal solution. It will throw some light on the global optimization of nonlinear optimization theory by the application of the thought proposed above in the algorithms design of nonlinear optimization models which are multi-objective, multi-level and non-differentiable constraint optimizations. Further, it will have profound impact on the theoretical analysis and application of evolutionary algorithms, based on the improvement of genetic algorithm and annealing algorithm for bi-level models and the computer realization of those algorithms.
引文
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