油田生产系统整体优化理论与方法
|
摘要
在油田进入高含水后期开采阶段以后,油田面临着液油比急剧增长,产液量大幅度上升,地面工程难以适应,维持油田稳产的措施工作量和费用明显增大等诸多困难。为了实现油田的可持续发展,必须对各项增产措施进行优化部署,更加合理地安排油田开发工作。
本论文在分析现有油田开发规划优化模型的基础上,根据油气储运、油藏工程和采油工程的基本理论和方法,结合油田开发的系统特征和已有方法在处理多目标多阶段多子系统问题上的局限性,利用系统工程方法、最优化理论和节点分析方法,建立水驱和聚驱条件下,综合考虑经济效益、成本、产量、可采储量等多目标决策模型,通过求解这种大系统优化模型,将地面服从地下、地下兼顾地面的思想模型化、定量化,从而得到油田生产的整体优化方案,按照优化方案,对目前存在“大马拉小车”等现象的地面系统、处于特高含水阶段且水驱剩余油分布不均匀的油藏系统和与之相适应的油、水井系统进行改造,对新投产的井网进行优化设计,从而大幅度降低油田开发成本,提高经济效益和油田开发水平。
由于油田生产系统的复杂性,若直接建立起优化模型,所得到的将是一个包括大量数学方程、数据、曲线、管网和油层约束条件的庞大的数学模型,在现有的计算条件下求解这样的最优化模型会耗费大量的机时,很难在生产中推广应用。本论文根据大系统的分解——协调原理,把油田生产系统分为地面注入系统、注入井井身系统、油藏系统、油井举升系统和地面油气水处理与集输系统等5 个子系统。在每一个子系统中,流体的流动受多种因素的制约,多因素综合影响的结果,使得系统处于一种动态的协调状态。5 个子系统中相邻两个系统存在一个输入和输出的接口,通过这一接口相互影响和制约。
引入节点分析方法,把油田生产系统的5 个子系统用4 个节点相连接,其中每一个结点把相邻的两个系统分为上游系统和下游系统,节点既是上游系统的出口,也是下游系统的入口,在节点处,两个系统进行流体传递,对应的动态指标是流量和压力。在节点分析的基础上,利用系统工程方法和最优化理论,对每一个子系统确定目标函数和约束条件,建立最优化模型。
通过对油田地面注水系统的分析,确定了地面注水系统的流程及其中各个部分的特点,在此基础上,利用相应的参数、公式、图形,建立了综合考虑注入水处理站、配水间、管线等中流体压力、流速、温度等变化规律的地面注水系统优化模型;根据地面油、气、水集输处理系统流程,构建了包括管、罐、站的流体压力、温度、流速变化的数学模型;分析了井身压力、流速、温度梯度、流动阻力的变化,根据注入井、采油井举升结构及各部分的特点,建立了注入井和采油井举升优化模型;在两维两相流数值模拟分析的基础上,建立了油藏系统的优化模型,并结合动态劈分方法构建了满足油藏生产系统优化需要的沉积单元剩余油分布和分层动态指标预测模型,编制了相应的软件;利用模糊数学方法建立了考虑注水系统和采油井举升系统动态特征的分层措施方案优选模型。
考虑控制变量的实际取值,求得了各个子系统模型的解。在此基础上,考虑到子系统间的连接关系,
Oil fields are confronted with a lot of difficult after they entered into the high water-cut stage, such as the sharp aggrandizement of liquid-oil ratio, the great rising of liquid production, the unaccommodated ground projects, the obviously augmentation of treatment workload and cost that in order to maintain the steady production of oil field, and so on. For the sustainable development of oil field, we must optimize every stimulation treatment, and arrange the development work more reasonablely.
Based on the theory and method of oilgas preservation and transportation, petroleum reservoir engineering, and petroleum engineering, the paper analyzes the current programming optimization models of oil field development, combines the system character of oilfield development and the localizations when using current methods to deal with the multiobjective multi-stage and multi-subsystem problem, utilizes systems engineering method, optimization theory and node analysis to establish the multiobjective decision-making model under water drive and polymer displacement. The economic benefit, cost, production and final recovery ratio are considered comprehensively. By solving this largescale optimization model, we can make the idea, underground submit to ground and underground consider the ground, modeling and quantification. So, the global optimization project can be achieved. According to the optimization project, by reconstructing reservoir system whose water cut is very high and remaining oil distribution is asymmetry and the corresponding oil well system and injection well system, and by optimization design the new put into production well net to reduce the cost greatly and enhance the economic benefit and the development level of oil field.
Because of the complexity of oilfield production system, if the optimization model is established directly, the mathematical model will be very large and including a mass of mathematical equations, data, pipe line and oil layer constraint conditions. Solving this optimization model in current calculation condition will wastes large amount of computer time, and it is difficulty to popularize in production. Based on the decompose-harmony principal of large scale system, the oilfield production system can be disassembled as five subsystems. They are ground injection system, injection well bore system, reservoir system, oil well lift system, and ground gathering and transport system of oil, gas and water. The flowage of liquid is restricted by many factors. The result is that the system will in a dynamic harmony condition. There is a interface of in-out that exists between two adjacent systems. They are interactional and interinhibitive by the interface.
Introducing into the node analysis method, the five subsystems of oilfield production system can be connected by four nodes. And every node divides the adjacent system into upper system and lower system. The node is the export of the upper system and the entrance of the lower system. The liquid is transferred between the two systems at the point of node. The corresponding dynamic indexes are flux and pressure. Based on the node analysis, utilizing systems engineering process and optimization theory, the objective function and constraint condition of the system can be confirmed, and the optimization model can be established.
Based on the analysis of the ground injection system, feathers of the flow and every part of it are ascertained. Utilizing corresponding parameters, formulas, figures, the ground injection system optimization
model is established, which takes the law of liquid pressure, velocity of flow and temperature etc. in injection water processing stations, pipelines into account. The changing of pressure, velocity of flow, temperature gradient, flow resistance in well bore is analyzed. Based on the well bore structure of injection well and production well and the characteristics of every part, the well bore optimization model of injection well and production well is established. Based on the numerical simulation of double-dimension and double-phase, the reservoir optimization model is established. Integrating with dynamic detaching method, the forecast model of sedimentation unit remaining oil distribution and delamination dynamic indexes is founded. And the corresponding software is programmed. Utilizing fuzzy mathematics technique, the optimization model of delamination treatment projects, which considers the dynamic characters of injection system and production well bore system, is established. According to the established optimization model, and considering the practical value, the result of subsystem can be resolved. In the view of the connected relation of subsystems, the global optimization model that aims at economic benefit can be established, and the global optimization of whole production system can be realized. Finally, the ground and underground general optimization project of one plot in oil field is proposed.
本论文在分析现有油田开发规划优化模型的基础上,根据油气储运、油藏工程和采油工程的基本理论和方法,结合油田开发的系统特征和已有方法在处理多目标多阶段多子系统问题上的局限性,利用系统工程方法、最优化理论和节点分析方法,建立水驱和聚驱条件下,综合考虑经济效益、成本、产量、可采储量等多目标决策模型,通过求解这种大系统优化模型,将地面服从地下、地下兼顾地面的思想模型化、定量化,从而得到油田生产的整体优化方案,按照优化方案,对目前存在“大马拉小车”等现象的地面系统、处于特高含水阶段且水驱剩余油分布不均匀的油藏系统和与之相适应的油、水井系统进行改造,对新投产的井网进行优化设计,从而大幅度降低油田开发成本,提高经济效益和油田开发水平。
由于油田生产系统的复杂性,若直接建立起优化模型,所得到的将是一个包括大量数学方程、数据、曲线、管网和油层约束条件的庞大的数学模型,在现有的计算条件下求解这样的最优化模型会耗费大量的机时,很难在生产中推广应用。本论文根据大系统的分解——协调原理,把油田生产系统分为地面注入系统、注入井井身系统、油藏系统、油井举升系统和地面油气水处理与集输系统等5 个子系统。在每一个子系统中,流体的流动受多种因素的制约,多因素综合影响的结果,使得系统处于一种动态的协调状态。5 个子系统中相邻两个系统存在一个输入和输出的接口,通过这一接口相互影响和制约。
引入节点分析方法,把油田生产系统的5 个子系统用4 个节点相连接,其中每一个结点把相邻的两个系统分为上游系统和下游系统,节点既是上游系统的出口,也是下游系统的入口,在节点处,两个系统进行流体传递,对应的动态指标是流量和压力。在节点分析的基础上,利用系统工程方法和最优化理论,对每一个子系统确定目标函数和约束条件,建立最优化模型。
通过对油田地面注水系统的分析,确定了地面注水系统的流程及其中各个部分的特点,在此基础上,利用相应的参数、公式、图形,建立了综合考虑注入水处理站、配水间、管线等中流体压力、流速、温度等变化规律的地面注水系统优化模型;根据地面油、气、水集输处理系统流程,构建了包括管、罐、站的流体压力、温度、流速变化的数学模型;分析了井身压力、流速、温度梯度、流动阻力的变化,根据注入井、采油井举升结构及各部分的特点,建立了注入井和采油井举升优化模型;在两维两相流数值模拟分析的基础上,建立了油藏系统的优化模型,并结合动态劈分方法构建了满足油藏生产系统优化需要的沉积单元剩余油分布和分层动态指标预测模型,编制了相应的软件;利用模糊数学方法建立了考虑注水系统和采油井举升系统动态特征的分层措施方案优选模型。
考虑控制变量的实际取值,求得了各个子系统模型的解。在此基础上,考虑到子系统间的连接关系,
Oil fields are confronted with a lot of difficult after they entered into the high water-cut stage, such as the sharp aggrandizement of liquid-oil ratio, the great rising of liquid production, the unaccommodated ground projects, the obviously augmentation of treatment workload and cost that in order to maintain the steady production of oil field, and so on. For the sustainable development of oil field, we must optimize every stimulation treatment, and arrange the development work more reasonablely.
Based on the theory and method of oilgas preservation and transportation, petroleum reservoir engineering, and petroleum engineering, the paper analyzes the current programming optimization models of oil field development, combines the system character of oilfield development and the localizations when using current methods to deal with the multiobjective multi-stage and multi-subsystem problem, utilizes systems engineering method, optimization theory and node analysis to establish the multiobjective decision-making model under water drive and polymer displacement. The economic benefit, cost, production and final recovery ratio are considered comprehensively. By solving this largescale optimization model, we can make the idea, underground submit to ground and underground consider the ground, modeling and quantification. So, the global optimization project can be achieved. According to the optimization project, by reconstructing reservoir system whose water cut is very high and remaining oil distribution is asymmetry and the corresponding oil well system and injection well system, and by optimization design the new put into production well net to reduce the cost greatly and enhance the economic benefit and the development level of oil field.
Because of the complexity of oilfield production system, if the optimization model is established directly, the mathematical model will be very large and including a mass of mathematical equations, data, pipe line and oil layer constraint conditions. Solving this optimization model in current calculation condition will wastes large amount of computer time, and it is difficulty to popularize in production. Based on the decompose-harmony principal of large scale system, the oilfield production system can be disassembled as five subsystems. They are ground injection system, injection well bore system, reservoir system, oil well lift system, and ground gathering and transport system of oil, gas and water. The flowage of liquid is restricted by many factors. The result is that the system will in a dynamic harmony condition. There is a interface of in-out that exists between two adjacent systems. They are interactional and interinhibitive by the interface.
Introducing into the node analysis method, the five subsystems of oilfield production system can be connected by four nodes. And every node divides the adjacent system into upper system and lower system. The node is the export of the upper system and the entrance of the lower system. The liquid is transferred between the two systems at the point of node. The corresponding dynamic indexes are flux and pressure. Based on the node analysis, utilizing systems engineering process and optimization theory, the objective function and constraint condition of the system can be confirmed, and the optimization model can be established.
Based on the analysis of the ground injection system, feathers of the flow and every part of it are ascertained. Utilizing corresponding parameters, formulas, figures, the ground injection system optimization
model is established, which takes the law of liquid pressure, velocity of flow and temperature etc. in injection water processing stations, pipelines into account. The changing of pressure, velocity of flow, temperature gradient, flow resistance in well bore is analyzed. Based on the well bore structure of injection well and production well and the characteristics of every part, the well bore optimization model of injection well and production well is established. Based on the numerical simulation of double-dimension and double-phase, the reservoir optimization model is established. Integrating with dynamic detaching method, the forecast model of sedimentation unit remaining oil distribution and delamination dynamic indexes is founded. And the corresponding software is programmed. Utilizing fuzzy mathematics technique, the optimization model of delamination treatment projects, which considers the dynamic characters of injection system and production well bore system, is established. According to the established optimization model, and considering the practical value, the result of subsystem can be resolved. In the view of the connected relation of subsystems, the global optimization model that aims at economic benefit can be established, and the global optimization of whole production system can be realized. Finally, the ground and underground general optimization project of one plot in oil field is proposed.
引文
1. 苏.克磊洛夫. 油田开发科学原理. 北京:石油工业出版社,1956;
2. Mcfarlanc JW, Lasdon WL, et al. Development Planning and Management of Petroleum Reservoir Using Tank model and Nonlinear Programming. Operations Research,1984,32:270~289
3. AmitR. Petroleum Reservoir Exploration: Switching from Primary to Secondary Recovery. Operations Research, 1986,34(4)
4. 王兴峰, 葛家理. 一种资源优化配置方法及其在石油生产规划中的应用. 西安石油学院学报(自然科学版), 2000, 15(6): 53～56
5. Elmtalab, Jabbar. An Expert System for Selection of an Appropriate Enhanced Oil Recovery Method.North Dakota State University of Agriculture and Applied Science, 1993
6. Palaz. I,Bilgesu. HI. Knowledge Based Simulation of Thermal Enhanced Oil Recovery Using Steam. 1990 Conference on Artificial Intelligence in Petroleum Exploration & Production, Texas, 1990
7. E. Bjornsson P. Eng, B. Huncik, et al. Drilling Optimization Using Bit Selection Expert System and ROP Prediction Algorithm Improves Drilling Performance and Enhances Operational Decision Making by Reducing Performance Uncertainties. SPE 90752
8. M. J. Fear, N. C. Meany and J. M. Evans. An Expert System for Drill Bit Selection. SPE/IADC paper no. 27470, SPE/IADC Drilling Conf., Society of Petroleum Engineers, Dallas 1994.
9. Susan Schrader. A New Method of Recoverable Reserves Estimation Using an Expert System. SPE International Student Paper Contest, 2004
10. Phil Rae and Gino Di Lullo. Achieving 100 Percent Success In Acid Stimulation Of Sandstone Reservoirs. SPE 77808, 2002
11. Ge.JL,Xu Hua.KBSTWL: An Expert System for Well Location. 1990 Conference on Artificial Intelligence in Petroleum Exploration & Production, Texas, 1990
12. Mir Asif Sultan and Abdulaziz U. Al-Kaabi. Application of Neural Network to the Determination of Well-Test Interpretation Model for Horizontal Wells. SPE 77878
13. 徐少华, 刘扬, 何新贵. 基于过程神经网络的水淹层自动识别系统. 石油学报, 2004, 25(4): 54～57
14. Y. Zhang, H. Koelemij, et al. Probabilities and Limitations of Neural Network Applications in Estimates of Porosity and Permeability from Wireline Logs. SPE 77880
15. 潘志坚,葛家理. 自适应神经网络示功图识别. 石油学报,1996,17(1), 104～109
16. 吴新根,葛家理. 应用人工神经网络预测油田产量. 石油勘探与开发,1994,21(3): 75～78
17. 赵国忠, 孟曙光, 姜祥成. 聚合物驱含水率的神经网络预测方法. 石油学报, 2004, 25(1): 70～73
18. E. A. Osman and R. E. Abdel-Aal. Abductive Networks: A New Modeling Tool for the Oil and Gas Industry. SPE 77882
19. 王兴峰, 葛家理等. 工业化聚驱地下地面一体化优化规划的网络流模型. 油气田地面工程, 2001, 20(6): 78～81
20. Paul A. Jensen, J. Wesley Barnes. Network Flow Programming. New York: John Wiley&Sons Inc. 1980
21. James. W Mcfarland, Leon Lasdonetal. Development Planning and Management of Petroleum Reservoirs Using Tank Model and Non-linear Programming. Operations Research, 1984, 32:270～289.
22. Wackowski R. K. et al. Applying Rigorous Decision Analysis Methodology to Optimization of a Ter-tiary Recovery Project: Rangely Weber Sand Unit, Colorado. SPE24234,1992.
23. Babayev DA. Mathematical Models for Optimal Timing of Drilling Multilayer Oil and Gas Fields. Mgmt.Sci., 1975, 21:1361～1369.
24. 李泽农,陈德泉等. 油田开发规划优选模型研究. 油气田开发系统工程方法专辑(二).北京:石油工业出版社,1991
25. 齐与峰,朱国金. 注水开发油田稳产规划自适应模型. 油气田开发系统工程方法专辑(二). 北京:石油工业出版社,1991
26. 王宽敬. 石油生产近期优化配产规划系统. 石油大学石油工程系[博士论文],石油大学石油工程系,1992
27. 刘志斌,丁辉,高珉,杨延辉. 油田开发规划产量构成优化模型及其应用. 石油学报,2004,25(1):62~65
28. 刘秀婷,王胜义,杨军. 稠油开发规划方案优选方法新探. 断块油气田,2003,10(1):47~50
29. 魏立新,刘扬,付云霞,赵洪激. 油气集输系统规划方案优化计算. 油气储运,2002,21(4):28~30
30. 殷爱贞,张在旭,李嘉庆. 油田产量优化的目标规划模型. 石油大学学报(自然科学版),2003,27(5):119~121
31. 尚明忠,盖英杰,李树荣,熊福力. 油田开发规划非线性多目标优化模型研究. 石油钻采技术,2003,31(4):59~61
32. 凡哲元,邴绍献,苏映宏,范智慧,丁辉,刘志斌. 油田开发规划优化决策系统研究. 油气地质与采收率,2003,10(6):34~36
33. 肖伟. 刘志斌,郭大立. 基于神经网络的油田注水动态预测. 交通与计算机,1997,15(2):57~60.
34. 李允,刘志斌. 现代优化技术在油田开发中的应用. 北京:石油工业出版社,2001
35. 齐与峰,张辉军,赵成民. 油田中后期稳产规划方法. 石油学报,1992,13(1):52~60
36. 范江. 确定油田开发最佳井数的多目标数学规划方法. 石油学报,1996,17(4):105~108
37. 张在旭. 油田开发系统规划的策略. 石油大学学报(自然科学版),1998,22(2):75~80.
38. Xiao Wei, Liu Zhibin. Multiobjective liner programming model on injection oil field recovery system. Computers Math Applic,1998,36(5):127~135
39. 李从信, 陈淼鑫等.注水系统的计算方法. 石油学报, 1998, 19(3): 120~124
40. 李从信, 刘贤梅, 陈淼鑫. 大型注水系统的优化运行. 石油学报, 2001, 22(6): 69~72
41. 常玉连, 高胜, 郭俊忠. 油田注水系统最优控制数学模型及系统设计. 石油机械, 2001, 29(12): 24~26
42. 赵建华, 韩志刚. 油田产液结构调整规划的一种优化方法. 系统工程学报, 1996, 11(1): 79~85
43. 齐与峰. 注水开发油田中后期动态预测方法和应用软件. 石油勘探与开发,1990,(1)
44. 刘扬,赵洪激,周士华. 低渗透油田地面工程总体规划方案优化研究. 石油学报,2000,21(2):88~95
45. 赵洪激,刘扬,孙旭新. 油田地下地面工程大系统总体规划方案优化研究. 天然气与石油,2003,21(3):10~14
46. 廖红伟, 王凤琴等. 基于大系统方法的油舱动态分析. 石油学报, 2002, 23(6): 45~49
47. 刘洪,赵金洲,胡永全,张绍伦,刘江雁. 模糊神经网络系统在优选压裂井层中的应用. 钻采工艺,2002,25(5):34~37
48. 吴亚红,李秀生,钟大康,林涛. 人工神经网络在压裂选井及选层中的应用,石油大学学报(自然科学版),2001,25(5):42~44
49. 刘长印,孔令飞,张国英,唐永槐,刘志远. 人工智能系统在压裂选井选层方面的应用. 钻采工艺,2003,26(1):37~38
50. 徐敏,赵粉霞. 神经网络法在气井产能预测中的应用. 天然气工业,2000,20(3):73~75
51. 张广杰,刘明新,武若霞. 神经网络在油田动态预测方面的应用. 石油学报,1997,18(4):70~75
52. 曹谢东,陈明强,黄春国,赵刚. 基于因素神经网络的混合型油藏数模辅助专家系统. 西南石油学院学报,1997,19(4):112~116
53. 范学平,吴宏,张华春. 神经网络专家系统及其在压裂酸化决策中的应用研究. 石油勘探与开发,1998,25(1):73~75
54. 刘友家,曾顺鹏,扬秀文. 神经网络专家系统在区块整体油层改造评价中的应用. 钻采工艺,1999,22(5):22~25
55. Nam H. Tran, M.K. Rahman, and Sheik S. Rahman, A Nested Neuro-Fractal-Stochastic Technique for Modeling Naturally Fractured Reservoirs. SPE 77877
56. 张小浩,李秋实,李学森,吴汉宁. 模糊数学在优选油田开发方案中的应用. 西北地质,2002,35(1): 77~80
57. 雍岐东,倪宏伟,肖芳淳,雍岐卫. 用模糊信息熵物元评价法优选气田开发方案. 天然气工业,2003,23(2): 85~86
58. 郭秀英,张秀云. 油(气)田开发方案优选的模糊决策方法. 西南石油学院学报,2002,24(2):71~74
59. Gupta. SP. Composition Effects on Displacement Mechanisms of the Micellar Fluid injected in Sloss Field Test. Soc.Pet.EngJ. 1984,24(1):38
60. Wanosik J. L, et al. Sloss Micellar Pilot:Project Design and Performance.SPE7092,1978
61. FathiZ, et al. Optimization of Enhanced Oil Recovery Process With Boundary Controls. Automatica, 1987
62. FathiZ. Optimization of Enhanced Oil Recovery Process With Boundary Controls A Large Scale Nonlinear Maximization. Ph.D thesis, University of Colorado,1986
63. 周勇, 张占生等. 神经网络自适应PID 控制器应用于螺杆泵井过程控制. 石油仪器, 2004, 18(3): 5~8
64. Russell C. Fontaine, Jeffrey A. Anderson, W. Alan Hunter, Optimization of Injection Field Design with Consideration of Cost and Pressure Constraints Under Conditions of Uncertainty. SPE 90005
65. J. K. Martinez and M. R. Konopczynski. Integrated Reservoir Management in an Intelligent Well Environment SPE 77853
66. Mochizuki S., Saputelli L.A., et al, Real Time Optimization: Classification and Assessment. SPE 90213
67. Saputelli, L.A., et al: Promoting Real-Time Optimization of Hydrocarbon Production Systems, SPE 83978 presented at the SPE Europe Offshore Technology Conference, 3-4 September 2003.
68. Jacques Dumont, Marie-Christine Duzan, etal. Information System Optimization for Operations. SPE 89915
69. 王兴峰, 葛家理. 石油工程方案综合评价优选方法研究述评. 石油规划设计, 2000, 11(6),4~8
70. J.L. Falc?o, Petrobras S.A., Uncertainties in the Pore Pressure Evaluation in Deepwater: a Statistical Approach. SPE 77821
71. R. Soto B., J. Carvajal and F.E. Torres Carreno, How to Integrate Static and Dyanamic Data Using a Connectivity Index and Fuzzy Logic:A Case Study. SPE 77879
72. C.C. Jacinto, Petrobras S.A., The Use of Uncertainty Concepts for the Development of an Oil Field: East Albacora, a Case History. SPE 77917
73. G. Caumon, S. Strebelle, et al, Assessment of Global Uncertainty for Early Appraisal of Hydrocarbon Field. SPE 89943
74. Galli, A. and Armstrong, M: Integrating technical uncertainty into real option, presented at the SPE ATW Risk Analysis Applied to Field Development Under Uncertainty, Rio de Janeiro, 2002
75. Bentley. M. R. and Woodhead T. J.: Uncertainty handling through scenario-based reservoir modeling, paper SPE 39717 presented at the 1998 SPE Asia Pacific Conference on Integrated Modeling for Asset Management, Kuala Lumpur, Insonesia.
76. Journel, A. G. and Bittannov, A.: Uncertainty in N/G ratio in early reservoir development, Journal of Petroleum Science and Engineering (2004) 42 (in paper).
77. Alberto Malinverno, Colin M. Sayers, et al, Integrating Diverse Measurements to Predict Pore Pressure With Uncertainties While Drilling. SPE 90001
78. Jose F. Correa, Paulo C. G. Carvalho, et al, Fuzzy Analysis for Progressing Cavity Pump. SPE 90110
79. Takagi, T. and Sugeno, M.: Fuzzy identification on systems and its application to modeling and control. IEEE Trans. On Systems, Man, and Cybern. Vol. 15 pp. 116-132,1985
80. 任军. 气田开发方案模糊加权聚类评价. 天然气工业, 1993; 14(2): 35~37
81. 刘秀婷,杨军,程仲平. 用模糊层次评判法优选稠油开发规划方案的探讨. 石油勘探与开发, 1996; 23(5): 50~52
82. 肖芳淳. 优选油气田开发方案的模糊物元分析. 石油勘探与开发, 1997; 24(2): 84~86
83. 杨宇,冯文光,杨洪志. 模糊判别在开发模式识别中的应用. 天然气工业, 2001; 21(6): 74~76
84. 孙福街,程松林,李秀生. 层次分析法在油田开发综合评价与方案优选中的应用探讨. 中国海上油气(地质), 2002. 16(5): 328~332
85. 肖芳淳. 天然气集输工程方案三维模糊物元平价法. 油气储运, 1996; 15(6), 1~5
86. 蒲爱华,姚安林. 水下穿越管道设计埋深的模糊决策. 天然气工业,1997;17(3):63~66
87. 汪玉春, 李长俊, 刘玉峰. 油气管道投资的灰色预测与计算. 天然气工业,1998;18(6):68~71
88. 汪玉春,赵胜,陈娟等.天然气长输管道设计方案优选系统.天然气工业,2003;23(6):136~140
89. 余建星,李建辉,黄国平,胡云昌. 基于模糊综合评判的输油管道系统安全风险评价方法. 中国海上油气(工程),1998,10(2):13~19
90. 王优强,张嗣伟. 连续油管可靠性的模糊综合评价. 石油机械,1999,27(9):8~12
91. 付亚荣, 吕德福, 徐定光. 原油集输工程方案信息熵模糊层次优选. 石油规划设计, 1999, 10(6): 27～29
92. 喻西崇,赵金洲,邬亚玲,胡永全,纪禄军. 利用模糊综合评判评价注水管道腐蚀程度. 西南石油学院学报,2003,25(3), 79~82
93. 任志平,刘青,魏立新,汪玉同. 穿越输油管道腐蚀因素的模糊评判. 大庆石油学院学报,2004,28(1):50~52
94. 张效羽,张先普,赵国珍等. 油水井套管变形损坏的模糊评判. 石油钻采工艺, 1996; 18(3): 9~14
95. 张效羽,张先普,赵国珍等. 预测套管寿命的一种新方法. 石油机械, 1996; 24(8): 28~31
96. 刘健,沈秀通,高福光,夏而华. 抽油机技术经济指标的模糊综合评判. 石油机械,1998,26(2):34~36
97. 刘扬松,华北庄. 石油机械零部件强度模糊可靠性预算. 石油矿场机械, 1998; 27(1): 41~42
98. 张玉斌,吴雅娟. 在役抽油机模糊可靠性评定. 石油学报, 1997; 18(4): 131~134
99. 张玉斌,刘彩玉. 石油机械可靠性指标的模糊决策. 石油工业技术监督, 2000; 16(9): 25~29
100. 王向英,钟安详,陶家胜. 用模糊数学原理综合评价抽油机井系统. 油气田地面工程,14(2):14~17
101. 张顶学,廖锐全,李西军等. 电潜泵系统工况的模糊诊断方法. 江汉石油学院学报, 2003; 25(1): 93~94
102. 祝效华,童华,刘清友,肖辞源,杨建忠,陈浩. 基于故障树的套管失效模糊综合评判分析模型. 石油机械,2004,32(2):17~19
103. 时文刚,戴静君,王永. 基于FSVM 的抽油泵示功图识别方法. 石油矿场机械, 2004; 33(4): 46~48
104. 李和全,郎兆新,周万平等. 选择注入井酸化的原则及算化效果预测. 新疆石油地质, 1997, 18(3): 271~276
105. 汪正勇,王晖,徐俊峰. 酸化决策技术在孤岛油田的应用. 油气田地面工程, 2002, 21(3): 6~7
106. 肖芳淳,李宾元. 压裂酸化中选井选层的模糊灰元分析. 钻采工艺, 1996; 19(1): 28~32
107. 肖芳淳. 压裂酸化中选井选层的模糊灰元评价分析. 石油钻采工艺, 1996; 18(6): 49~53
108. 蒋廷学,王欣,王永辉. 压裂优化设计方案的模糊决策方法及应用. 石油钻采工艺, 1996; 19(4): 74~76
109. 蒋廷学. 重复压裂选井选层的模糊识别方法. 石油钻采工艺, 1997; 19(3): 60~62
110. 蒋廷学等. 模糊数学在压裂设计中的应用. 天然气工业,1998,18(3):61~64
111. 吴建发,郭建春,赵金州. 压裂酸化选井模糊综合评判方法. 石油钻采工艺,2004,26(2):54~56
112. 银本才,杜卫平,陈小新. 重复压裂技术研究与应用. 断块油气田, 2001; 8(2): 54~57
113. 冯其红,陈月明,姜汉桥等. 模糊数学在区块整体调剖选井中的应用. 石油勘探与开发, 1998 , 25(3): 76~79
114. 白宝君,李宇乡,刘翔鹗. 区块整体调剖筛选技术研究. 石油钻采工艺, 1999, 21(1): 64~67
115. 陈民锋,郎兆新,张兴焰. 模糊综合评判法评价油田注水开发状况. 断块油气田,2001,8(2):27~30
116. 黄炳光,付永强,唐海,唐刚,王怒涛,周小平. 模糊综合评判法确定水驱油藏的水驱难易程度. 西南石油学院学报,1999,21(4):1~3
117. 唐海,黄炳光,李道轩,曾凡华. 模糊综合评判法确定有藏水驱开发潜力. 石油勘探与开发,2002,29(2):97~99
118. 吕小理,陶先高,廖俊臣,赵兴涛. 模糊综合评判法在油田注水开发状况评价中的应用.江汉石油 学院学报,2001, 23 卷(增):88~91
119. 高有瑞,熊运斌,李春玉.模糊综合评判在聚合物驱油区块评价中的应用. 现代地质,2003,17(2): 222~226
120. 张兴焰,樊继宗,陈天钢,顾勤. 模糊原理综合评判油田注水开发. 特种油气藏,2002,9(1):34~36
121. 熊钰,孙雷,孙良田,武毅. 沈84 块高凝油注水开发效果模糊综合评价. 特种油气藏,2001,8(3):49~52
122. 路秀广,朱法银. 地层损害严重程度模糊综合评判. 西安石油学院学报,1998,13(3): 17~19
123. 桑宇,吴建. 模糊分析方法在采气工艺技术经济指标评价中的应用. 钻采工艺,1998,26(2): 29~31
124. 王广运,黄兴艳,付志国. 大庆喇萨杏油田萨零组油层模糊综合评判. 大庆石油地质与开发,2003,22(5):22~24
125. 郭恩昌、宋考平、石成方、王一民,分层注水情况下的注采系统节点分析方法初探,大庆石油学院学报,97.2
126. 张在旭. 油田开发优化决策的目标规划模型,石油大学学报(自然科学版),2000,24(6):87~90
127. 张在旭,冯德田. 油田开发规划[J]. 系统工程理论与实践,1998,18(15):124~127
128. 严振鸣,彭剑琴. 石油企业管理整体优化. 北京:石油工业出版社,1996
129. 刘宝碇,赵瑞清. 随机规划与模糊规划. 北京:清华大学出版社,1998
130. 汪培庄,李洪兴. 模糊系统理论与模糊计算机. 北京:科学出版社,1996
131. 韩立岩,汪培庄. 应用模糊数学. 北京:首都经济贸易大学出版社,1998
132. 肖芳淳,张效羽,张鹏,姚安林. 模糊分析设计在石油工业中的应用. 北京:石油工业出版社,1993
133. 张在旭. 油田开发最优规划模型的求解. 石油大学学报(自然科学版),1998,22(4):109 ~110
134. 卢名高. 最优化应用技术[M]. 北京:石油工业出版社,1997
135. 周雪漪. 计算水力学. 北京:清华大学出版社,1995
136. 冯树初,郭揆常,王学敏. 油气集输. 北京:石油大学出版社,1988
137. 陈涛平,胡靖邦. 石油工程. 北京:石油工业出版社,2000
138. 李敬松,孙义新,熊海灵,徐春梅. 油田开发经济评价. 北京:石油工业出版社,2000
139. Kaoping Song,Jicheng Zhang, Erlong Yang, A Fuzzy Optimization Model to Select the Fracturing Layers, Proceedings of The 12th IEEE International Conference on Fuzzy Systems,May 25-28,2003, St.Louis,Missouri,USA
140. 张继成,宋考平:利用模糊数学方法优选分层堵水方案,第四届中国青年运筹与管理学者大会论文集,98-103,北京,2001 年9 月
141. Kaoping Song, Xian He, Jicheng Zhang, Erlong Yang: Method to Select Target Wells and Target Zones for the Technique of Water Plugging, the First International Conference on Electronic Business(ICEB 2001), the Chinese University of Hong Kong, 19-21/12/2001
142. Kaoping Song, Shaohua Xu, Jiuzhen Liang, Ronghua Li: A Normalized Fuzzy Neural Network and Its Application, the First International Conference on Electronic Business(ICEB 2001), the Chinese University of Hong Kong, 19-21/12/2001
143. Kaoping Song, Yuzhuo Wang, Jicheng Zhang, Erlong Yang: Fuzzy Optimization of Separate Zone Water Shutoff, The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2-5/12/2001
144. Ruiqing Zhao, Kaoping Song: Redundancy Optimization With Fuzzy Random Lifetimes, The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2-5/12/2001
145. Jin Peng, Kaoping Song: Fuzzy Expected Value Goal Programming Model for Fuzzy Scheduling, The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2-5/12/2001
146. Jian Zhou, Kaoping Song: Facility Location Problem With Fuzzy Random Demands, the First International Conference on Electronic Business(ICEB 2001), the Chinese University of Hong Kong, 19-21/12/2001
147. Kaoping Song, Xiande Zhao, Baoding Liu, Proceedings of the first International Conference on Information and Management Sciences, Xi’An, China, May 27-31,2002, Editor: Eldon Y. Li, Cal Poly State University, Copyright By California Polytechnic State University, USA, ISSN 1539-2023
148. 宋考平、张强、刘克 主编,第五届中国青年运筹与管理学者大会论文集,大庆,2003 年8 月16-19日,香港GLOBAL-LINK INFORMATICS 出版社
149. 宋考平、何鲜、王羽、刘述明,单层剩余油分布及动态指标预测动态劈分法,石油学报,2000 年11 月,第六期;
150. Xiangguo Lu, Kaoping Song, Keliang Wang , Numerical Simulation of Effect of Reservoir Heterogeneity on the Residal Oil Distribution After Polymer Flooding, Scientia Geologica Sinica,Vol.5,June 1996
151. Kaoping Song, Erlong Yang, Jian Li, Li liu, Global Optimization of Adjustment of Water Flooding Oilfield, Proceedings of the first International Conference on Information and Management Sciences, Xi’An, China, May 27-31,2002,pp. 184-186
152. Zhao R.,Song Kaoping, and Zhu J.,Block Replacement for Multi-Component System with Fuzzy Lifetimes, Proceedings of the 11th IEEE International Conference on Fuzzy Systems,Hawaii,USA,May 12-17,2002.
153. 宋考平, 宋洪才, 吴文祥. 油藏数值模拟理论基础. 北京: 石油工业出版社,1996
2. Mcfarlanc JW, Lasdon WL, et al. Development Planning and Management of Petroleum Reservoir Using Tank model and Nonlinear Programming. Operations Research,1984,32:270~289
3. AmitR. Petroleum Reservoir Exploration: Switching from Primary to Secondary Recovery. Operations Research, 1986,34(4)
4. 王兴峰, 葛家理. 一种资源优化配置方法及其在石油生产规划中的应用. 西安石油学院学报(自然科学版), 2000, 15(6): 53～56
5. Elmtalab, Jabbar. An Expert System for Selection of an Appropriate Enhanced Oil Recovery Method.North Dakota State University of Agriculture and Applied Science, 1993
6. Palaz. I,Bilgesu. HI. Knowledge Based Simulation of Thermal Enhanced Oil Recovery Using Steam. 1990 Conference on Artificial Intelligence in Petroleum Exploration & Production, Texas, 1990
7. E. Bjornsson P. Eng, B. Huncik, et al. Drilling Optimization Using Bit Selection Expert System and ROP Prediction Algorithm Improves Drilling Performance and Enhances Operational Decision Making by Reducing Performance Uncertainties. SPE 90752
8. M. J. Fear, N. C. Meany and J. M. Evans. An Expert System for Drill Bit Selection. SPE/IADC paper no. 27470, SPE/IADC Drilling Conf., Society of Petroleum Engineers, Dallas 1994.
9. Susan Schrader. A New Method of Recoverable Reserves Estimation Using an Expert System. SPE International Student Paper Contest, 2004
10. Phil Rae and Gino Di Lullo. Achieving 100 Percent Success In Acid Stimulation Of Sandstone Reservoirs. SPE 77808, 2002
11. Ge.JL,Xu Hua.KBSTWL: An Expert System for Well Location. 1990 Conference on Artificial Intelligence in Petroleum Exploration & Production, Texas, 1990
12. Mir Asif Sultan and Abdulaziz U. Al-Kaabi. Application of Neural Network to the Determination of Well-Test Interpretation Model for Horizontal Wells. SPE 77878
13. 徐少华, 刘扬, 何新贵. 基于过程神经网络的水淹层自动识别系统. 石油学报, 2004, 25(4): 54～57
14. Y. Zhang, H. Koelemij, et al. Probabilities and Limitations of Neural Network Applications in Estimates of Porosity and Permeability from Wireline Logs. SPE 77880
15. 潘志坚,葛家理. 自适应神经网络示功图识别. 石油学报,1996,17(1), 104～109
16. 吴新根,葛家理. 应用人工神经网络预测油田产量. 石油勘探与开发,1994,21(3): 75～78
17. 赵国忠, 孟曙光, 姜祥成. 聚合物驱含水率的神经网络预测方法. 石油学报, 2004, 25(1): 70～73
18. E. A. Osman and R. E. Abdel-Aal. Abductive Networks: A New Modeling Tool for the Oil and Gas Industry. SPE 77882
19. 王兴峰, 葛家理等. 工业化聚驱地下地面一体化优化规划的网络流模型. 油气田地面工程, 2001, 20(6): 78～81
20. Paul A. Jensen, J. Wesley Barnes. Network Flow Programming. New York: John Wiley&Sons Inc. 1980
21. James. W Mcfarland, Leon Lasdonetal. Development Planning and Management of Petroleum Reservoirs Using Tank Model and Non-linear Programming. Operations Research, 1984, 32:270～289.
22. Wackowski R. K. et al. Applying Rigorous Decision Analysis Methodology to Optimization of a Ter-tiary Recovery Project: Rangely Weber Sand Unit, Colorado. SPE24234,1992.
23. Babayev DA. Mathematical Models for Optimal Timing of Drilling Multilayer Oil and Gas Fields. Mgmt.Sci., 1975, 21:1361～1369.
24. 李泽农,陈德泉等. 油田开发规划优选模型研究. 油气田开发系统工程方法专辑(二).北京:石油工业出版社,1991
25. 齐与峰,朱国金. 注水开发油田稳产规划自适应模型. 油气田开发系统工程方法专辑(二). 北京:石油工业出版社,1991
26. 王宽敬. 石油生产近期优化配产规划系统. 石油大学石油工程系[博士论文],石油大学石油工程系,1992
27. 刘志斌,丁辉,高珉,杨延辉. 油田开发规划产量构成优化模型及其应用. 石油学报,2004,25(1):62~65
28. 刘秀婷,王胜义,杨军. 稠油开发规划方案优选方法新探. 断块油气田,2003,10(1):47~50
29. 魏立新,刘扬,付云霞,赵洪激. 油气集输系统规划方案优化计算. 油气储运,2002,21(4):28~30
30. 殷爱贞,张在旭,李嘉庆. 油田产量优化的目标规划模型. 石油大学学报(自然科学版),2003,27(5):119~121
31. 尚明忠,盖英杰,李树荣,熊福力. 油田开发规划非线性多目标优化模型研究. 石油钻采技术,2003,31(4):59~61
32. 凡哲元,邴绍献,苏映宏,范智慧,丁辉,刘志斌. 油田开发规划优化决策系统研究. 油气地质与采收率,2003,10(6):34~36
33. 肖伟. 刘志斌,郭大立. 基于神经网络的油田注水动态预测. 交通与计算机,1997,15(2):57~60.
34. 李允,刘志斌. 现代优化技术在油田开发中的应用. 北京:石油工业出版社,2001
35. 齐与峰,张辉军,赵成民. 油田中后期稳产规划方法. 石油学报,1992,13(1):52~60
36. 范江. 确定油田开发最佳井数的多目标数学规划方法. 石油学报,1996,17(4):105~108
37. 张在旭. 油田开发系统规划的策略. 石油大学学报(自然科学版),1998,22(2):75~80.
38. Xiao Wei, Liu Zhibin. Multiobjective liner programming model on injection oil field recovery system. Computers Math Applic,1998,36(5):127~135
39. 李从信, 陈淼鑫等.注水系统的计算方法. 石油学报, 1998, 19(3): 120~124
40. 李从信, 刘贤梅, 陈淼鑫. 大型注水系统的优化运行. 石油学报, 2001, 22(6): 69~72
41. 常玉连, 高胜, 郭俊忠. 油田注水系统最优控制数学模型及系统设计. 石油机械, 2001, 29(12): 24~26
42. 赵建华, 韩志刚. 油田产液结构调整规划的一种优化方法. 系统工程学报, 1996, 11(1): 79~85
43. 齐与峰. 注水开发油田中后期动态预测方法和应用软件. 石油勘探与开发,1990,(1)
44. 刘扬,赵洪激,周士华. 低渗透油田地面工程总体规划方案优化研究. 石油学报,2000,21(2):88~95
45. 赵洪激,刘扬,孙旭新. 油田地下地面工程大系统总体规划方案优化研究. 天然气与石油,2003,21(3):10~14
46. 廖红伟, 王凤琴等. 基于大系统方法的油舱动态分析. 石油学报, 2002, 23(6): 45~49
47. 刘洪,赵金洲,胡永全,张绍伦,刘江雁. 模糊神经网络系统在优选压裂井层中的应用. 钻采工艺,2002,25(5):34~37
48. 吴亚红,李秀生,钟大康,林涛. 人工神经网络在压裂选井及选层中的应用,石油大学学报(自然科学版),2001,25(5):42~44
49. 刘长印,孔令飞,张国英,唐永槐,刘志远. 人工智能系统在压裂选井选层方面的应用. 钻采工艺,2003,26(1):37~38
50. 徐敏,赵粉霞. 神经网络法在气井产能预测中的应用. 天然气工业,2000,20(3):73~75
51. 张广杰,刘明新,武若霞. 神经网络在油田动态预测方面的应用. 石油学报,1997,18(4):70~75
52. 曹谢东,陈明强,黄春国,赵刚. 基于因素神经网络的混合型油藏数模辅助专家系统. 西南石油学院学报,1997,19(4):112~116
53. 范学平,吴宏,张华春. 神经网络专家系统及其在压裂酸化决策中的应用研究. 石油勘探与开发,1998,25(1):73~75
54. 刘友家,曾顺鹏,扬秀文. 神经网络专家系统在区块整体油层改造评价中的应用. 钻采工艺,1999,22(5):22~25
55. Nam H. Tran, M.K. Rahman, and Sheik S. Rahman, A Nested Neuro-Fractal-Stochastic Technique for Modeling Naturally Fractured Reservoirs. SPE 77877
56. 张小浩,李秋实,李学森,吴汉宁. 模糊数学在优选油田开发方案中的应用. 西北地质,2002,35(1): 77~80
57. 雍岐东,倪宏伟,肖芳淳,雍岐卫. 用模糊信息熵物元评价法优选气田开发方案. 天然气工业,2003,23(2): 85~86
58. 郭秀英,张秀云. 油(气)田开发方案优选的模糊决策方法. 西南石油学院学报,2002,24(2):71~74
59. Gupta. SP. Composition Effects on Displacement Mechanisms of the Micellar Fluid injected in Sloss Field Test. Soc.Pet.EngJ. 1984,24(1):38
60. Wanosik J. L, et al. Sloss Micellar Pilot:Project Design and Performance.SPE7092,1978
61. FathiZ, et al. Optimization of Enhanced Oil Recovery Process With Boundary Controls. Automatica, 1987
62. FathiZ. Optimization of Enhanced Oil Recovery Process With Boundary Controls A Large Scale Nonlinear Maximization. Ph.D thesis, University of Colorado,1986
63. 周勇, 张占生等. 神经网络自适应PID 控制器应用于螺杆泵井过程控制. 石油仪器, 2004, 18(3): 5~8
64. Russell C. Fontaine, Jeffrey A. Anderson, W. Alan Hunter, Optimization of Injection Field Design with Consideration of Cost and Pressure Constraints Under Conditions of Uncertainty. SPE 90005
65. J. K. Martinez and M. R. Konopczynski. Integrated Reservoir Management in an Intelligent Well Environment SPE 77853
66. Mochizuki S., Saputelli L.A., et al, Real Time Optimization: Classification and Assessment. SPE 90213
67. Saputelli, L.A., et al: Promoting Real-Time Optimization of Hydrocarbon Production Systems, SPE 83978 presented at the SPE Europe Offshore Technology Conference, 3-4 September 2003.
68. Jacques Dumont, Marie-Christine Duzan, etal. Information System Optimization for Operations. SPE 89915
69. 王兴峰, 葛家理. 石油工程方案综合评价优选方法研究述评. 石油规划设计, 2000, 11(6),4~8
70. J.L. Falc?o, Petrobras S.A., Uncertainties in the Pore Pressure Evaluation in Deepwater: a Statistical Approach. SPE 77821
71. R. Soto B., J. Carvajal and F.E. Torres Carreno, How to Integrate Static and Dyanamic Data Using a Connectivity Index and Fuzzy Logic:A Case Study. SPE 77879
72. C.C. Jacinto, Petrobras S.A., The Use of Uncertainty Concepts for the Development of an Oil Field: East Albacora, a Case History. SPE 77917
73. G. Caumon, S. Strebelle, et al, Assessment of Global Uncertainty for Early Appraisal of Hydrocarbon Field. SPE 89943
74. Galli, A. and Armstrong, M: Integrating technical uncertainty into real option, presented at the SPE ATW Risk Analysis Applied to Field Development Under Uncertainty, Rio de Janeiro, 2002
75. Bentley. M. R. and Woodhead T. J.: Uncertainty handling through scenario-based reservoir modeling, paper SPE 39717 presented at the 1998 SPE Asia Pacific Conference on Integrated Modeling for Asset Management, Kuala Lumpur, Insonesia.
76. Journel, A. G. and Bittannov, A.: Uncertainty in N/G ratio in early reservoir development, Journal of Petroleum Science and Engineering (2004) 42 (in paper).
77. Alberto Malinverno, Colin M. Sayers, et al, Integrating Diverse Measurements to Predict Pore Pressure With Uncertainties While Drilling. SPE 90001
78. Jose F. Correa, Paulo C. G. Carvalho, et al, Fuzzy Analysis for Progressing Cavity Pump. SPE 90110
79. Takagi, T. and Sugeno, M.: Fuzzy identification on systems and its application to modeling and control. IEEE Trans. On Systems, Man, and Cybern. Vol. 15 pp. 116-132,1985
80. 任军. 气田开发方案模糊加权聚类评价. 天然气工业, 1993; 14(2): 35~37
81. 刘秀婷,杨军,程仲平. 用模糊层次评判法优选稠油开发规划方案的探讨. 石油勘探与开发, 1996; 23(5): 50~52
82. 肖芳淳. 优选油气田开发方案的模糊物元分析. 石油勘探与开发, 1997; 24(2): 84~86
83. 杨宇,冯文光,杨洪志. 模糊判别在开发模式识别中的应用. 天然气工业, 2001; 21(6): 74~76
84. 孙福街,程松林,李秀生. 层次分析法在油田开发综合评价与方案优选中的应用探讨. 中国海上油气(地质), 2002. 16(5): 328~332
85. 肖芳淳. 天然气集输工程方案三维模糊物元平价法. 油气储运, 1996; 15(6), 1~5
86. 蒲爱华,姚安林. 水下穿越管道设计埋深的模糊决策. 天然气工业,1997;17(3):63~66
87. 汪玉春, 李长俊, 刘玉峰. 油气管道投资的灰色预测与计算. 天然气工业,1998;18(6):68~71
88. 汪玉春,赵胜,陈娟等.天然气长输管道设计方案优选系统.天然气工业,2003;23(6):136~140
89. 余建星,李建辉,黄国平,胡云昌. 基于模糊综合评判的输油管道系统安全风险评价方法. 中国海上油气(工程),1998,10(2):13~19
90. 王优强,张嗣伟. 连续油管可靠性的模糊综合评价. 石油机械,1999,27(9):8~12
91. 付亚荣, 吕德福, 徐定光. 原油集输工程方案信息熵模糊层次优选. 石油规划设计, 1999, 10(6): 27～29
92. 喻西崇,赵金洲,邬亚玲,胡永全,纪禄军. 利用模糊综合评判评价注水管道腐蚀程度. 西南石油学院学报,2003,25(3), 79~82
93. 任志平,刘青,魏立新,汪玉同. 穿越输油管道腐蚀因素的模糊评判. 大庆石油学院学报,2004,28(1):50~52
94. 张效羽,张先普,赵国珍等. 油水井套管变形损坏的模糊评判. 石油钻采工艺, 1996; 18(3): 9~14
95. 张效羽,张先普,赵国珍等. 预测套管寿命的一种新方法. 石油机械, 1996; 24(8): 28~31
96. 刘健,沈秀通,高福光,夏而华. 抽油机技术经济指标的模糊综合评判. 石油机械,1998,26(2):34~36
97. 刘扬松,华北庄. 石油机械零部件强度模糊可靠性预算. 石油矿场机械, 1998; 27(1): 41~42
98. 张玉斌,吴雅娟. 在役抽油机模糊可靠性评定. 石油学报, 1997; 18(4): 131~134
99. 张玉斌,刘彩玉. 石油机械可靠性指标的模糊决策. 石油工业技术监督, 2000; 16(9): 25~29
100. 王向英,钟安详,陶家胜. 用模糊数学原理综合评价抽油机井系统. 油气田地面工程,14(2):14~17
101. 张顶学,廖锐全,李西军等. 电潜泵系统工况的模糊诊断方法. 江汉石油学院学报, 2003; 25(1): 93~94
102. 祝效华,童华,刘清友,肖辞源,杨建忠,陈浩. 基于故障树的套管失效模糊综合评判分析模型. 石油机械,2004,32(2):17~19
103. 时文刚,戴静君,王永. 基于FSVM 的抽油泵示功图识别方法. 石油矿场机械, 2004; 33(4): 46~48
104. 李和全,郎兆新,周万平等. 选择注入井酸化的原则及算化效果预测. 新疆石油地质, 1997, 18(3): 271~276
105. 汪正勇,王晖,徐俊峰. 酸化决策技术在孤岛油田的应用. 油气田地面工程, 2002, 21(3): 6~7
106. 肖芳淳,李宾元. 压裂酸化中选井选层的模糊灰元分析. 钻采工艺, 1996; 19(1): 28~32
107. 肖芳淳. 压裂酸化中选井选层的模糊灰元评价分析. 石油钻采工艺, 1996; 18(6): 49~53
108. 蒋廷学,王欣,王永辉. 压裂优化设计方案的模糊决策方法及应用. 石油钻采工艺, 1996; 19(4): 74~76
109. 蒋廷学. 重复压裂选井选层的模糊识别方法. 石油钻采工艺, 1997; 19(3): 60~62
110. 蒋廷学等. 模糊数学在压裂设计中的应用. 天然气工业,1998,18(3):61~64
111. 吴建发,郭建春,赵金州. 压裂酸化选井模糊综合评判方法. 石油钻采工艺,2004,26(2):54~56
112. 银本才,杜卫平,陈小新. 重复压裂技术研究与应用. 断块油气田, 2001; 8(2): 54~57
113. 冯其红,陈月明,姜汉桥等. 模糊数学在区块整体调剖选井中的应用. 石油勘探与开发, 1998 , 25(3): 76~79
114. 白宝君,李宇乡,刘翔鹗. 区块整体调剖筛选技术研究. 石油钻采工艺, 1999, 21(1): 64~67
115. 陈民锋,郎兆新,张兴焰. 模糊综合评判法评价油田注水开发状况. 断块油气田,2001,8(2):27~30
116. 黄炳光,付永强,唐海,唐刚,王怒涛,周小平. 模糊综合评判法确定水驱油藏的水驱难易程度. 西南石油学院学报,1999,21(4):1~3
117. 唐海,黄炳光,李道轩,曾凡华. 模糊综合评判法确定有藏水驱开发潜力. 石油勘探与开发,2002,29(2):97~99
118. 吕小理,陶先高,廖俊臣,赵兴涛. 模糊综合评判法在油田注水开发状况评价中的应用.江汉石油 学院学报,2001, 23 卷(增):88~91
119. 高有瑞,熊运斌,李春玉.模糊综合评判在聚合物驱油区块评价中的应用. 现代地质,2003,17(2): 222~226
120. 张兴焰,樊继宗,陈天钢,顾勤. 模糊原理综合评判油田注水开发. 特种油气藏,2002,9(1):34~36
121. 熊钰,孙雷,孙良田,武毅. 沈84 块高凝油注水开发效果模糊综合评价. 特种油气藏,2001,8(3):49~52
122. 路秀广,朱法银. 地层损害严重程度模糊综合评判. 西安石油学院学报,1998,13(3): 17~19
123. 桑宇,吴建. 模糊分析方法在采气工艺技术经济指标评价中的应用. 钻采工艺,1998,26(2): 29~31
124. 王广运,黄兴艳,付志国. 大庆喇萨杏油田萨零组油层模糊综合评判. 大庆石油地质与开发,2003,22(5):22~24
125. 郭恩昌、宋考平、石成方、王一民,分层注水情况下的注采系统节点分析方法初探,大庆石油学院学报,97.2
126. 张在旭. 油田开发优化决策的目标规划模型,石油大学学报(自然科学版),2000,24(6):87~90
127. 张在旭,冯德田. 油田开发规划[J]. 系统工程理论与实践,1998,18(15):124~127
128. 严振鸣,彭剑琴. 石油企业管理整体优化. 北京:石油工业出版社,1996
129. 刘宝碇,赵瑞清. 随机规划与模糊规划. 北京:清华大学出版社,1998
130. 汪培庄,李洪兴. 模糊系统理论与模糊计算机. 北京:科学出版社,1996
131. 韩立岩,汪培庄. 应用模糊数学. 北京:首都经济贸易大学出版社,1998
132. 肖芳淳,张效羽,张鹏,姚安林. 模糊分析设计在石油工业中的应用. 北京:石油工业出版社,1993
133. 张在旭. 油田开发最优规划模型的求解. 石油大学学报(自然科学版),1998,22(4):109 ~110
134. 卢名高. 最优化应用技术[M]. 北京:石油工业出版社,1997
135. 周雪漪. 计算水力学. 北京:清华大学出版社,1995
136. 冯树初,郭揆常,王学敏. 油气集输. 北京:石油大学出版社,1988
137. 陈涛平,胡靖邦. 石油工程. 北京:石油工业出版社,2000
138. 李敬松,孙义新,熊海灵,徐春梅. 油田开发经济评价. 北京:石油工业出版社,2000
139. Kaoping Song,Jicheng Zhang, Erlong Yang, A Fuzzy Optimization Model to Select the Fracturing Layers, Proceedings of The 12th IEEE International Conference on Fuzzy Systems,May 25-28,2003, St.Louis,Missouri,USA
140. 张继成,宋考平:利用模糊数学方法优选分层堵水方案,第四届中国青年运筹与管理学者大会论文集,98-103,北京,2001 年9 月
141. Kaoping Song, Xian He, Jicheng Zhang, Erlong Yang: Method to Select Target Wells and Target Zones for the Technique of Water Plugging, the First International Conference on Electronic Business(ICEB 2001), the Chinese University of Hong Kong, 19-21/12/2001
142. Kaoping Song, Shaohua Xu, Jiuzhen Liang, Ronghua Li: A Normalized Fuzzy Neural Network and Its Application, the First International Conference on Electronic Business(ICEB 2001), the Chinese University of Hong Kong, 19-21/12/2001
143. Kaoping Song, Yuzhuo Wang, Jicheng Zhang, Erlong Yang: Fuzzy Optimization of Separate Zone Water Shutoff, The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2-5/12/2001
144. Ruiqing Zhao, Kaoping Song: Redundancy Optimization With Fuzzy Random Lifetimes, The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2-5/12/2001
145. Jin Peng, Kaoping Song: Fuzzy Expected Value Goal Programming Model for Fuzzy Scheduling, The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2-5/12/2001
146. Jian Zhou, Kaoping Song: Facility Location Problem With Fuzzy Random Demands, the First International Conference on Electronic Business(ICEB 2001), the Chinese University of Hong Kong, 19-21/12/2001
147. Kaoping Song, Xiande Zhao, Baoding Liu, Proceedings of the first International Conference on Information and Management Sciences, Xi’An, China, May 27-31,2002, Editor: Eldon Y. Li, Cal Poly State University, Copyright By California Polytechnic State University, USA, ISSN 1539-2023
148. 宋考平、张强、刘克 主编,第五届中国青年运筹与管理学者大会论文集,大庆,2003 年8 月16-19日,香港GLOBAL-LINK INFORMATICS 出版社
149. 宋考平、何鲜、王羽、刘述明,单层剩余油分布及动态指标预测动态劈分法,石油学报,2000 年11 月,第六期;
150. Xiangguo Lu, Kaoping Song, Keliang Wang , Numerical Simulation of Effect of Reservoir Heterogeneity on the Residal Oil Distribution After Polymer Flooding, Scientia Geologica Sinica,Vol.5,June 1996
151. Kaoping Song, Erlong Yang, Jian Li, Li liu, Global Optimization of Adjustment of Water Flooding Oilfield, Proceedings of the first International Conference on Information and Management Sciences, Xi’An, China, May 27-31,2002,pp. 184-186
152. Zhao R.,Song Kaoping, and Zhu J.,Block Replacement for Multi-Component System with Fuzzy Lifetimes, Proceedings of the 11th IEEE International Conference on Fuzzy Systems,Hawaii,USA,May 12-17,2002.
153. 宋考平, 宋洪才, 吴文祥. 油藏数值模拟理论基础. 北京: 石油工业出版社,1996