基于支持向量机的储层缝洞预测方法研究及应用
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摘要
储层缝洞预测是油气勘探开发的重点,也是难点,属于世界级的难题。能否准确预测缝洞系统的发育程度及其位置将直接影响勘探开发的效果,历来受到油气勘探开发专家及工作人员的高度重视。因此,寻求一种良好的预测方法成为储层缝洞预测的重要环节。从上个世纪70年代起,国内外很多学者致力于缝洞系统预测的研究,提出了许多缝洞预测方法,譬如模式识别、神经网络、灰色遗传等等。诚然这些方法在理论及实际应用中都取得了一定的效果,但是还存在诸如地震属性利用有限、定量描述不足、预测精度不够高等问题。
此外,利用地震资料进行储层缝洞预测,不管采用哪一种方法都必须对大量的地震属性进行提取和分类,再加上地质问题本身具有不确定性和随机性的典型特征,因此必然涉及到地震属性的优选问题。
针对上述问题,本文提出基于支持向量机的储层缝洞预测方法。其基本思想是:首先,利用粗糙集分析方法对所提取的地震属性进行优化处理,获得属性约简组合;然后,利用约简的地震属性作为条件属性,缝洞发育程度作为决策属性构建基于支持向量机的储层缝洞预测模型;最后,将所构建的模型应用于中伊朗盆地Kashan区块,以检验该方法的实际应用效果。
经过研究,论文主要取得了以下几点进步:
1.利用粗糙集进行地震属性约简,能够在保持原有分类能力的情况下,提取反映储层缝洞发育程度的主要特征参数,大大降低了属性维数,减少了计算量,也精简了模型,使得模型更为可靠。
2.将粗糙集和支持向量机相结合,粗糙集作为前置系统,支持向量机作为后置系统,构建了基于支持向量机的缝洞预测模型。该模型具有回判率高和识别预测精度高的优点。
3.将构建的缝洞预测模型应用于中伊朗盆地Kashan区块库姆组F-E、C和B-A三个层段,实现了三个层段缝洞系统的横向预测,可为该区块的进一步勘探开发提供参考。
Fracture and cave prediction, a world-class issue, is the focus of oil and gas exploration and development. The accuracy of prediction on fracture-cave development degree and location will have direct impact on the effect of the exploration and development, so it has always been attracted great importance by the oil-gas exploration experts and workers. Therefore it is a key procedure to seek a better method for fracture-cave reservoir prediction. From 1970s, many scholars at home and abroad have been devoted themself to the study of fracture-cave systems prediction. They put forward plenty of methods, such as pattern recognition, neural networks, grey genetic algorithm, and so on. It is true that these methods have achieved certain degree of success in the theory and practical applications, but there are still a lot of problems, for example,limited using of seismic attributes, lacking of quantitative description, low prediction accuracy.
In addition, the use of seismic data to predict fracture-cave reservoir no matter what method has been chosen, large number of seismic attributes must be extracted and classified. Added with the uncertainty and randomness of geology itself, So, it certainly involves the preferential optimization of seismic attributes.
In response to above problems, this paper proposes a fracture-cave reservoir prediction method relying on support vector machine. The foundamental idea is: firstly, using rough set to optimize the seismic attributes and obtaining combination of attribute reduction; then, using seismic attributes which have been reduced as condtional attribute, fracture-cave development degree as decision attribute to build fracture-cave reservoir prediction model basing on support vector machine. Finally, the model was applied to the Kashan block in middle Iran basin to test the practical applicational effects of the method.
Through researching, the thesis has made the following progresses:
1. Using rough set to reduce the seismic attributes can extract the main characteristic parameters that can reflect the development degree of fracture-cave reservoir without changing the original classification capacity. This procedure also greatly brings down the dimension of attributes, reduces the calculation, simplifies the model and makes the model more reliable.
2. Combining rough set with support vector machine, using rough sets as the pre-system and support vector machine as the rear system, building a fracture-cave reservoir prediction model basing on support vector machine. The model has high rate of return to contractors and accuracy of identification and prediction.
3. Applying the model this paper built to Qom Formation in the Kashan Block, achieved the horizontal prediction in F-E、C and B-A layers, which could provide reference for the further exploration and development of this block.
此外,利用地震资料进行储层缝洞预测,不管采用哪一种方法都必须对大量的地震属性进行提取和分类,再加上地质问题本身具有不确定性和随机性的典型特征,因此必然涉及到地震属性的优选问题。
针对上述问题,本文提出基于支持向量机的储层缝洞预测方法。其基本思想是:首先,利用粗糙集分析方法对所提取的地震属性进行优化处理,获得属性约简组合;然后,利用约简的地震属性作为条件属性,缝洞发育程度作为决策属性构建基于支持向量机的储层缝洞预测模型;最后,将所构建的模型应用于中伊朗盆地Kashan区块,以检验该方法的实际应用效果。
经过研究,论文主要取得了以下几点进步:
1.利用粗糙集进行地震属性约简,能够在保持原有分类能力的情况下,提取反映储层缝洞发育程度的主要特征参数,大大降低了属性维数,减少了计算量,也精简了模型,使得模型更为可靠。
2.将粗糙集和支持向量机相结合,粗糙集作为前置系统,支持向量机作为后置系统,构建了基于支持向量机的缝洞预测模型。该模型具有回判率高和识别预测精度高的优点。
3.将构建的缝洞预测模型应用于中伊朗盆地Kashan区块库姆组F-E、C和B-A三个层段,实现了三个层段缝洞系统的横向预测,可为该区块的进一步勘探开发提供参考。
Fracture and cave prediction, a world-class issue, is the focus of oil and gas exploration and development. The accuracy of prediction on fracture-cave development degree and location will have direct impact on the effect of the exploration and development, so it has always been attracted great importance by the oil-gas exploration experts and workers. Therefore it is a key procedure to seek a better method for fracture-cave reservoir prediction. From 1970s, many scholars at home and abroad have been devoted themself to the study of fracture-cave systems prediction. They put forward plenty of methods, such as pattern recognition, neural networks, grey genetic algorithm, and so on. It is true that these methods have achieved certain degree of success in the theory and practical applications, but there are still a lot of problems, for example,limited using of seismic attributes, lacking of quantitative description, low prediction accuracy.
In addition, the use of seismic data to predict fracture-cave reservoir no matter what method has been chosen, large number of seismic attributes must be extracted and classified. Added with the uncertainty and randomness of geology itself, So, it certainly involves the preferential optimization of seismic attributes.
In response to above problems, this paper proposes a fracture-cave reservoir prediction method relying on support vector machine. The foundamental idea is: firstly, using rough set to optimize the seismic attributes and obtaining combination of attribute reduction; then, using seismic attributes which have been reduced as condtional attribute, fracture-cave development degree as decision attribute to build fracture-cave reservoir prediction model basing on support vector machine. Finally, the model was applied to the Kashan block in middle Iran basin to test the practical applicational effects of the method.
Through researching, the thesis has made the following progresses:
1. Using rough set to reduce the seismic attributes can extract the main characteristic parameters that can reflect the development degree of fracture-cave reservoir without changing the original classification capacity. This procedure also greatly brings down the dimension of attributes, reduces the calculation, simplifies the model and makes the model more reliable.
2. Combining rough set with support vector machine, using rough sets as the pre-system and support vector machine as the rear system, building a fracture-cave reservoir prediction model basing on support vector machine. The model has high rate of return to contractors and accuracy of identification and prediction.
3. Applying the model this paper built to Qom Formation in the Kashan Block, achieved the horizontal prediction in F-E、C and B-A layers, which could provide reference for the further exploration and development of this block.
引文
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[2]曹辉.关于地震属性的几点思考.石油物探[J] ,2000(增刊):162～166.
[3]黄捍东,魏修成,叶连池等.分形边缘检测在裂缝预测中的应用[J] .石油地球物理勘探,2002(1):65-68.
[4]中思瑛.有限元应力法在构造裂缝预测中的应用[J] .石油天然气学报,2005,27(4):556-558.
[5]李操.基于地震属性的储层预测方法研究及初步应用[D] .大庆:大庆石油学院,2006.
[6]沈永红.油气储层预测RS理论-信息熵-小波神经网络模型研究[D] .成都:成都理工大学,2007.
[7]周文.裂缝性油气储集层评价方法[M] .成都:四川科学技术出版社,1998.
[8]王珏,刘三阳,张杰.模糊决策表的数据挖掘[J] .计算机工程与应,2003(14):73-74.
[9]梁美莲.不完备信息系统中数据挖掘的粗糙集方法[D] .南宁:广西大学,2005.
[10]吴成东,许可,韩中华等.基于粗糙集和决策树的数据挖掘方法[J] .2006 (5):482-485.
[11]程岩.数据库中挖掘决策偏好信息的粗糙集方法研究[J] .计算机工程,2003(6):14-16.
[12]菅利荣,刘思峰.模糊决策表中获取概率规则的扩展VPRS方法[J] .控制与决策,2005(11):1291-1295.
[13]蒙祖强,蔡自兴.个性化决策规则的发现:一种基于Rough Set的方法[J] .控制与决策,2004 (9):994-999.
[14] Divyendu Sinha,Philip Laplante.A rough set-based approach to handling spatial uncertainty in binary images.Engineering Application of Artificial Intelligence,2004,17:97-110.
[15] Asharaf S.Narasimha Murty.A rough fuzzy approach to web usage categorization.Fuzzy Sets and Systems,2004,148:119-129.
[16]许峰,于天彪,巩亚东等.基于粗糙集的人力资源模糊指派[J] .东北大学学报.2006 (3):308-311.
[17]安利平,陈增强,袁著祉.基于粗集理论的多属性决策分析[J] .控制与决策,2005(3):294-298.
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