火山岩岩性识别和储层评价的理论与技术研究
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摘要
从19世纪末起,世界各地相继涌现出储量巨大的火山岩油气藏,其所具备的巨大勘探开发潜力,日益引起世界各国研究者们的广泛关注和兴趣,火山岩油气藏已经成为目前油气勘探开发领域里研究热点。
本论文基于上述背景,针对火山岩储层测井解释所面临的难题,建立了一套火山岩储层测井识别与评价的理论和技术。将测井解释模型的建立作为根本出发点,即建立一个针对火山岩地层由岩石骨架和孔隙两部分组成的体积模型。为准确求取体积模型中两部分的各种参数而提出了一些新的见解和具有创新性的处理方法。面对岩性多样且组分变化大的火山岩储层,通过统计分析方法及模式识别方法对测井数据的处理,使利用测井资料所识别的岩性类型能够媲美地质定名的丰富和精准；火山岩储层复杂的储集空间和渗流通道造成的其储层非均质性明显,通过对成像测井图像的数字图像处理和成像测井采样数据矩阵的处理能够有效地获得各孔隙度数值。在此基础上对后续储层的渗透率和饱和度等参数进行准确求取,从而实现了针对火山岩储层广泛通用的测井评价理论和数据处理方法。
As numbers of huge volcanic reservoirs have been found all over the world since 19th century 20 years, the enormous potential for exploration and development of volcanic reservoirs has aroused much attention and interest of the researchers around the world. At present,volcanic reservoir is becoming a research focus in the area of oil and gas exploration.
Because of the variety of lithology and the complexity of reservoir space, as well as the poor physical properties, volcanic reservoir has more complex rock-electricity relation compared with clastic sandstone and carbonate rocks, and this brings great difficulties about the logging interpretation on volcanic reservoir. The main difficulties are:1) improving the coincidence rate of lithology identification when dealing with the volcanic reservoirs with complex lithology and components; 2) immature theory and methods for accurate identification and quantitative evaluation of fractures and vugs, which are the storage space and channel of fluid for volcanic reservoir; 3) the unreasonable interpretation model for volcanic reservoir, because in the past rocks were directly used as component to establish volume model, rather than mineral volume model, thus affecting the calculation accuracy of reservoir porosity, permeability, saturation and some other parameters.
The identification of volcanic reservoir's lithology, quantitative evaluation of fractures and building the logging interpretation model are important supplement to the reservoir evaluation after clastic rocks and carbonate rocks. As the evolvement to the traditional logging data processing methods and interpretation theory, the logging evaluation techniques and interpretation methods for volcanic reservoir have significant theory meaning, and will promote the exploration and development of various volcanic reservoirs in our country.
As the existing logging evaluation techniques and interpretation methods have a series of problems and difficulties in theory and data processing, systematic research has to be carried out to establish appropriate evaluation theory and interpretation method, so that we could make a breakthrough in the logging evaluation of volcanic reservoir. According to the existing problems and difficulties in logging evaluation on volcanic reservoirs, we systematically studied the theories and techniques for volcanic reservoir identification and evaluation. Based on the establishment of the logging interpretation model, appropriate evaluation theory and data processing method were established to obtain each parameter of the model. We strived to establish a general evaluation theory and data processing method for volcanic reservoirs.
1. Summary of volcanic rocks logging response characteristics
Based on the domestic and foreign literatures as well as our years of research on the deep volcanic reservoir in Songliao basin (China), we systematically reviewed the logging response characteristics and response rules of volcanic strata.
1) In detail, we reviewed the logging response characteristics of the typical volcanic rocks in Songliao basin, including a variety of conventional features of logging curves and the image response characteristics of formation image logging. Given the specific range of logging response parameters of volcanic rocks in Songliao basin, general rules for the response characteristics of volcanic rocks was concluded.
2) We also discussed the general characteristics of volcanic reservoirs according to the core analysis of porosity, permeability and rock density, et al.
2. Lithology identification of volcanic rocks:the determined mineral composition matrix in the logging interpretation model
Lithology identification is the basis and difficulty of the volcanic reservoirs research. To identify the lithology of volcanic rocks through logging data, we have to establish a response relationship between logging data and geologic lithology naming data. As a result, the first thing we need to consider is how the lithology of the volcanic rocks classify in geology. Only when the lithology types and the geological classification principles are associated appropriately, accurate logging identification results can be obtained. Geologically speaking, there are four categories of volcanic rocks which are classified according to the chemical composition of the rocks. They are basic rocks, intermediate rocks, intermediate-acid rocks and acid rocks. According to the structures of volcanic rocks, however, there are three categories:volcanic lava, pyroclastic lava and pyroclastic rocks. The exact types of the volcanic rocks can be determined by comprehensively considering the information of mineral composition and structures.
1) Lithology identification according to the chemical composition of four types volcanic rocks.
For the information of volcanic well section from more than 30 wells in Songliao basin which had exact naming data of core slices, we made each depth point with naming data as a sampling point (totally 430 points). Six conventional logging data were read, such as GR, TH, U, K, PE and NPHI. These data are used as the basis to discuss the lithology identification of volcanic rocks.
a) Cross plot method using two groups of conventional logging data:for the 430 sampling points, the cross plot of GR-TH had the best identification result which had a numerate correct rate of 93.72%.
b) Statistical method using multiple groups of conventional logging data.
Cluster analysis:for the 430 sampling points, case cluster (i.e. q-type cluster) was adopted, and K-means cluster analysis with given number of categories was selected. When the iteration number was 20, the numerate correct rate was less than 50%.
Discriminant analysis:for the 430 sampling points,90.2% of numerate correct rate was obtained with Fisher discriminant analysis method.
Principal component analysis:the numerate correct rate was 78.42%.
Factor analysis:the numerate correct rate was 77.95%.
Correspondence analysis:the numerate correct rate was 65.82%.
c) Establishment of pattern identification method for exact sample by using multiple conventional logging data.
BP neural network method:with 60 input nodes,5 output nodes,100 middle layer nodes,0.01 of learning step and 10000 times of training, the numerate correct rate was 64.42%.
SOM neural network method:standardized by mean square deviation, with 2000 of iteration number and 10*10 of output layer,95.35% of numerate correct rate was obtained.
Support Vector Machine method:using radial basis function as kernel function, when C=20 andγ=23, the numerate correct rate was 98.84%.
d) Pattern identification of the data processed after statistical analysis.
The principal component analysis, factor analysis and correspondence analysis of statistical analysis method can well extract a few independent composite indicators of the sample from multiple conventional logging data. Thus, the correlation between inputs can be eliminated, and the input dimension can be reduced. With a simplified network structure, the network convergence can be speed up, too. As a result, the neural network method and support vector machine method that based on the principal component analysis, factor analysis and correspondence analysis, can all obtained higher correct rate.
e) Selection of the final result.
The methods which had numerate correct rate up to 80% were selected, and the final result was obtained using voting method. In the case that the lithology types obtained by various methods were different with each other, the result obtained by Support Vector Machine method which had the highest correct identification rate was provided as the final identification result.
2) Lithology identification according to the structures of three main types volcanic rocks.
a) Cross plot method using conventional logging data.
Through analyzing the cross plot, we found that the logging curves which were sensitive to the rock structure included NPHI, AC and RT.
b) Wavelet transform method for the conventional logging data.
One-dimensional continuous wavelet transform method was mainly adopted. The transform result of dbl wavelet, bior wavelet, Haar wavelet on the resistivity curves, and the transform result of dbl wavelet and Haar wavelet on the acoustic time curves, as well as the transform result of Haar wavelet on neutron porosity curves, all have indication effect on the structure classification of volcanic rocks.
c) Fractal dimension method for the conventional logging data.
The ranges of response values of resistivity curves, acoustic time curves and neutron curves after fractal dimension calculation was reviewed and used to identify the type of volcanic rocks with different structures.
d) Identification method using image logging images.
FMI image identification mode was established in term of the volcanic rocks categories which were classified according to the rock structures, and it was applied to identify volcanic rocks with different structures.
e) Selection of the final result.
Based on the result of FMI image identification mode and consulting the results of fractal dimension method and wavelet transform method, the final result was determined.
3) Final determination of the exact types of volcanic rocks by comprehensively considering the information of mineral composition and characteristic structures (see Table 4-1-1).
3. Study of obtain fracture and vug porosity
The calculation of fracture and vug porosity is conducted by studying the data of image logging.
1) Using the human-computer interactive interpretation function of software to calculate fracture and vug porosity based on the data of image logging.
We have analyzed many FMI image modes of various types of fractures in the deep volcanic strata of Songliao basin. Using LogView, the image logging data was decoded and mapped. The interactive interpretation function of this software provides a platform for the identification of fractures. Besides, the interactive interpretation parameter calculation function of the software could evaluate the fractures, and provide hydraulic electrical fracture aperture (FVAH), areal trace length (FVTL), corrected fracture density (FVDC) and apparent electrical fracture porosity (FVPA).
2) Digital image processing method based on the image logging images.
By studying the FMI image information from image logging, fractures and caves were emerged from the basic rocks using computer image processing method, i.e. image enhancement technology, edge detection and image segmentation. Then, using Hough transformation, contour extraction and tracking technology, the fractures and vugs are extracted and recognized. Finally, the quantitative parameters of fractures and caves were calculated with image analysis techniques according to some algorithm. This effectively avoided the error caused by subjective factors during human-computer interactive interpretation process.
3) Evaluation method for fracture porosity based on the sampling data of image logging.
For the volcanic strata with image logging data, we can directly study the sample data matrix of image logging to identify and evaluate the fracture development. Threshold values can be chosen with appropriate methods to distinguish whether the resistivity of each point in the matrix is low resistivity response (caused by filling the pores with mud) or high impedance response (caused by dense rock and such kind of materials). Then adjacent vertex search model is created to search for low resistivity response range. Thus the fracture development can be evaluated.
4) With the above results, using the weighted average method to determine the final value of fracture and vug porosity.
4. Establishment of logging interpretation model
The volcanic rocks containing clay minerals can be seen as the transition of volcanic and metamorphic rocks, and are not within the scope our study which focuses on the typical volcanic rocks of Songliao basin. Thus, the established logging interpretation model contains two parts, i.e. matrixs and fractures of the rocks. The rock matrix established through lithologic identification theories and methods is called determined rock matrix, and the resultant logging interpretation model is determined matrix volume model. Density logging, neutron logging, or the combination technique of these two methods can be used to calculate the total porosity. As a result, the matrix porosity can be obtained by calculating the difference between total porosity and fracture porosity.
5. Comprehensive reservoir evaluation
Based on the calculation of porosity, permeability was calculated according to appropriate formula. Then water saturation was calculated according to water saturation equation. Finally, under the guidance of the regional geological data, comprehensive evaluation to the volcanic reservoirs was given based on the above logging evaluation results.
本论文基于上述背景,针对火山岩储层测井解释所面临的难题,建立了一套火山岩储层测井识别与评价的理论和技术。将测井解释模型的建立作为根本出发点,即建立一个针对火山岩地层由岩石骨架和孔隙两部分组成的体积模型。为准确求取体积模型中两部分的各种参数而提出了一些新的见解和具有创新性的处理方法。面对岩性多样且组分变化大的火山岩储层,通过统计分析方法及模式识别方法对测井数据的处理,使利用测井资料所识别的岩性类型能够媲美地质定名的丰富和精准；火山岩储层复杂的储集空间和渗流通道造成的其储层非均质性明显,通过对成像测井图像的数字图像处理和成像测井采样数据矩阵的处理能够有效地获得各孔隙度数值。在此基础上对后续储层的渗透率和饱和度等参数进行准确求取,从而实现了针对火山岩储层广泛通用的测井评价理论和数据处理方法。
As numbers of huge volcanic reservoirs have been found all over the world since 19th century 20 years, the enormous potential for exploration and development of volcanic reservoirs has aroused much attention and interest of the researchers around the world. At present,volcanic reservoir is becoming a research focus in the area of oil and gas exploration.
Because of the variety of lithology and the complexity of reservoir space, as well as the poor physical properties, volcanic reservoir has more complex rock-electricity relation compared with clastic sandstone and carbonate rocks, and this brings great difficulties about the logging interpretation on volcanic reservoir. The main difficulties are:1) improving the coincidence rate of lithology identification when dealing with the volcanic reservoirs with complex lithology and components; 2) immature theory and methods for accurate identification and quantitative evaluation of fractures and vugs, which are the storage space and channel of fluid for volcanic reservoir; 3) the unreasonable interpretation model for volcanic reservoir, because in the past rocks were directly used as component to establish volume model, rather than mineral volume model, thus affecting the calculation accuracy of reservoir porosity, permeability, saturation and some other parameters.
The identification of volcanic reservoir's lithology, quantitative evaluation of fractures and building the logging interpretation model are important supplement to the reservoir evaluation after clastic rocks and carbonate rocks. As the evolvement to the traditional logging data processing methods and interpretation theory, the logging evaluation techniques and interpretation methods for volcanic reservoir have significant theory meaning, and will promote the exploration and development of various volcanic reservoirs in our country.
As the existing logging evaluation techniques and interpretation methods have a series of problems and difficulties in theory and data processing, systematic research has to be carried out to establish appropriate evaluation theory and interpretation method, so that we could make a breakthrough in the logging evaluation of volcanic reservoir. According to the existing problems and difficulties in logging evaluation on volcanic reservoirs, we systematically studied the theories and techniques for volcanic reservoir identification and evaluation. Based on the establishment of the logging interpretation model, appropriate evaluation theory and data processing method were established to obtain each parameter of the model. We strived to establish a general evaluation theory and data processing method for volcanic reservoirs.
1. Summary of volcanic rocks logging response characteristics
Based on the domestic and foreign literatures as well as our years of research on the deep volcanic reservoir in Songliao basin (China), we systematically reviewed the logging response characteristics and response rules of volcanic strata.
1) In detail, we reviewed the logging response characteristics of the typical volcanic rocks in Songliao basin, including a variety of conventional features of logging curves and the image response characteristics of formation image logging. Given the specific range of logging response parameters of volcanic rocks in Songliao basin, general rules for the response characteristics of volcanic rocks was concluded.
2) We also discussed the general characteristics of volcanic reservoirs according to the core analysis of porosity, permeability and rock density, et al.
2. Lithology identification of volcanic rocks:the determined mineral composition matrix in the logging interpretation model
Lithology identification is the basis and difficulty of the volcanic reservoirs research. To identify the lithology of volcanic rocks through logging data, we have to establish a response relationship between logging data and geologic lithology naming data. As a result, the first thing we need to consider is how the lithology of the volcanic rocks classify in geology. Only when the lithology types and the geological classification principles are associated appropriately, accurate logging identification results can be obtained. Geologically speaking, there are four categories of volcanic rocks which are classified according to the chemical composition of the rocks. They are basic rocks, intermediate rocks, intermediate-acid rocks and acid rocks. According to the structures of volcanic rocks, however, there are three categories:volcanic lava, pyroclastic lava and pyroclastic rocks. The exact types of the volcanic rocks can be determined by comprehensively considering the information of mineral composition and structures.
1) Lithology identification according to the chemical composition of four types volcanic rocks.
For the information of volcanic well section from more than 30 wells in Songliao basin which had exact naming data of core slices, we made each depth point with naming data as a sampling point (totally 430 points). Six conventional logging data were read, such as GR, TH, U, K, PE and NPHI. These data are used as the basis to discuss the lithology identification of volcanic rocks.
a) Cross plot method using two groups of conventional logging data:for the 430 sampling points, the cross plot of GR-TH had the best identification result which had a numerate correct rate of 93.72%.
b) Statistical method using multiple groups of conventional logging data.
Cluster analysis:for the 430 sampling points, case cluster (i.e. q-type cluster) was adopted, and K-means cluster analysis with given number of categories was selected. When the iteration number was 20, the numerate correct rate was less than 50%.
Discriminant analysis:for the 430 sampling points,90.2% of numerate correct rate was obtained with Fisher discriminant analysis method.
Principal component analysis:the numerate correct rate was 78.42%.
Factor analysis:the numerate correct rate was 77.95%.
Correspondence analysis:the numerate correct rate was 65.82%.
c) Establishment of pattern identification method for exact sample by using multiple conventional logging data.
BP neural network method:with 60 input nodes,5 output nodes,100 middle layer nodes,0.01 of learning step and 10000 times of training, the numerate correct rate was 64.42%.
SOM neural network method:standardized by mean square deviation, with 2000 of iteration number and 10*10 of output layer,95.35% of numerate correct rate was obtained.
Support Vector Machine method:using radial basis function as kernel function, when C=20 andγ=23, the numerate correct rate was 98.84%.
d) Pattern identification of the data processed after statistical analysis.
The principal component analysis, factor analysis and correspondence analysis of statistical analysis method can well extract a few independent composite indicators of the sample from multiple conventional logging data. Thus, the correlation between inputs can be eliminated, and the input dimension can be reduced. With a simplified network structure, the network convergence can be speed up, too. As a result, the neural network method and support vector machine method that based on the principal component analysis, factor analysis and correspondence analysis, can all obtained higher correct rate.
e) Selection of the final result.
The methods which had numerate correct rate up to 80% were selected, and the final result was obtained using voting method. In the case that the lithology types obtained by various methods were different with each other, the result obtained by Support Vector Machine method which had the highest correct identification rate was provided as the final identification result.
2) Lithology identification according to the structures of three main types volcanic rocks.
a) Cross plot method using conventional logging data.
Through analyzing the cross plot, we found that the logging curves which were sensitive to the rock structure included NPHI, AC and RT.
b) Wavelet transform method for the conventional logging data.
One-dimensional continuous wavelet transform method was mainly adopted. The transform result of dbl wavelet, bior wavelet, Haar wavelet on the resistivity curves, and the transform result of dbl wavelet and Haar wavelet on the acoustic time curves, as well as the transform result of Haar wavelet on neutron porosity curves, all have indication effect on the structure classification of volcanic rocks.
c) Fractal dimension method for the conventional logging data.
The ranges of response values of resistivity curves, acoustic time curves and neutron curves after fractal dimension calculation was reviewed and used to identify the type of volcanic rocks with different structures.
d) Identification method using image logging images.
FMI image identification mode was established in term of the volcanic rocks categories which were classified according to the rock structures, and it was applied to identify volcanic rocks with different structures.
e) Selection of the final result.
Based on the result of FMI image identification mode and consulting the results of fractal dimension method and wavelet transform method, the final result was determined.
3) Final determination of the exact types of volcanic rocks by comprehensively considering the information of mineral composition and characteristic structures (see Table 4-1-1).
3. Study of obtain fracture and vug porosity
The calculation of fracture and vug porosity is conducted by studying the data of image logging.
1) Using the human-computer interactive interpretation function of software to calculate fracture and vug porosity based on the data of image logging.
We have analyzed many FMI image modes of various types of fractures in the deep volcanic strata of Songliao basin. Using LogView, the image logging data was decoded and mapped. The interactive interpretation function of this software provides a platform for the identification of fractures. Besides, the interactive interpretation parameter calculation function of the software could evaluate the fractures, and provide hydraulic electrical fracture aperture (FVAH), areal trace length (FVTL), corrected fracture density (FVDC) and apparent electrical fracture porosity (FVPA).
2) Digital image processing method based on the image logging images.
By studying the FMI image information from image logging, fractures and caves were emerged from the basic rocks using computer image processing method, i.e. image enhancement technology, edge detection and image segmentation. Then, using Hough transformation, contour extraction and tracking technology, the fractures and vugs are extracted and recognized. Finally, the quantitative parameters of fractures and caves were calculated with image analysis techniques according to some algorithm. This effectively avoided the error caused by subjective factors during human-computer interactive interpretation process.
3) Evaluation method for fracture porosity based on the sampling data of image logging.
For the volcanic strata with image logging data, we can directly study the sample data matrix of image logging to identify and evaluate the fracture development. Threshold values can be chosen with appropriate methods to distinguish whether the resistivity of each point in the matrix is low resistivity response (caused by filling the pores with mud) or high impedance response (caused by dense rock and such kind of materials). Then adjacent vertex search model is created to search for low resistivity response range. Thus the fracture development can be evaluated.
4) With the above results, using the weighted average method to determine the final value of fracture and vug porosity.
4. Establishment of logging interpretation model
The volcanic rocks containing clay minerals can be seen as the transition of volcanic and metamorphic rocks, and are not within the scope our study which focuses on the typical volcanic rocks of Songliao basin. Thus, the established logging interpretation model contains two parts, i.e. matrixs and fractures of the rocks. The rock matrix established through lithologic identification theories and methods is called determined rock matrix, and the resultant logging interpretation model is determined matrix volume model. Density logging, neutron logging, or the combination technique of these two methods can be used to calculate the total porosity. As a result, the matrix porosity can be obtained by calculating the difference between total porosity and fracture porosity.
5. Comprehensive reservoir evaluation
Based on the calculation of porosity, permeability was calculated according to appropriate formula. Then water saturation was calculated according to water saturation equation. Finally, under the guidance of the regional geological data, comprehensive evaluation to the volcanic reservoirs was given based on the above logging evaluation results.
引文
[1]李思田.联合古陆演化周期中超大型含煤及含油气盆地的形成[J].地学前缘,1997,4(3-4)：299-304
[2]金强.裂谷盆地生油层中火山岩及其矿物与有机质的相互作用[J].地球科学进展,1998,13(6)：542-545
[3]伊培荣,彭峰,韩芸.国外火山岩油气藏特征及其勘探方法[J].特种油气藏,1998,5(2)：65-70
[4]郭占谦.火山作用与油气田的形成和分布[J].新疆石油地质,2002,23(3)：183-185
[5]吕炳全,王红罡,陈振岩.前第三系是中国东部油气勘探的新领域[J].特种油气藏,2003,10(2)：10-13
[6]牛嘉玉,张映红,袁选俊等.中国东部中、新生代火成岩石油地质研究、油气勘探前景及面临问题[J].特种油气藏,2003,10(1)：7-12
[7]罗静兰,邵红梅,张成立.火山岩油气藏研究方法与勘探技术综述[J].石油学报,2003,24(1)：31-37
[8]吴昌志,顾连兴,任作伟等.中国东部中、新生代含油气盆地火成岩油气藏成藏机制[J].地质学报,2005,79(4)：522-528
[9]贾承造,赵文智,邹才能等.岩性地层油气藏地质理论与勘探技术[J].石油勘探与开发,2007,34(3)：257-272
[10]邹才能,赵文智,贾承造等.中国沉积盆地火山岩油气藏形成与分布[J].石油勘探与开发,2008,35(3)：237-271
[11]张玉广,刘永建,霍进杰等.中国火山岩油气资源现状及前景预测[J].资源与产业,2009,11(3)：23-25
[12]Nelson P. H., Glenn W. E. Influence of bound water on the neutron log in mineralized igneous rocks. Kerherve J. An introduction to volcanic reservoirs and to their evaluation from wireline electrical measurements. Note for a seminar,1977.
[13]Rigby.A F. Fracture identification in an igneous geothermal reservo}r. Surprise Valley. Ca上ifornia, SPWLA,21st annual logging symposium,1980
[14]Sanyal S. K., Juprasert S., Jubasche J., An evaluation of rhyolite-basalt-volcanic ash sequence from welllogs[J]. The log analyst, 1980,, Jan-Feb.
[15]Khatchikan A. Log evaluation of oil-bearing igneous rocks. SPWLA,23rd annual logging symposium transactions,1982
[16]Serra.0. Formation Micro Scanner image interpretation:Schlumberger Educational Service,1989. Houston. SMP-7028,117p
[17]C. Calle. Neutron porosity logging and core porosity measurements in the beauvior granite, Massif Central Range France. Applied Geophysics,1994,32
[18]欧阳健,王运位,胡淑芬.复杂岩性最优化测井数字处理方法[J].石油与天然气地质,1983,4(1)：76-87
[19]尚林阁,潘保芝.应用模糊数学统计识别花岗岩古潜山裂缝的方法与效果[J].长春地质学院学报,1986,4：81-84
[20]卞德智,邱子刚,刘明高.模糊数学识别火成岩岩性的方法及地质效果——测井资料的地质应用[M].北京：石油工业出版社,1991
[21]范宜仁,黄隆基,代诗华.交会图技术在火山岩岩性与裂缝识别中的应用[J].测井技术,1999,23(1)：53-56
[22]符翔,高振中.FMI测井在地质方面的应用[J].测井技术,1998,22(6)：435-438
[23]刘呈冰,史占国,李俊国等.全面评价低孔裂缝／孔洞型碳酸盐岩及火成岩储层[J].测井技术,1999；23(6)：457-465
[24]吴文圣.地层微电阻率成像测井的地质应用[J].中国海上油气(地质),2000,14(6)：438-441
[25]陈本才,马俊芳,高亚文.地层微电阻率扫描成像测井沉积学分析及储层评价[J].西部探矿工程,2004,12：93-95
[26]赵舒.微电阻率成像测井资料在塔河油田缝洞型储层综合评价中的应用[J].石油物探,.2005,44(5)：509-512
[27]张莹,潘保芝,印长海等.成像测井图像在火山岩岩性识别中的应用[J].石油物探,2007,46(3)：288-293
[28]麻伟明,王艳,姚少军等.应用核磁共振T2谱划分火成岩次生孔隙储层[J].国外测井技术,2009,172：19-23
[29]Sanyal S K, Juprasert S, Jusbasche M. An evaluation of rhyolite-basalt-volcanic ash sequence from well logs[R]. Tulsa, Oklahoma:SPWLA 20th Annual Logging Symposium,1979.
[30]Benoit W R, Sethi D K, Fertl W H. Geotherm al well log analysis at Desert Peak, Neaada[R]. Lafayette, Indiana:SPWLA 21th Annual Logging Symposium,1980.
[31]Khatchikian A. Log evaluation of oil-bearing igneous rocks[R]. Corpus Christ, Texas:SPWLA 23th Annual Logging Symposium,1982.
[32]黄隆基,范宜仁.火山岩测井评价的地质和地球物理基础[J].测井技术,1997,21(5)：341-344
[33]朱爱丽,吴伯福,武江.火成岩的测井评价一大港油田枣北地区应用实例[J].测井技术, 1997,21(5)：345-350
[34]高秋涛,黄思赵,时新芹.用FMI测井研究砾岩、火山岩储层[J].测井技术,1998,22增刊：56-59
[35]陈建文,魏斌,李长山等.火山岩岩性的测井识别[J].地学前缘,2000,7(4)：458
[36]黄布宙,潘保芝.松辽盆地北部深层火成岩测井响应特征及岩性划分[J].石油地质,2001,40(3)：42-47
[37]刘为付,孙立新,刘双龙等.模糊数学识别火山岩岩性[J].特种油气藏,2002,9(1)：14-17
[38]丁秀春.测井响应在火成岩储层研究中的应用[J].特种油气藏,2003,10(1)：69-72
[39]赵建,高福红.测井资料交会图法在火山岩岩性识别中的应用[J].世界地质,2003,22(2)：136-140
[40]杨申谷,吴红珍.大洼油田中、新生界火山岩的测井识别方法[J].江汉石油学院学报,2003,25(2)：58-59
[41]魏斌,陈文建,李长山等.徐家围子断陷火山岩岩性的测井识别技术[J].特种油气藏,2003,10(1)：73-75
[42]潘保芝,闫桂京,吴海波.对应分析确定松辽盆地北部深层火成岩岩性[J].大庆石油地质与开发,2003,22(1)：7-9
[43]王薇,李红娟,杨学峰.徐深气田火山岩储层岩性与流体性质测井识别研究[J].国外测井,2006,21(1)：6-9
[44]陈钢花,吴文圣,毛克文.利用地层微电阻率扫描图像识别岩性[J].石油勘探与开发,2001,28(2)：53-55
[45]张剑,谯晓容,曹云安等.火山岩储集层岩性识别的研究及应用[J].中外能源,2006,11：46-50
[46]王拥军,冉启全,童敏等.ECS测井在火山岩岩性识别中的应用[J].国外测井技术,
2006,21(1)：13-16
[47]张莹,潘保芝,印长海等.成像测井图像在火山岩岩性识别中的应用[J].石油物探,2007,46(3)：288-293
[48]王飞,鲁明文,常银辉.利用地球化学测井资料识别火山岩岩性[J].大庆石油地质与开发,2008,27(5)
[49]席道瑛,张涛.神经网络模型在测井岩性识别中的应用[J].煤田地质与勘探,1994,22(6)：56-61
[50]夏宏泉,刘红岐.BP神经网络在测井资料标准化中的应用[J].测井技术,1996,20(3)：201-206
[51]邹长春,严成信,李学文.神经网络在枣北地区火成岩储层测井解释中的应用[J].石 油地球物理勘探,1997,32(增刊2)：27-33
[52]张洪,邹乐君,沈晓华.BP神经网络在测井岩性识别中的应用[J].地质与勘探,2002,38(6)：63-65
[53]刘为付,孙立新,刘双龙,等.模糊数学识别火山岩岩性[J].特种油气藏,2002,9(1)：14-17
[54]潘保芝,薛林福,李舟波,等.裂缝性火成岩储层测井评价的理论与方法研究[M].北京：石油工业出版社,2003
[55]张世晖,刘天佑,顾汉明.用人工神经网络识别火成岩[J].石油地球物理勘探,2003,38(增刊)：84-87
[56]张平,潘保芝,张莹等.自组织神经网络在火成岩岩性识别中的应用.石油物探.2009,48(1)：53-56
[57]Rigby F A. Fracture identification in an igneous geothermal reservoir[R]. Lafayette, Indiana:SPWLA 21th Annual Logging Symposium,1980.
[58]Koshlyak V. A., T. L. Dong, P. D. Hai.. Characteristics of fractured reservoirs in magmatic rocks and their reservoir properties[A]. Asia Pacific oil and gas conference and exhibition[C], Brisbane, Australia,16-18 October 2000, SPE-64464:1-11
[59]阎新民.应用计算机进行准噶尔盆地火山岩裂缝识别[J].石油地球物理勘探,1994,29(增刊2)：139-143
[60]罗静兰,曲志浩,孙卫等.风化店火山岩岩相、储集性与油气的关系[J].石油学报,1996,17(1)：32-39
[61]丁中一,钱祥麟,霍红.构造裂缝定量预测的一种新方法——二元法[J].石油与天然气地质,1998,19(1)：1-7
[62]刘呈冰,史占国,李俊国等.全面评价低孔裂缝—孔洞型碳酸盐岩及火成岩储层[J].测井技术,1999,23(6)：457-465
[63]陈忠,张吉昌,罗玉庆等.有限元数值模拟在构造裂缝定量预测中的应用[J].特种油气藏,2001,8(1)：64-67
[64]王全柱.火成岩储层研究[J].西安石油大学学报(自然科学版),2004,19(2)：13-16
[65]曹毅民,章成广,杨维英等.裂缝性储层电成像测井孔隙度定量评价方法研究[J].测井技术,2006,30(3)：237-239
[66]李善军,肖承文,汪涵明等.裂缝的双侧向测井响应的数学模型及裂缝孔隙度的定量解释[J].地球物理学报,1996,39(6)：845-852
[67]戴诗华,罗兴平,王军等.火山岩储集层测井响应与解释方法[J].新疆石油地质,1998,19(6)：465-469
[68]邓攀,陈孟晋,高哲荣.火山岩储层构造裂缝的测井识别及解释[J].石油学报,2002, 23(6)：32-36
[69]欧阳健.用最优化方法研究含油火成岩的测井解释[J].测井技术,1985,19(1)：1-16
[70]PAN Baozhi, XUE Linfu, HUANG Buzhou, et al. Evaluation of volcanic reservoirs with the "QAPM mineral model" using a genetic algorithm [J]. APPLIED GEOPIIYSICS, 2008,5(1):1-8
[71]Chayes, F. Distribution of basalt,, basanite, andesite and dacite in a normative equivalent of the QAPF double triangle[J]. Chemical Geology,1981,33(1-2):127-140
[72]LeBas, M.J., LeMaitre, R.W., Sreckeisen, A. and Zanettin, B. A chemical classification of volcanic rocks based on total alkali-silica diagram. Journal of Petrology,1986,27:745-750.
[73]Winchester, J. A., Geochemical discrimination of magma series using immobile elements[J]. Chemical Geology,1977,20:325-343.
[74]LeMaitre, R.W., Bateman, P., Dudek, A. et al. A classification of igneous rocks and glossary of terms[M]. London:Blackwell,1989
[75]邱家骧.国际地科联火成岩分类学分委会推荐的火山岩分类简介[J].现代地质,1991,5(4)：457-468
[76]林景仟.火成岩岩类学与岩理学[M].北京：地质出版社,1995
[77]中国地质学会岩石专业委员会火山岩分类命名小组.火山碎屑岩的分类和命名(国内推荐方案)[J].岩石矿物及测试,1984a,3(4)：301-309
[78]中国地质学会岩石专业委员会火山岩分类命名小组.火山岩的分类和命名(熔岩部分)(国内推荐方案)[J].岩石矿物及测试,1984b,3(4)：289-300
[79]李兆鼐,王碧香,王富宝,等.火山岩(熔岩)的分类和命名[J].中国地质科学院院报,1989,(9)：175-194
[80]王德滋,周新民.火山岩岩石学[M].北京：科学出版社,1982
[81]王璞瑶,郑常青,舒萍等.松辽盆地深层火山岩岩性分类方案[J].大庆石油地质与开发,2007,26(4)：17-22
[82]郭军,陈守田,王维林等.松辽盆地徐家围子断陷火山岩岩石化学特征及其构造环境[J].长春科技大学学报,2000,30(3)：243-245
[83]闫全人,高山林,王宗起等.松辽盆地火山岩的同位素年代、地球化学特征及意义[J].地球化学,2002,31(2)：168-177
[84]谢意红,胡双怀,路凤香等.吉林平岗-渭津中性中基性火山岩特征及成因[J].吉林大学学报(地球科学版),2004,34(3)：344-350
[85]周波,李舟波,潘保芝.火山岩岩性识别方法研究[J].吉林大学学报,2005,35(3)：395-397
[86]邵红梅,毛庆云,姜洪启等.徐家围子断陷营城组火山岩气藏储层特征[J].天然气工业, 2006,26(6)：29-32
[87]徐正顺,王渝明,庞彦明等.大庆徐深气田火山岩气藏储集层识别与评价[J].石油勘探与开发,2006,33(5)：521-531
[88]王明健,国景星,盛世锋等.商741区沙一段火山岩测井识别[J].断块油气田,2007,14(6)：31-34
[89]杨申谷,刘笑翠,胡志华等.储层分析中火山岩岩性的测井识别[J].石油天然气学报,2007,29(6)：33-37
[90]张美玲,邵阳,高柏原等.海拉尔盆地含火山岩地层主要岩性分布及测井响应分析[J].中国石油勘探,2009,(2)：50-54
[91]张红兵,贾来喜,李潞.SPSS宝典[M].北京：电子工业出版社,2008
[92]张家政,关泉生,谈继强等.Fisher判别在红嘴油田火山岩岩性识别中的应用[J].新疆石油地质,2008,29(6)：761-764
[93]张莹,潘保芝等.基于主成分分析的SOM神经网络在火山岩岩性识别中的应用[J].测井技术.2009,33(6)：550-554
[94]熊婷燕.主成分分析与R型因子分析的异同比较[J].统计与决策,2006,下：129-132
[95]林海明,张文霖.主成分分析与因子分析的异同和SPSS软件[J].统计研究,2005,(3)：65-68
[96]孙道志.统计分析中对应分析方法应用[J].黄山学院学报,2006,8(3)：13-16
[97]徐佩华.基于人工神经网络方法的锦屏一级水电站枢纽区高边坡稳定性分区研究[D].长春,吉林大学建设工程学院,2006
[98]佩特罗夫斯基AD.地下无线电波法[M].陆克,蔡刚民,译.北京：地质出版社,1971
[99]HARRISON W H, MAZZA R L, RUBINL A. Air-drilling, electro2 magnetic MWD system development[J]. SPE?IADC19970,1990:485-495.
[100]张莹,潘保芝,黄布宙等.测井资料小波变换在高分辨率层序地层划分中的应用[J].物探化探与计算技术,2008,30(1)：58-62
[101]辛厚文.1993.分形理论及其应用.中国科学技术大学出版社.
[102]D.L.特科特.分形与混沌-在地质学和地球物理学中的应用.地震出版社,1993.
[103]潘保芝,薛林福.分数维及其在测井地质解释中的应用[J].测井技术,1992,(3)：214-221.
[104]石雨田,潘保芝.分维的应用一定量描述裂缝发育程度[J].物探与化探,2000,24(6)：426-430.
[105]刘阳.松辽盆地北部徐家围子断陷营城组一段火山岩储层评价[D].大庆,大庆石油学院,2009
[106]卢继平.徐家围子营城组火山岩储层成岩作用及孔隙演化[D].大庆,大庆石油学 院,2009
[107]李雪英.成像测井资料的裂缝自动识别方法研究[D].大庆,大庆石油学院,2003
[108]王满.基于FMI的火成岩组构分析[D].吉林,吉林大学地球科学学院,2007
[109][美]]3en Ezzell编著,傅祖芸等译.Windows NT 3.1图形编程技术.北京.电子工业出版社,1994
[110][美]]David Kay, John Levine.图形图象文件格式大全.北京.电子工业出版社,1994
[111]康文炜.基于图像确定空间坐标方法的研究[D].长春,吉林大学,2004
[112]青莉.接触导线几何参数测量研究[D].唐山,西南交通大学,2005
[113]彭瑾.基于数字图像处理的车型检测[D].合肥,合肥工业大学,2008
[114]王青.裂缝性储集层测井解释方法研究[D].北京,中国地质大学,2002
[115]吴海波.松辽盆地北部深层火成岩储层裂缝特征及测井评价[D].长春.吉林大学,2004
[2]金强.裂谷盆地生油层中火山岩及其矿物与有机质的相互作用[J].地球科学进展,1998,13(6)：542-545
[3]伊培荣,彭峰,韩芸.国外火山岩油气藏特征及其勘探方法[J].特种油气藏,1998,5(2)：65-70
[4]郭占谦.火山作用与油气田的形成和分布[J].新疆石油地质,2002,23(3)：183-185
[5]吕炳全,王红罡,陈振岩.前第三系是中国东部油气勘探的新领域[J].特种油气藏,2003,10(2)：10-13
[6]牛嘉玉,张映红,袁选俊等.中国东部中、新生代火成岩石油地质研究、油气勘探前景及面临问题[J].特种油气藏,2003,10(1)：7-12
[7]罗静兰,邵红梅,张成立.火山岩油气藏研究方法与勘探技术综述[J].石油学报,2003,24(1)：31-37
[8]吴昌志,顾连兴,任作伟等.中国东部中、新生代含油气盆地火成岩油气藏成藏机制[J].地质学报,2005,79(4)：522-528
[9]贾承造,赵文智,邹才能等.岩性地层油气藏地质理论与勘探技术[J].石油勘探与开发,2007,34(3)：257-272
[10]邹才能,赵文智,贾承造等.中国沉积盆地火山岩油气藏形成与分布[J].石油勘探与开发,2008,35(3)：237-271
[11]张玉广,刘永建,霍进杰等.中国火山岩油气资源现状及前景预测[J].资源与产业,2009,11(3)：23-25
[12]Nelson P. H., Glenn W. E. Influence of bound water on the neutron log in mineralized igneous rocks. Kerherve J. An introduction to volcanic reservoirs and to their evaluation from wireline electrical measurements. Note for a seminar,1977.
[13]Rigby.A F. Fracture identification in an igneous geothermal reservo}r. Surprise Valley. Ca上ifornia, SPWLA,21st annual logging symposium,1980
[14]Sanyal S. K., Juprasert S., Jubasche J., An evaluation of rhyolite-basalt-volcanic ash sequence from welllogs[J]. The log analyst, 1980,, Jan-Feb.
[15]Khatchikan A. Log evaluation of oil-bearing igneous rocks. SPWLA,23rd annual logging symposium transactions,1982
[16]Serra.0. Formation Micro Scanner image interpretation:Schlumberger Educational Service,1989. Houston. SMP-7028,117p
[17]C. Calle. Neutron porosity logging and core porosity measurements in the beauvior granite, Massif Central Range France. Applied Geophysics,1994,32
[18]欧阳健,王运位,胡淑芬.复杂岩性最优化测井数字处理方法[J].石油与天然气地质,1983,4(1)：76-87
[19]尚林阁,潘保芝.应用模糊数学统计识别花岗岩古潜山裂缝的方法与效果[J].长春地质学院学报,1986,4：81-84
[20]卞德智,邱子刚,刘明高.模糊数学识别火成岩岩性的方法及地质效果——测井资料的地质应用[M].北京：石油工业出版社,1991
[21]范宜仁,黄隆基,代诗华.交会图技术在火山岩岩性与裂缝识别中的应用[J].测井技术,1999,23(1)：53-56
[22]符翔,高振中.FMI测井在地质方面的应用[J].测井技术,1998,22(6)：435-438
[23]刘呈冰,史占国,李俊国等.全面评价低孔裂缝／孔洞型碳酸盐岩及火成岩储层[J].测井技术,1999；23(6)：457-465
[24]吴文圣.地层微电阻率成像测井的地质应用[J].中国海上油气(地质),2000,14(6)：438-441
[25]陈本才,马俊芳,高亚文.地层微电阻率扫描成像测井沉积学分析及储层评价[J].西部探矿工程,2004,12：93-95
[26]赵舒.微电阻率成像测井资料在塔河油田缝洞型储层综合评价中的应用[J].石油物探,.2005,44(5)：509-512
[27]张莹,潘保芝,印长海等.成像测井图像在火山岩岩性识别中的应用[J].石油物探,2007,46(3)：288-293
[28]麻伟明,王艳,姚少军等.应用核磁共振T2谱划分火成岩次生孔隙储层[J].国外测井技术,2009,172：19-23
[29]Sanyal S K, Juprasert S, Jusbasche M. An evaluation of rhyolite-basalt-volcanic ash sequence from well logs[R]. Tulsa, Oklahoma:SPWLA 20th Annual Logging Symposium,1979.
[30]Benoit W R, Sethi D K, Fertl W H. Geotherm al well log analysis at Desert Peak, Neaada[R]. Lafayette, Indiana:SPWLA 21th Annual Logging Symposium,1980.
[31]Khatchikian A. Log evaluation of oil-bearing igneous rocks[R]. Corpus Christ, Texas:SPWLA 23th Annual Logging Symposium,1982.
[32]黄隆基,范宜仁.火山岩测井评价的地质和地球物理基础[J].测井技术,1997,21(5)：341-344
[33]朱爱丽,吴伯福,武江.火成岩的测井评价一大港油田枣北地区应用实例[J].测井技术, 1997,21(5)：345-350
[34]高秋涛,黄思赵,时新芹.用FMI测井研究砾岩、火山岩储层[J].测井技术,1998,22增刊：56-59
[35]陈建文,魏斌,李长山等.火山岩岩性的测井识别[J].地学前缘,2000,7(4)：458
[36]黄布宙,潘保芝.松辽盆地北部深层火成岩测井响应特征及岩性划分[J].石油地质,2001,40(3)：42-47
[37]刘为付,孙立新,刘双龙等.模糊数学识别火山岩岩性[J].特种油气藏,2002,9(1)：14-17
[38]丁秀春.测井响应在火成岩储层研究中的应用[J].特种油气藏,2003,10(1)：69-72
[39]赵建,高福红.测井资料交会图法在火山岩岩性识别中的应用[J].世界地质,2003,22(2)：136-140
[40]杨申谷,吴红珍.大洼油田中、新生界火山岩的测井识别方法[J].江汉石油学院学报,2003,25(2)：58-59
[41]魏斌,陈文建,李长山等.徐家围子断陷火山岩岩性的测井识别技术[J].特种油气藏,2003,10(1)：73-75
[42]潘保芝,闫桂京,吴海波.对应分析确定松辽盆地北部深层火成岩岩性[J].大庆石油地质与开发,2003,22(1)：7-9
[43]王薇,李红娟,杨学峰.徐深气田火山岩储层岩性与流体性质测井识别研究[J].国外测井,2006,21(1)：6-9
[44]陈钢花,吴文圣,毛克文.利用地层微电阻率扫描图像识别岩性[J].石油勘探与开发,2001,28(2)：53-55
[45]张剑,谯晓容,曹云安等.火山岩储集层岩性识别的研究及应用[J].中外能源,2006,11：46-50
[46]王拥军,冉启全,童敏等.ECS测井在火山岩岩性识别中的应用[J].国外测井技术,
2006,21(1)：13-16
[47]张莹,潘保芝,印长海等.成像测井图像在火山岩岩性识别中的应用[J].石油物探,2007,46(3)：288-293
[48]王飞,鲁明文,常银辉.利用地球化学测井资料识别火山岩岩性[J].大庆石油地质与开发,2008,27(5)
[49]席道瑛,张涛.神经网络模型在测井岩性识别中的应用[J].煤田地质与勘探,1994,22(6)：56-61
[50]夏宏泉,刘红岐.BP神经网络在测井资料标准化中的应用[J].测井技术,1996,20(3)：201-206
[51]邹长春,严成信,李学文.神经网络在枣北地区火成岩储层测井解释中的应用[J].石 油地球物理勘探,1997,32(增刊2)：27-33
[52]张洪,邹乐君,沈晓华.BP神经网络在测井岩性识别中的应用[J].地质与勘探,2002,38(6)：63-65
[53]刘为付,孙立新,刘双龙,等.模糊数学识别火山岩岩性[J].特种油气藏,2002,9(1)：14-17
[54]潘保芝,薛林福,李舟波,等.裂缝性火成岩储层测井评价的理论与方法研究[M].北京：石油工业出版社,2003
[55]张世晖,刘天佑,顾汉明.用人工神经网络识别火成岩[J].石油地球物理勘探,2003,38(增刊)：84-87
[56]张平,潘保芝,张莹等.自组织神经网络在火成岩岩性识别中的应用.石油物探.2009,48(1)：53-56
[57]Rigby F A. Fracture identification in an igneous geothermal reservoir[R]. Lafayette, Indiana:SPWLA 21th Annual Logging Symposium,1980.
[58]Koshlyak V. A., T. L. Dong, P. D. Hai.. Characteristics of fractured reservoirs in magmatic rocks and their reservoir properties[A]. Asia Pacific oil and gas conference and exhibition[C], Brisbane, Australia,16-18 October 2000, SPE-64464:1-11
[59]阎新民.应用计算机进行准噶尔盆地火山岩裂缝识别[J].石油地球物理勘探,1994,29(增刊2)：139-143
[60]罗静兰,曲志浩,孙卫等.风化店火山岩岩相、储集性与油气的关系[J].石油学报,1996,17(1)：32-39
[61]丁中一,钱祥麟,霍红.构造裂缝定量预测的一种新方法——二元法[J].石油与天然气地质,1998,19(1)：1-7
[62]刘呈冰,史占国,李俊国等.全面评价低孔裂缝—孔洞型碳酸盐岩及火成岩储层[J].测井技术,1999,23(6)：457-465
[63]陈忠,张吉昌,罗玉庆等.有限元数值模拟在构造裂缝定量预测中的应用[J].特种油气藏,2001,8(1)：64-67
[64]王全柱.火成岩储层研究[J].西安石油大学学报(自然科学版),2004,19(2)：13-16
[65]曹毅民,章成广,杨维英等.裂缝性储层电成像测井孔隙度定量评价方法研究[J].测井技术,2006,30(3)：237-239
[66]李善军,肖承文,汪涵明等.裂缝的双侧向测井响应的数学模型及裂缝孔隙度的定量解释[J].地球物理学报,1996,39(6)：845-852
[67]戴诗华,罗兴平,王军等.火山岩储集层测井响应与解释方法[J].新疆石油地质,1998,19(6)：465-469
[68]邓攀,陈孟晋,高哲荣.火山岩储层构造裂缝的测井识别及解释[J].石油学报,2002, 23(6)：32-36
[69]欧阳健.用最优化方法研究含油火成岩的测井解释[J].测井技术,1985,19(1)：1-16
[70]PAN Baozhi, XUE Linfu, HUANG Buzhou, et al. Evaluation of volcanic reservoirs with the "QAPM mineral model" using a genetic algorithm [J]. APPLIED GEOPIIYSICS, 2008,5(1):1-8
[71]Chayes, F. Distribution of basalt,, basanite, andesite and dacite in a normative equivalent of the QAPF double triangle[J]. Chemical Geology,1981,33(1-2):127-140
[72]LeBas, M.J., LeMaitre, R.W., Sreckeisen, A. and Zanettin, B. A chemical classification of volcanic rocks based on total alkali-silica diagram. Journal of Petrology,1986,27:745-750.
[73]Winchester, J. A., Geochemical discrimination of magma series using immobile elements[J]. Chemical Geology,1977,20:325-343.
[74]LeMaitre, R.W., Bateman, P., Dudek, A. et al. A classification of igneous rocks and glossary of terms[M]. London:Blackwell,1989
[75]邱家骧.国际地科联火成岩分类学分委会推荐的火山岩分类简介[J].现代地质,1991,5(4)：457-468
[76]林景仟.火成岩岩类学与岩理学[M].北京：地质出版社,1995
[77]中国地质学会岩石专业委员会火山岩分类命名小组.火山碎屑岩的分类和命名(国内推荐方案)[J].岩石矿物及测试,1984a,3(4)：301-309
[78]中国地质学会岩石专业委员会火山岩分类命名小组.火山岩的分类和命名(熔岩部分)(国内推荐方案)[J].岩石矿物及测试,1984b,3(4)：289-300
[79]李兆鼐,王碧香,王富宝,等.火山岩(熔岩)的分类和命名[J].中国地质科学院院报,1989,(9)：175-194
[80]王德滋,周新民.火山岩岩石学[M].北京：科学出版社,1982
[81]王璞瑶,郑常青,舒萍等.松辽盆地深层火山岩岩性分类方案[J].大庆石油地质与开发,2007,26(4)：17-22
[82]郭军,陈守田,王维林等.松辽盆地徐家围子断陷火山岩岩石化学特征及其构造环境[J].长春科技大学学报,2000,30(3)：243-245
[83]闫全人,高山林,王宗起等.松辽盆地火山岩的同位素年代、地球化学特征及意义[J].地球化学,2002,31(2)：168-177
[84]谢意红,胡双怀,路凤香等.吉林平岗-渭津中性中基性火山岩特征及成因[J].吉林大学学报(地球科学版),2004,34(3)：344-350
[85]周波,李舟波,潘保芝.火山岩岩性识别方法研究[J].吉林大学学报,2005,35(3)：395-397
[86]邵红梅,毛庆云,姜洪启等.徐家围子断陷营城组火山岩气藏储层特征[J].天然气工业, 2006,26(6)：29-32
[87]徐正顺,王渝明,庞彦明等.大庆徐深气田火山岩气藏储集层识别与评价[J].石油勘探与开发,2006,33(5)：521-531
[88]王明健,国景星,盛世锋等.商741区沙一段火山岩测井识别[J].断块油气田,2007,14(6)：31-34
[89]杨申谷,刘笑翠,胡志华等.储层分析中火山岩岩性的测井识别[J].石油天然气学报,2007,29(6)：33-37
[90]张美玲,邵阳,高柏原等.海拉尔盆地含火山岩地层主要岩性分布及测井响应分析[J].中国石油勘探,2009,(2)：50-54
[91]张红兵,贾来喜,李潞.SPSS宝典[M].北京：电子工业出版社,2008
[92]张家政,关泉生,谈继强等.Fisher判别在红嘴油田火山岩岩性识别中的应用[J].新疆石油地质,2008,29(6)：761-764
[93]张莹,潘保芝等.基于主成分分析的SOM神经网络在火山岩岩性识别中的应用[J].测井技术.2009,33(6)：550-554
[94]熊婷燕.主成分分析与R型因子分析的异同比较[J].统计与决策,2006,下：129-132
[95]林海明,张文霖.主成分分析与因子分析的异同和SPSS软件[J].统计研究,2005,(3)：65-68
[96]孙道志.统计分析中对应分析方法应用[J].黄山学院学报,2006,8(3)：13-16
[97]徐佩华.基于人工神经网络方法的锦屏一级水电站枢纽区高边坡稳定性分区研究[D].长春,吉林大学建设工程学院,2006
[98]佩特罗夫斯基AD.地下无线电波法[M].陆克,蔡刚民,译.北京：地质出版社,1971
[99]HARRISON W H, MAZZA R L, RUBINL A. Air-drilling, electro2 magnetic MWD system development[J]. SPE?IADC19970,1990:485-495.
[100]张莹,潘保芝,黄布宙等.测井资料小波变换在高分辨率层序地层划分中的应用[J].物探化探与计算技术,2008,30(1)：58-62
[101]辛厚文.1993.分形理论及其应用.中国科学技术大学出版社.
[102]D.L.特科特.分形与混沌-在地质学和地球物理学中的应用.地震出版社,1993.
[103]潘保芝,薛林福.分数维及其在测井地质解释中的应用[J].测井技术,1992,(3)：214-221.
[104]石雨田,潘保芝.分维的应用一定量描述裂缝发育程度[J].物探与化探,2000,24(6)：426-430.
[105]刘阳.松辽盆地北部徐家围子断陷营城组一段火山岩储层评价[D].大庆,大庆石油学院,2009
[106]卢继平.徐家围子营城组火山岩储层成岩作用及孔隙演化[D].大庆,大庆石油学 院,2009
[107]李雪英.成像测井资料的裂缝自动识别方法研究[D].大庆,大庆石油学院,2003
[108]王满.基于FMI的火成岩组构分析[D].吉林,吉林大学地球科学学院,2007
[109][美]]3en Ezzell编著,傅祖芸等译.Windows NT 3.1图形编程技术.北京.电子工业出版社,1994
[110][美]]David Kay, John Levine.图形图象文件格式大全.北京.电子工业出版社,1994
[111]康文炜.基于图像确定空间坐标方法的研究[D].长春,吉林大学,2004
[112]青莉.接触导线几何参数测量研究[D].唐山,西南交通大学,2005
[113]彭瑾.基于数字图像处理的车型检测[D].合肥,合肥工业大学,2008
[114]王青.裂缝性储集层测井解释方法研究[D].北京,中国地质大学,2002
[115]吴海波.松辽盆地北部深层火成岩储层裂缝特征及测井评价[D].长春.吉林大学,2004