火山碎屑岩岩性的测井识别方法研究
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摘要
本文主要是采用贝叶斯判别分析的方法对海拉尔盆地乌尔逊凹陷的大磨拐组、南屯组和铜钵庙组的岩性识别。文中利用地质和录井的资料对海拉尔盆地的岩性的测井响应特征进行了归纳和总结。并且提取了井径好、岩性厚的层段做为判别岩性的样本。用马尔科夫链的转移矩阵做为贝叶斯判别方法的先验概率来确定岩性,以减少人为的因素的影响,并且也能和实际地质情况相结合。用C++编出贝叶斯判别岩性的程序,在结合马尔科夫链的转移概率矩阵来判别未知处的岩性。然后用卡奔软件呈现判别出的岩性的剖面图,用来和地质资料得对比,并统计了符合率。
The Hailar Basin, in northern part of the Inner Mongolia Autonomous Region and to the west side of the Da Hinggan mountains, was a Late Mesozoic-Paleogene basin, with the basement being Hercynides. The boundary faults to control the basin developing of NE- and NNE-strikings created. Only Urxun depression displays SN distributing, it created in Early Cretaceous, and overprinted by later deformations. Urxun depression is controlled by the fault of the west of Urxun, it is a large unsymmetrical fault-depression sag with strike-slip characteristics in Mesozoic-Cenozoic in Hailaer Basin, the area is 2350 km 2, the depression’s pose and develop is mainly controlled by NE and NW fault. The north of the basin is mainly controlled by NNE Urxun west fault, in north the east boundary fault is a little small, but its reverset is distinct, and growth time is early, the middle of sediment leans to a side of Urxun west fault. The geological structure in the south of the Urxun is different from the north of the Urxun, NW fault is its main boundary fault in the west side, is also named Urxun west fault south section, and its reverse is distinct. The fault of the east fault is not grown, and is also a large unsymmetrical fault-depression sag with strike-slip characteristic, we could divide Urxun depression into Urxun north fault, Urxun middle fault, Urxun sorth fault.
and overprinted by later deformations. Urxun-Beier depression in Hailaer basin is the Mesozoic and Cenozoic basin are located in the Paleozoic orogenic belt in Inner Mongolia-Daxing′anling area.they are regarded as p rosperity in exploration.
Bayesian analysis was used to identify the lithology of the Damoguaihe formation, Nantun formation and Tongbomiao formation in Hailaer Basin Urxun sag in the article. The area of Urxun sag is big, and the difference of diagenesis is also big. The main types of rock in the research region are: Sedimentary rocks, volcanic clastic sedimentary rocks, volcanic sedimentary rocks, volcanic rocks and lava. Rocks in the area also use the classification criteria of the sandstone, and tuff, melting guitar-tuff and pyroclastic tuff are subdivided into coarse grain tuff/ignimbrite/bedded tuff、granule tuff/ignimbrite/bedded tuff、silt tuff/ignimbrite/bedded tuff. according to the size of the volcanic rocks particles. Because of the thin layer of the lithology, some borehole wall collapses, these make vertical resolution of most of the logging methods are distorted, and the identification of lithology is difficult.
By way of using the various logging data in being, Bayesian distinguish was used. Bayesian distinguish is a statistical method. It begins the probability distributing property of the variable, through density function estimate to complete the probability of the stylebook belong to one type. It bases on the rule of compartmentalizing m-dimension space, and compartmentalize m-dimension space to G subspaces, the subspaces between each other are not intersectant, afterward we judge which subspace the samples in it according to the rule. So compartmentalizing each m-dimension corresponds a distinguish rule and result. Due to the samples in one subspace also belong to other subspaces, so we may meet error distinguish. By way of reducing this phenomenon, we should make the samples in a subspace centralize this subspace as much as possible, and this subspace includes other samples as little as possible.
In addition, the prior probability of Bayesian distinguish is determined by transferred matrix probability of Markov chain. As the prior probability of Bayesian distinguish knows little, and its foundation of normal mode is related with the method, the man-made factor is in existence, by way of reducing the man-made factor, and integrating with the actual geological information, I use the transferred matrix probability of Markov chain to decide the prior probability of Bayesian distinguish. Markov chain is a diverse geological statistical model. Using it to describe the geological statistical information could reflect the actual distribution of types of geology , its conversional continuity and randomicity. I take the asymmetry of the actual geological type and to make the geological statistical placed the model into account, I use the transferred matrix probability of Markov chain to describe the regional variable.
I use c++ mode to complete the distinguish in the article. For each layer of this borehole, we can use the Bayesian distinguish to identify the lithology of the borehole that we do not get core. The program is the following: firstly we put the lithology model which has been distinguished to the program, then input the lithology samples which need to be distinguished, following we could distinguish the needed lithology after running the program of distinguish, at last, we use kaben softer to display the section chart, this could contrast with the lithology data of geology, and we also compute the according rate that Bayesian distinguish identify the lithology in term of layer, simultaneity we gives the instruction about according rate.
Contrasted with the geological core data we could instruct: using Bayesian analysis to identify the lithology is got by layer in statistical method.Some wells of Bayesian analysis in the eastern of Urxun sag was used to identify the rate which is over 70 percent of lithology.
The Hailar Basin, in northern part of the Inner Mongolia Autonomous Region and to the west side of the Da Hinggan mountains, was a Late Mesozoic-Paleogene basin, with the basement being Hercynides. The boundary faults to control the basin developing of NE- and NNE-strikings created. Only Urxun depression displays SN distributing, it created in Early Cretaceous, and overprinted by later deformations. Urxun depression is controlled by the fault of the west of Urxun, it is a large unsymmetrical fault-depression sag with strike-slip characteristics in Mesozoic-Cenozoic in Hailaer Basin, the area is 2350 km 2, the depression’s pose and develop is mainly controlled by NE and NW fault. The north of the basin is mainly controlled by NNE Urxun west fault, in north the east boundary fault is a little small, but its reverset is distinct, and growth time is early, the middle of sediment leans to a side of Urxun west fault. The geological structure in the south of the Urxun is different from the north of the Urxun, NW fault is its main boundary fault in the west side, is also named Urxun west fault south section, and its reverse is distinct. The fault of the east fault is not grown, and is also a large unsymmetrical fault-depression sag with strike-slip characteristic, we could divide Urxun depression into Urxun north fault, Urxun middle fault, Urxun sorth fault.
and overprinted by later deformations. Urxun-Beier depression in Hailaer basin is the Mesozoic and Cenozoic basin are located in the Paleozoic orogenic belt in Inner Mongolia-Daxing′anling area.they are regarded as p rosperity in exploration.
Bayesian analysis was used to identify the lithology of the Damoguaihe formation, Nantun formation and Tongbomiao formation in Hailaer Basin Urxun sag in the article. The area of Urxun sag is big, and the difference of diagenesis is also big. The main types of rock in the research region are: Sedimentary rocks, volcanic clastic sedimentary rocks, volcanic sedimentary rocks, volcanic rocks and lava. Rocks in the area also use the classification criteria of the sandstone, and tuff, melting guitar-tuff and pyroclastic tuff are subdivided into coarse grain tuff/ignimbrite/bedded tuff、granule tuff/ignimbrite/bedded tuff、silt tuff/ignimbrite/bedded tuff. according to the size of the volcanic rocks particles. Because of the thin layer of the lithology, some borehole wall collapses, these make vertical resolution of most of the logging methods are distorted, and the identification of lithology is difficult.
By way of using the various logging data in being, Bayesian distinguish was used. Bayesian distinguish is a statistical method. It begins the probability distributing property of the variable, through density function estimate to complete the probability of the stylebook belong to one type. It bases on the rule of compartmentalizing m-dimension space, and compartmentalize m-dimension space to G subspaces, the subspaces between each other are not intersectant, afterward we judge which subspace the samples in it according to the rule. So compartmentalizing each m-dimension corresponds a distinguish rule and result. Due to the samples in one subspace also belong to other subspaces, so we may meet error distinguish. By way of reducing this phenomenon, we should make the samples in a subspace centralize this subspace as much as possible, and this subspace includes other samples as little as possible.
In addition, the prior probability of Bayesian distinguish is determined by transferred matrix probability of Markov chain. As the prior probability of Bayesian distinguish knows little, and its foundation of normal mode is related with the method, the man-made factor is in existence, by way of reducing the man-made factor, and integrating with the actual geological information, I use the transferred matrix probability of Markov chain to decide the prior probability of Bayesian distinguish. Markov chain is a diverse geological statistical model. Using it to describe the geological statistical information could reflect the actual distribution of types of geology , its conversional continuity and randomicity. I take the asymmetry of the actual geological type and to make the geological statistical placed the model into account, I use the transferred matrix probability of Markov chain to describe the regional variable.
I use c++ mode to complete the distinguish in the article. For each layer of this borehole, we can use the Bayesian distinguish to identify the lithology of the borehole that we do not get core. The program is the following: firstly we put the lithology model which has been distinguished to the program, then input the lithology samples which need to be distinguished, following we could distinguish the needed lithology after running the program of distinguish, at last, we use kaben softer to display the section chart, this could contrast with the lithology data of geology, and we also compute the according rate that Bayesian distinguish identify the lithology in term of layer, simultaneity we gives the instruction about according rate.
Contrasted with the geological core data we could instruct: using Bayesian analysis to identify the lithology is got by layer in statistical method.Some wells of Bayesian analysis in the eastern of Urxun sag was used to identify the rate which is over 70 percent of lithology.
引文
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[2] Mark E M, John G M.Volcaniclastic deposits: implications for hydrocarbon exploration. In:Richard V, Fisher, Smith G A,eds. Sedimentation in volcanic settings[J].Society for Sedimentary Geology,Special Publication 1991,45:20～27.
[3] Secmann U, Schere M.Volcaniclastics as potential hydrocarbon reservoirs [J]. Clay Minerals, 1984, 19(9):457～470.
[4] Yuli Mitsuhata, Koichi Matsuo, Matato Minegishi.Magnetotelluric survey for exploration of a volcanicrock reservoir in the Yurihara oil and gas field,Japan[J].Geophysical Prospecing,1999,47(2):195～218.
[5] Wang Petal Relationship Between Volcanic Facies and Volcanic Reservoirs in Songliao Basin[J].Oil Gas Geol, 2003,24(1):18～23.
[6] Alberto Malinvemo. Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem[J].Geophysical Journal International,2002,151(3):675～688.
[7] Amro Elfeki, Michel Dekking. A Markov Chain Model for Sub-surface Characterization:Theory and Applications[J].Mathe-matical Geology,2001 ,33 (5):568～589.
[8] Wolf M et al.Faciolog-Automatic Electrofacies Determination.SPWLA 23rd annual Logging Symposium.1982.
[9] Ffron.B.统计学基础的争论。应用数学与计算数学。1980,(4):69~81.原载Amer.Math Monthly.1987, 85(4):231~246.
[10] Hunang,S,Y, Liang. T. C. Empirical Bayes Estimation of The Truncation Parameter with Lines Loss.Statistic Sinica.1997,(7):755~769.
[11] Ring.R.S.A. Decision Theretic Approach.Mathematical Statistics.New York Academic Press, 1967:98~102.
[12] Kiang, Melody Y. Extending the Kohonen Self-Organizing Map Networks for Clustering Analysis.Computational Statistics and Data Analysis. 2001.38(2) 161~180.
[13] Yang Miinshen, Ko Chenghsiu. On A Class of Fuzzy C-Numbers Clustering Procedures for Fuzzy Data.Fuzzy Sets and Systems.1996,84(1):49~60。
[14] 王维东。火成岩储层油气水层测井识别方法研究[D]。大庆石油学院硕士研究生学位论文,2005,1～2。
[15] 郎东升。大庆油区火成岩储层识别综合研究[J]。隐蔽油气藏形成机理与勘探实践,2004:329～334。
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