松辽盆地徐家围子断陷深层火山岩岩性、岩相的测井识别
摘要
本文针对松辽盆地徐家围子断陷深层火山岩,以地质-测井综合研究为指导思想,就火山岩岩性测井识别和岩相变化规律进行了深入研究,并用于预测火山岩岩相的井间分布。火山岩岩性研究方面,采用灰色关联分析方法进行岩性识别,首先利用极值方差聚类分层方法对研究区测井资料进行分层处理,结合岩心资料对火山岩岩性的定名,建立测井相-火山岩岩性统计模式,然后利用灰色关联分析方法识别研究区火山岩岩性。火山岩岩相规律则利用马尔科夫链分析方法进行研究,首先利用该方法对井内岩相垂向上的变化规律进行总结和分析,在此基础上尝试推算二维的岩相展布规律。这些方法的应用将火山岩研究在一定程度上又向前推进了一步。利用这些方法已对松辽盆地徐家围子断陷多口井进行了处理,取得了较好的实际应用效果,对于提高火山岩岩性测井识别和岩相研究的精度具有重要意义。
Currently, the biggest problem that the world must to face is a serious shortage of backup reserves, it’s urgently need to use a new geological theory toguide the search for new exploration areas. Volcanic is the main hydrocarbon reservoir rocks in China, Xujiaweizi fault depression as the larger deep depression in the northern of Songliao Basin, with the deep understanding of geological law to the natural gas and with the growing capabilities of investigation techniques, the exploration of the gas reservoir of lithology in Mesozoic volcanic got a breakthrough.
Logging is an important means to the oil and gas exploration and development, it is the integrative reflection of the lithology, facies, porosity and permeability and hydrocarbon saturation of reservoir. For the volcanic reservoir, studying the relation of logs curve and lithology and facies can play a direct role instructions in the oil and gas exploration and development.
Therefore, this article statistics and summaries the well logging characters for the deep volcanic in Xujiaweizi fault depression. On this basis, establishing the volcanic rocks-logging data statistical model and using gray relational analysis to identify the single well lithology for the study area. At the same time, using Markov chain analysis to summary the phase change rules of the deep volcanics in Xujiaweizi depression, Songliao Basin, and making prediction of single well facies and simulation of two-dimensional facies on the base.
1. Response characteristics of volcanic rocks in logging
GR and resistivity curves have high-value overall, acoustic mostly low-value (high speed). With the transition of basic volcanic to acidic volcanic, radioactivity increased gradually; neutron porosity value decreased gradually, and up and down with the porosity and the fluid content in fracture; density vares from large into small; the acoustic value of compact basalt is the lowest and the acidity rhyolite slightly higher; PE value gradually decreased. In similar rocks, with the transition of lava to pyroclastic rock, radioactivity increased; density will be reduced, and the density of the development of crack was significantly decreased and dramatic changed serrated; acoustic usually decreased, when stomatal or cracks developed, the acoustic value will increase and resistivity generally decreased.
2. Cross plot characters of deep stratum volcanic rocks in Xujiaweizi fault depression
The volcanic rocks have the close contitunent but different structure can be considered one kind of lithology. In the study, the kinds of volcanic lithology can be identified by logging are: basaltic lithology, andesitic lithology, trachyandesitic lithology, dacitic lithology, and ryolitic lithology. Statistic analyse is done for core from twenty wells, and considered the depth point that has wafer analyse and was modifid by whole rock analyse as one swatch point. Read corresponding logs data and calculate complex parameters M and N. At all the base mentioned above, after making cross plot, discovered the cross plot of GR-TH and GR-PE are better.
The cross plot of GR-TH is good at distinguish these rocks, which are basaltic lithology, andesitic lithology, trachyandesitic lithology, dacitic lithology, and ryolitic lithology. The plate correctness can reach at 92.62%。The cross plot of GR-PE is also good, the plate correctness can reach at 84.43%。
3. Log character of deep volcanic facies in Xujieweizi fault depression Songliao Basin
Volcanic facies of Xujieweizi fault depression can be devided into five facies and fifteen subfacies, their log character is as follows:
Volcanic vent facies: log character of volcanic vent facies generally are big value of GR and middle resistivity, except value of GR log of basalt with volcanic vent subfacies is small and resistivity of tuff with volcanic vent subfacies is low. Character of curves’modality mostly is serrated form of high amplitude and peak, especially, difference between deep investigation laterolog and shallow investigation laterolog of andesite with subvolcanic subfacies is large, so heterogeneity is obvious.
Explosive facies: Value of GR log of rhyolite with explosive facies is big, value of GR log of andesite with explosive facies is middle, but most of tuff is rhyolite and its value of GR log is big but on the low side. Character of curves’modality is high amplitude and serrated form. Generally, log character of explosive facies is low-middle resistivity, and curves’modality of explosive is serrated form of low-middle amplitude.
Effusive facies: GR log of rhyolite with effusive facies has big value and serrated form of high amplitude, GR log of andesite with effusive facies has mid value and serrated form of middle amplitude, and GR log of basalt with effusive facies has low value and serrated form of low amplitude. Resisitivity log of volcanic rocks with effusive facies has low value (for middle subfacies)and low-middle value (for upper and down subfacies), and curves’modality of volcanic rocks with effusive facies is serrated form of high amplitude, but for down subfacies with some or none breccia the amplitude is micro serrated form.
Extrusive facies: Value of GR log of volcanic lava with effusive facies is big, and the curves’modality is serrated form of middle-high amplitude. Dual laterolog of volcanic lava with effusive facies is low-middle resistivity (for middle-inner subfacies) and middle resistivity (for outer subfacies), the curves’modality is serrated form of middle-micro amplitude. Especially, the value of GR of rhyolite is bigger than the value of GR of pearlite. GR log of tuff lava with outer subfacies is serrated form of middle amplitude. Resistivity log of tuff lava with outer subfacies is middle resistivity and the curves’modality is serrated form of low amplitude.
volcanogenitic sedimentary facies: Value of GR log of volcanic sedimentary rocks usually is middle-low, and Value of resistivity log of volcanic-sedimentary rocks is usually middle-low. Difference between different subfacies is obivious. The GR curves and resistivity curves of retransported volcanic sediments obviously are imageing. The curves` amplitude of tuff sandstone with epiclastic-bearing sedimentary subfacies is low.
4.Application extremum variance cluster method in layering data record
The changes of Logging curve was mainly due to changes in the physical properties of rocks, The different physical properties of rocks corresponding to different logging response. We need to divide log profile into many small layers which according to the changes of logging curve in shape and amplitude characteristics, so that each layer within the same physical characteristics and facies types.
The essential of variance analysis is to find out the largest variance among layers and the smallest variance in layers as a point of layering point. Through the Q extremum for crude layering data record, the Rm ax for subdivisable layering data record and merge layers processing, we can use extremum variance cluster analysis method to completed auto-layering data record.
Using this method successfully completed subdivisable layering data record for logging data of XuShen5, XuShen4, XuShen602 and so on. It is the base of volcanic lithological identification.
5. Using grey relational analysis method to identify volcanic lithology
Through standardize the logging data, calculate grey multiple weighted coefficient Pi(j) and calculate grey correlation of standard model ( reference array Xi) Cgi, so as to achieve logging lithological identification.
Identification of volcanic lithology of Yingcheng formation in Xujiaweizi fault depression in Songliao Basin, which need to access to lithology profile information of the key well from geological logging and coring data, pick up the characters of logging response in homologous section of formation profile. Then the relationship statistical model between the volcanic lithology of study area and logging data can be established.
Using this method to identify the volcanic rocks, compared to based on coring data for geological naming of volcanic lithology in this area. The correct identification rate could achieve above 70%.
6. Study phase transition of volcanic by using Markov chain method
Markov method is one analysis method to forecast one variable’s intending estate and moving direction by using the one variable’s current estate and moving direction. The method is fit for time list and space list. The Markov process that time and state are discrete is called Markov chain.
Facies change on strata section is only related to facies change of former stratum, and is not related to the more former stratum. It is resulted that strata section has typical Markov chain character.
With Markov chain analysis of volcanic facies in deep stratum of Xujiaweizi fault depression, simplified facies model of volcanic facies change in study zone can be gotten, and on the base, facies can be predicted by using absolute forecast method and superpose forecast method, judge accuracy of facies is 80% in well field of xushen 1 well.
Currently, the biggest problem that the world must to face is a serious shortage of backup reserves, it’s urgently need to use a new geological theory toguide the search for new exploration areas. Volcanic is the main hydrocarbon reservoir rocks in China, Xujiaweizi fault depression as the larger deep depression in the northern of Songliao Basin, with the deep understanding of geological law to the natural gas and with the growing capabilities of investigation techniques, the exploration of the gas reservoir of lithology in Mesozoic volcanic got a breakthrough.
Logging is an important means to the oil and gas exploration and development, it is the integrative reflection of the lithology, facies, porosity and permeability and hydrocarbon saturation of reservoir. For the volcanic reservoir, studying the relation of logs curve and lithology and facies can play a direct role instructions in the oil and gas exploration and development.
Therefore, this article statistics and summaries the well logging characters for the deep volcanic in Xujiaweizi fault depression. On this basis, establishing the volcanic rocks-logging data statistical model and using gray relational analysis to identify the single well lithology for the study area. At the same time, using Markov chain analysis to summary the phase change rules of the deep volcanics in Xujiaweizi depression, Songliao Basin, and making prediction of single well facies and simulation of two-dimensional facies on the base.
1. Response characteristics of volcanic rocks in logging
GR and resistivity curves have high-value overall, acoustic mostly low-value (high speed). With the transition of basic volcanic to acidic volcanic, radioactivity increased gradually; neutron porosity value decreased gradually, and up and down with the porosity and the fluid content in fracture; density vares from large into small; the acoustic value of compact basalt is the lowest and the acidity rhyolite slightly higher; PE value gradually decreased. In similar rocks, with the transition of lava to pyroclastic rock, radioactivity increased; density will be reduced, and the density of the development of crack was significantly decreased and dramatic changed serrated; acoustic usually decreased, when stomatal or cracks developed, the acoustic value will increase and resistivity generally decreased.
2. Cross plot characters of deep stratum volcanic rocks in Xujiaweizi fault depression
The volcanic rocks have the close contitunent but different structure can be considered one kind of lithology. In the study, the kinds of volcanic lithology can be identified by logging are: basaltic lithology, andesitic lithology, trachyandesitic lithology, dacitic lithology, and ryolitic lithology. Statistic analyse is done for core from twenty wells, and considered the depth point that has wafer analyse and was modifid by whole rock analyse as one swatch point. Read corresponding logs data and calculate complex parameters M and N. At all the base mentioned above, after making cross plot, discovered the cross plot of GR-TH and GR-PE are better.
The cross plot of GR-TH is good at distinguish these rocks, which are basaltic lithology, andesitic lithology, trachyandesitic lithology, dacitic lithology, and ryolitic lithology. The plate correctness can reach at 92.62%。The cross plot of GR-PE is also good, the plate correctness can reach at 84.43%。
3. Log character of deep volcanic facies in Xujieweizi fault depression Songliao Basin
Volcanic facies of Xujieweizi fault depression can be devided into five facies and fifteen subfacies, their log character is as follows:
Volcanic vent facies: log character of volcanic vent facies generally are big value of GR and middle resistivity, except value of GR log of basalt with volcanic vent subfacies is small and resistivity of tuff with volcanic vent subfacies is low. Character of curves’modality mostly is serrated form of high amplitude and peak, especially, difference between deep investigation laterolog and shallow investigation laterolog of andesite with subvolcanic subfacies is large, so heterogeneity is obvious.
Explosive facies: Value of GR log of rhyolite with explosive facies is big, value of GR log of andesite with explosive facies is middle, but most of tuff is rhyolite and its value of GR log is big but on the low side. Character of curves’modality is high amplitude and serrated form. Generally, log character of explosive facies is low-middle resistivity, and curves’modality of explosive is serrated form of low-middle amplitude.
Effusive facies: GR log of rhyolite with effusive facies has big value and serrated form of high amplitude, GR log of andesite with effusive facies has mid value and serrated form of middle amplitude, and GR log of basalt with effusive facies has low value and serrated form of low amplitude. Resisitivity log of volcanic rocks with effusive facies has low value (for middle subfacies)and low-middle value (for upper and down subfacies), and curves’modality of volcanic rocks with effusive facies is serrated form of high amplitude, but for down subfacies with some or none breccia the amplitude is micro serrated form.
Extrusive facies: Value of GR log of volcanic lava with effusive facies is big, and the curves’modality is serrated form of middle-high amplitude. Dual laterolog of volcanic lava with effusive facies is low-middle resistivity (for middle-inner subfacies) and middle resistivity (for outer subfacies), the curves’modality is serrated form of middle-micro amplitude. Especially, the value of GR of rhyolite is bigger than the value of GR of pearlite. GR log of tuff lava with outer subfacies is serrated form of middle amplitude. Resistivity log of tuff lava with outer subfacies is middle resistivity and the curves’modality is serrated form of low amplitude.
volcanogenitic sedimentary facies: Value of GR log of volcanic sedimentary rocks usually is middle-low, and Value of resistivity log of volcanic-sedimentary rocks is usually middle-low. Difference between different subfacies is obivious. The GR curves and resistivity curves of retransported volcanic sediments obviously are imageing. The curves` amplitude of tuff sandstone with epiclastic-bearing sedimentary subfacies is low.
4.Application extremum variance cluster method in layering data record
The changes of Logging curve was mainly due to changes in the physical properties of rocks, The different physical properties of rocks corresponding to different logging response. We need to divide log profile into many small layers which according to the changes of logging curve in shape and amplitude characteristics, so that each layer within the same physical characteristics and facies types.
The essential of variance analysis is to find out the largest variance among layers and the smallest variance in layers as a point of layering point. Through the Q extremum for crude layering data record, the Rm ax for subdivisable layering data record and merge layers processing, we can use extremum variance cluster analysis method to completed auto-layering data record.
Using this method successfully completed subdivisable layering data record for logging data of XuShen5, XuShen4, XuShen602 and so on. It is the base of volcanic lithological identification.
5. Using grey relational analysis method to identify volcanic lithology
Through standardize the logging data, calculate grey multiple weighted coefficient Pi(j) and calculate grey correlation of standard model ( reference array Xi) Cgi, so as to achieve logging lithological identification.
Identification of volcanic lithology of Yingcheng formation in Xujiaweizi fault depression in Songliao Basin, which need to access to lithology profile information of the key well from geological logging and coring data, pick up the characters of logging response in homologous section of formation profile. Then the relationship statistical model between the volcanic lithology of study area and logging data can be established.
Using this method to identify the volcanic rocks, compared to based on coring data for geological naming of volcanic lithology in this area. The correct identification rate could achieve above 70%.
6. Study phase transition of volcanic by using Markov chain method
Markov method is one analysis method to forecast one variable’s intending estate and moving direction by using the one variable’s current estate and moving direction. The method is fit for time list and space list. The Markov process that time and state are discrete is called Markov chain.
Facies change on strata section is only related to facies change of former stratum, and is not related to the more former stratum. It is resulted that strata section has typical Markov chain character.
With Markov chain analysis of volcanic facies in deep stratum of Xujiaweizi fault depression, simplified facies model of volcanic facies change in study zone can be gotten, and on the base, facies can be predicted by using absolute forecast method and superpose forecast method, judge accuracy of facies is 80% in well field of xushen 1 well.
引文
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