The Research On Lithology Identification Methods For Pyroclastic Rocks Reservoir

Currently, As the scale and depth continues to increase on oil and gas exploration, in many parts of the world, An increasing number of hydrocarbon reservoirs are found in pyroclastic rocks, for example, there develop a large-scale reservoirs of pyroclastic rocks in Hailar Basin of China, Therefore, people add important attention to research the evaluation about pyroclastic rock reservoir.Lithology identification is not only the basis of reservoir evaluation, but it is one of the log interpretation's major tasks. Only the lithology of reservoir was accurately identified, some other reservoir parameters can be computed more exactly. However, because the lithology of pyroclastic rocks is complexity, which make response characteristic of pyroclastic rocks in logging varied, and make the logging law worst. Lithology identification for pyroclastic rocks is so difficult that the parameters of pyroclastic rock reservoir isn't easy to compute accurately. So, This paper takes pyroclastic rock reservoir in Hailar Basin as study example, and developing an series of studies which is about lithology identification of pyroclastic rock.Before identifying pyroclastic rock, firstly, this paper researchs for rock types and strata characteristics of pyroclastic rocks. We can find the growthful cause of pyroclastic rocks which is greatly complex, for example, it can be basaltic, andesitic, rhyolitic, etc.. under the most circumstance, pyroclastic rock is growthful with lava and normal sedimentary rock,and if it intermingle with a large number of extrinsic clast, the logging characteristics of its are quite different. pyroclastic rocks can be divided into two major categories and five sub-categories, the classification of pyroclastic rocks are mainly based on the causes and number of clast, because every kind of pyroclastic rock contain the causes and the number of clast which is uncertain, the response characteristics of the same lithology in logging may also be different, so that the response characteristics of pyroclastic rocks in logging is very poor, but it doesn't means there are no rules to follow. According to the logging theory and the concept of volume model, the skeleton parameters should be between the sedimentary rocks and igneous rocks, So,the logging response of pyroclastic rock is the transition between the response of igneous rocks and the response of sedimentary rocks.This paper is based on the study of rock types and stratigraphic features for pyroclastic rocks, researching the logging curves characteristics of some pyroclastic rocks in Wuerxun– Bell depression of Hailar Basin, and this paper finds that these rocks comply basically with the law of log response characteristics. For example, the andesitic crystal tuff show at a low natural gamma value in Wuerxun– Bell depression of Hailar Basin, in the study literature of lava, andesite also show at a low natural gamma value. Moreover, the sediment tuff and the rhyolitic conglomerate are pyroclastic rock, which also comply with the logging theory and the concept of volume model, the logging response of pyroclastic rock is the transition between the response of igneous rocks and the response of normal sedimentary rocks.There are many the methods of lithology identification, but a majority of methods are technology, which aims at the simple mineral composition, the lithology of pyroclastic rock is so complex that these methods may not be applicable. Therefore, before recognizing pyroclastic rocks, firstly, this paper analyze the applicability for the lithology identification methods. And this paper finds that some logging parameters crossplot have good instructions for pyroclastic rocks, Moreover, considering the characteristics of pyroclastic rock in Hailar Basin, this paper choose the fuzzy pattern recognition method to identify pyroclastic rock.According to the analysis, firstly, this paper takes the reservoir of pyroclastic rock in Wuerxun - Bell depression of Hailar Basin for example, crossplot technique is used to identify primary lithology. In the process of using crossplot for the study area, we find it has good effect for the stratum of some relatively simple lithology, but in the stratum of complex lithology, the effect of crossplot is normally. In the process of using crossplot, we find that some crossplots have good effect for the lithology identification of pyroclastic rock. For example, the crossplots of PE-TH/U and PE-DEN is good at distinguishing the pyroclastic rock from the normal sedimentary rock, the cross plot of GR-RD is also good at distinguishing the pyroclastic rock from the normal sedimentary rock, the cross plot of TH-DT can be well distinguish the subclass of pyroclastic rock, Besides, the cross plot of DEN-(PROD-PRON) has very good instruction for the granularity which transit from the tuffaceous rough sandstone to the tuffaceous siltstone. In a word, In the process of identifying pyroclastic rocks, we find that some crossplots have good instructions for lithology identification, and understanding some characteristics of pyroclastic rocks.By studying the description data of core and the analysis data of flakes,we can find that there are more than 40 kinds of lithology in the studying area, it is difficult to be identified by logging methods. So, by analyzing the applicability of useful methods, finally, we choose the fuzzy pattern recognition method to identify pyroclastic rocks in the studying area. In the process of studying the character of logging curves and the lithology identification of cross plot, we can find that there are eleven kinds of logging parameters, which have good instructions for pyroclastic rocks, such as, GR, LLD, MSFL, KTH, DEN, CNL, DT, U, TH, K and so on. Therefore, these logging parameters are used for the input parameters of classifiable, there are more than forty lithology by geological core description, which are classified as over ten kinds of lithology with logging identification by using the method of fuzzy clustering, then we average the logging value of these lithology, establishing a statistical model, and choosing the right matching function, finally, programming to identify the lithology of pyroclastic rocks, and making correct rate over 80 percent.