Characteristics of Log Responses and Lithology Determination of Igneous Rock Reservoirs

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NANJING INSTITUTE OF GEOPHYSICAL PROSPECTING AND INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF GEOPHYSICS AND ENGINEERING J. Geophys. Eng. 1 (2004) 51–55 PII: S1742-2132(04)73730-9 Characteristics of log responses and lithology determination of igneous rock reservoirs

Buzhou Huang and Baozhi Pan

School of Geoexploration Science and Technology, Jilin University, Changchun 130026, China E-mail: [email protected] Downloaded from https://academic.oup.com/jge/article/1/1/51/5127508 by guest on 02 October 2021 Received 1 August 2003 Accepted for publication 1 November 2003 Published 16 February 2004 Online at stacks.iop.org/JGE/1/51 (DOI: 10.1088/1742-2132/1/1/006)

Abstract The determination of the lithology of deep igneous rocks in the northern Songliao Basin in China has been found to be difﬁcult because of insufﬁcient core-analysis data and accurate descriptions of cuttings in the area. This paper presents a method for determining the lithology of these rocks. First, the igneous rocks are divided into six classes according to cores and cuttings in the area. Then, for each class, a statistical analysis is performed for 11 log data sets to determine their log characteristics. Finally, the different lithologies of the igneous rock reservoirs in the area are automatically determined using the maximum membership function rule of fuzzy mathematics. A comparison between the results obtained and the lithologic column obtained from cores or cuttings shows that this method gives good results for the determination of igneous rock lithology in the area.

Keywords: lithology determination, igneous rocks, log characteristics, maximum membership function

1. Introduction igneous formations were automatically determined using the maximum membership function rule of fuzzy mathematics. An igneous rock can be a potential hydrocarbon reservoir (Shirley 1990) and, in the northern Songliao Basin in China, 2. Basic characteristics and types of igneous rock the igneous rocks of the deep formations are new prospective oil/gas reservoirs. However, an accurate determination of the The igneous rocks in the study area are mainly distributed lithology of the igneous rocks cannot be obtained because in the strata of the Huoshiling and Yingcheng groups of the of insufficient core analysis data and accurate descriptions Jurassic. Igneous formations were encountered in 10 wells of cuttings. Accurate lithology determination is essential during drilling. The dominant ones are intermediate and acid. because not only may it provide new information for geological However, due to the depth of the igneous reservoirs, core has research but it may also be an important basis for quantitative been difficult to obtain and there is an insufficient number of igneous core samples for analysis. In contrast, a large evaluation using log data. Its accuracy directly affects the amount of log data has been obtained in the study area. Thus, reliability of the evaluation (Scott 1979). the classification of the lithologies of the igneous reservoirs Log data reflect the variation in the physical properties of where the igneous rocks have not been cored is an urgent but a reservoir in situ. Different lithologies have different physical difficult task. properties, thus we are able to classify the lithologies of the All 90 natural elements in the crust have been found in igneous rocks using log data. In the study area, 11 logs over igneous rocks, but the proportions of these elements vary the cored igneous formations were recorded in different wells. greatly. Among them, O, Si, Al, Fe, Ca, Mg, Na, K, etc, The log characteristics of six lithologies were then determined account for the greatest part. The oxides in igneous rocks using statistical analysis. Finally, the lithologies of other mainly consist of SiO2,Al2O3, FeO, etc. More than 10 kinds

Figure 2. Neutron–gamma ray cross-plot for tuffs.

Figure 1. Neutron–density cross-plot for rhyolites. of the log responses of the six lithologies in the study area are

illustrated using several different cross-plots, each point in the Downloaded from https://academic.oup.com/jge/article/1/1/51/5127508 by guest on 02 October 2021 of common mineral have been found in igneous rocks and cross-plots representing the average value of the log data from their average contents are as follows: feldspar 60.2%, quartz identical lithologies in a cored interval. 12.4%, olivine, pyroxene and fasciculite 16.3%, mica 5.2%, apatite 0.6%, dark minerals (such as ilmenite and magnetite) 4.1%, and others 1.2%. 3.1. Uranium–thorium cross-plot Log characteristics of igneous formations vary from one Figure 3 shows a plot of uranium versus thorium obtained from place to another (Keys 1979, Rigby 1980, Sanyal et al 1980, a natural gamma-ray spectrometry log in the study area. Both Ouyang et al 1990, Khatchikian 1982). However, previous the uranium and thorium contents show an increasing trend results of other researchers cannot be used directly in this from basic igneous rocks to acid ones, but the rate of increase study area because of the differing mineral compositions and of uranium is lower than that of thorium. Therefore, the complicated textures of the igneous rocks. lithologies of the igneous rocks can be effectively determined The mineral composition of an igneous rock is the basis from the uranium versus thorium cross-plot. In figure 3, of its classification and it is important to comprehend its the uranium value for andesitic basalt is 0.4–0.6 ppm and origin, geological setting, chemical composition and physical the thorium value is 4.5–4.8 ppm. Its range of distribution properties (Belgasem 1992). The mineral composition of each is located on the lower left-hand side of the cross-plot. In class of igneous rock has its own typical constituent. These comparison, the uranium value for rhyolitic tuff has a wider constituents vary regularly in basic to acidic rocks if the rocks range, and the thorium value is 16.0–17.0 ppm, higher than are derived from the same magma source so it is possible that of other igneous rocks. Its range of distribution is located to classify igneous rocks using log data. According to core on the upper right-hand side of the cross-plot. For rhyolite, the analysis data and cuttings, the igneous rocks in the study uranium value also has a wide range, and the thorium value area can be divided into five main classes: rhyolite, andesite, is 10.0–14.0 ppm. Its range of distribution is below that of andesitic basalt, dacite and tuff. The cross-plots of log data for rhyolitic tuff. Based on the cross-plot, the three lithologies the same lithology from every reservoir, for convenience, were above can be effectively distinguished, but the method fails summed and then plotted. From these plots, we found that the for other lithologies. distribution range of rhyolite is the most fixed, andesite takes second place and tuff has two different ranges of distribution. 3.2. Resistivity–gamma ray cross-plot Figure 1 shows a neutron–density cross-plot for the rhyolites and figure 2 a neutron–gamma ray cross-plot for the tuffs. As we know, gamma-ray logs provide the most effective Thus, according to the gamma-ray value, tuff is divided into information for distinguishing between various igneous rocks, intermediate tuff (or andesitic tuff) and acid tuff (or rhyolitic and resistivity can be used to distinguish lava from tuff. Thus tuff) in this paper. Therefore six classes of igneous rocks the two logs will be more effective if plotted together. For are investigated here: andesitic basalt, andesite, andesitic tuff, example, there is only a small difference in the gamma- dacite, rhyolite and rhyolitic tuff. ray values for andesite and andesitic tuff, but the difference between their resistivity values is quite large. So, a resistivity– 3. Log characteristics of the igneous rocks gamma ray cross-plot can effectively distinguish between various igneous rocks. The log characteristics of igneous rocks are the integrated Figure 4 shows a plot with the actual log data from responses of their composition, structure, hydrothermal the study area. The gamma-ray value for andesitic basalt alteration, pore abundance and oil potential. The mineral is relatively low and that of the resistivity is comparatively compositions, such as quartz, feldspar, olivine, pyroxene, high. Its range of distribution is located in the lower central fasciculate and mica, of which igneous rock is mainly part of the cross-plot. For andesite, the resisitivity value is also composed, vary in a regular way so regular characteristics in comparatively high, often above 200 m, and the gamma-ray their log responses can be observed. Here, the characteristics value is about 60 API. The gamma-ray value for andesitic tuff

52 Characteristics of log responses and lithology determination of igneous rock reservoirs

Figure 3. Uranium–thorium cross-plot of the six classes of igneous rock in the study area. Downloaded from https://academic.oup.com/jge/article/1/1/51/5127508 by guest on 02 October 2021

Figure 4. Resistivity–gamma ray cross-plot of the six classes of the igneous rock in the study area.

Figure 5. Gamma ray–density cross-plot of the six classes of igneous rock in the study area. is 70–80 API, and that of the resistivity is comparatively low, cross-plot, but the density is higher than that of rhyolite. In although it varies widely. The three lithologies above can be addition, andesitic basalt is located on the lower left-hand side effectively distinguished in the cross-plot, but the method fails of the cross-plot. These three lithologies can be effectively for other lithologies. If there are abundant fractures in lava, the distinguished, but the method fails for other lithologies. resistivity value will be lower. When this occurs it is difficult to distinguish lava from tuff using resistivity values. 3.4. M–N cross-plot The responses of three porosity logs, i.e. density, neutron and 3.3. Gamma ray–density cross-plot sonic logs, are all influenced by lithology and true porosity. Density is an important index of lithology classification, and On the cross-plots of any two of them, it is found that the slope the density values obtained using a compensated density of identical lithologic lines is comparatively stable. Therefore logging tool can accurately reflect the true values of the density two new parameters, M and N, are defined in order to eliminate of various strata. Thus the gamma ray–density cross-plot can the influence of porosity. The compound parameters M and N effectively distinguish between lithologies. are calculated as follows: Figure 5 shows a cross-plot of gamma ray versus density tf − t obtained using the actual data from the study area. Rhyolite M = × 0.01, (1) ρ − ρ is located on the lower right-hand side of the cross-plot, b f −3 φ − φ the density being comparatively low (about 2.45 g cm ). N = Nf N , (2) Rhyolitic tuff is also located on the right-hand side of the ρb − ρf

53 B Huang and B Pan

Figure 6. M–N cross-plot of the six classes of igneous rock in the study area. Table 1. Average value of logs of the igneous rocks from the cored intervals in the study area.

Lithology Interval GR CNL RHOB U Th K DT Rt Pe MN Downloaded from https://academic.oup.com/jge/article/1/1/51/5127508 by guest on 02 October 2021 Andesitic basalt Well A-1 36.9 0.27 2.55 0.32 4.5 1.15 64.9 130 4.11 0.81 0.47 Well A-2 36.4 0.37 2.39 0.47 4.32 1.11 75.1 87.7 3.85 0.82 0.45 Andesite Well B-1 77.4 0.06 2.72 1.97 6.8 2.83 50.9 1793 3.97 0.8 0.55 Well B-2 59.1 0.12 2.71 1.23 5.25 2.25 53.8 492 4.36 0.79 0.51 Well A-3 70.4 0.17 2.6 2.31 6.29 2.19 63.2 248 3.92 0.78 0.52 Andesitic tuff Well B-3 82.7 0.1 2.62 1.95 5.89 3.4 59.9 65.5 3.69 0.8 0.56 Well C-1 73.4 0.15 2.69 1.19 7.51 2.73 66.6 43.3 3.49 0.72 0.5 Well A-4 70.7 0.3 2.52 1.34 6.11 2.73 82.1 5.58 3.96 0.77 0.46 Dacite Well D-1 80 0.08 2.6 2.35 7.8 3.42 60.2 120 2.9 0.77 0.52 Rhyolite Well E-1 118 0.05 2.46 2.1 13.4 4.02 65.6 52.5 2.48 0.85 0.65 Well E-2 129 0.06 2.43 4.49 13.1 3.61 65.9 60.2 2.57 0.86 0.66 Well F-1 147 0.01 2.59 53.2 5168 2.96 0.86 0.63 Well H-1 167 0.06 2.45 58.5 60.8 0.9 0.65 Rhyolitic tuff Well C-2 153 0.06 2.6 3.64 16.4 4.12 93 99.8 2.99 0.6 0.59 Well I-1 162 0.11 2.63 8.35 17.9 2.92 74.2 5.1 3.11 0.71 0.55

where t and tf are the transit times of rock and pore fluid Eberle (1992) used multivariate statistical analyses, respectively, ρb and ρf are the densities of rock and pore including principal component analysis (PCA) and linear fluid respectively, and φN and φNf are the apparent neutron discriminant analysis (LDA), to classify the complex porosities of rock and pore fluid respectively. lithologies in KTB Oberpfalz VB (0–480 m). Pan et al Figure 6 shows a cross-plot of M versus N obtained using (2003) applied correspondence analysis to the identification actual data from the study area. It shows that the value of of igneous rock lithology in the Songliao Basin, China. M for rhyolite is 0.85–0.90 and that of N is 0.63–0.65, much In this paper, the multi-parameter fuzzy cluster method higher than that of other lithologies. For andesitic basalt, the is used to classify the lithostratigraphy of igneous rocks value of M is 0.8–0.83 and that of N is 0.45–0.47; it is located because the log data of identical lithologies will show normal on the lower right-hand side of the cross-plot. In addition, the distribution if there are sufficient samples. The membership value of M for rhyolitic tuff is about 0.6–0.7 and that of N is function is given by 0.54–0.58; it is located on the upper left-hand side of the cross- m 2 −(xj − x¯ij ) plot. These three igneous rock lithologies can be effectively UA (x) = Bj exp , (3) i 2σ 2 distinguished with an M–N cross-plot, but the method fails ˜ j=1 ij for other lithologies. Thus it is impossible to distinguish where Ai represents the fuzzy subset of the ith lithology, between all classes of igneous rocks using only one or two ˜ UA is the membership function of x(x1,x2,...,xj ,...,xm) ˜ i cross-plots. belonging to Ai , Bj is the weight of the jth log data set, σij is the square root˜ of variance of the jth log data set of the ith 4. Determining lithology using the fuzzy cluster lithology, x¯ij is the average of the jth log data set of the ith method lithology, and xj is the jth log of the lithology of the reservoir to be classified. Subscript i = 1, 2,...,n denotes different From the above analyses, we find that one cross-plot can lithologies, and subscript j = 1, 2,...,m denotes different differentiate some classes of igneous rocks effectively, but not log data sets. Because the ability of each kind of log data set all of them. This is because insufficient log data are used in the to classify lithologies is not the same, the weights Bj are also cross-plots, i.e. only two or three log data sets. The problem not the same. Bj is obtained after analysis of a large number can be solved if a large number of log data sets, weighted of log data. according to their degree of variation between the lithologies, In the application, 11 logs of the igneous rock reservoir, are summed and used simultaneously. recorded in different wells in the study area, were analysed

54 Characteristics of log responses and lithology determination of igneous rock reservoirs 5. Conclusions

Certain differences in gamma-ray, density, neutron and resistivity logs occur for various igneous rocks. Using many logs rather than only one or two, the lithologies of these rocks can be better differentiated. Automatic classiﬁcation of the lithologies can be effectively realized using the fuzzy cluster method. This involves applying the maximum membership function rule to the summed log characteristics, using a large number of log data from the igneous rocks in the study area. The result is gratifying. The accurate lithologic classiﬁcation of the igneous rock reservoirs is then the basis for further calculations of their reservoir petrophysical parameters.

Acknowledgments Downloaded from https://academic.oup.com/jge/article/1/1/51/5127508 by guest on 02 October 2021 This work was supported ﬁnancially by the Natural Science Foundation of China (no 49894190-4). The authors thank Daqing Oil Company for permission to use log and core data from the Songliao Basin, China.

References

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