Lithology analysis using Neutr on-gamma Logging

Lithology analysis using Neutr on-gamma Logging Takao AIZAWA, Tsutomu HASHIMOTO (SUNCOH CONSULTANTS Co., Ltd.), Nirou OKAMOTO (NEDO), Hiroshi KARSHIMA (Japan Coal Energy Center) SUMMARY We tried to estimate lithology analysis using Neutron-Gamma Logging and geophysical logging data. If we use only the Neutron-Gamma Logging derived parameters, an overall success rate of up to 78.2% is achieved. Compared with the success rate of 83.5% from the conventional log parameters, this is a valuable achievement as it uses only a single log data set. If we combine the conventional and Neutron-Gamma Logging data together, an overall success rate of up to 93.1% for the four control holes is obtained. When this technique is applied to independent holes, the success rates can reach 87.3%. By using Newtron-Gamma Logging data showed that lithology analysis could be carried out more effectively. 1 INTRODUCTION The analysis of drill core gives all the information which can be extracted from a borehole. However, in many cases the core cannot be fully recovered and geophysical logging is an alternative method to provide the required information. The prime benefit of geophysical logging is that it allows detailed interpretation of non-cored holes, permitting either substitution of diamond drilling or extension of drilling programs on the same budget based on the fact that non cored holes are cheaper to drill. Geophysical logs can provide rock properties independent of core recovery, offering scope for grade prediction and rock mass characterization as well as orebody delineation and litho-stratigraphic interpretation. Neutron-gamma logging technique is able to produce information on chemical composition, while sonic and resistivity logging techniques can present information related to physical microstructure. It can be envisaged that a combination of these different borehole logging techniques would provide information related to rock characteristics and properties. Therefore, appropriate analysis of the information obtained by combining different geophysical techniques would have the potential for improved in-situ determination of rock characteristics and properties. We performed examination about the lithological analysis using Neutron-Gamma Logging and geophysical logging data. 2 METHODOLOGY OF NEWTRON-GAMMA LOGGING The Methodology of Neutron-gamma Logging is concerning with a scattering and absorption of r-ray and serialization and disappearance of neutrons. The neutron emitted by the the neutron source are etherealized by elastic scattering with hydrogen nuclei present in and around the borehole fluid. They subsequently interact with the nuclei from the rock matrix when they reach the thermal energy. In this way, the gamma ray is produced. (Fig.1) About this gamma ray, the energy level is different with each element, and the intensity is proportional to the content of the element. Elastic scattering Th erm al n eu tron N eu tron from source Atom Gam ma ray Emitting of a neutron elastic scattering Thermal neutron Neutron capture(n,γ) reaction Produce of Gamma ray Fig.1 Schematic diagram of the newtron behavior

Therefore, it is possible by measuring a gamma ray to presume the kind and quantity of the element contained in a formation. This technique is called prompt gamma neutron activation analysis (PGNAA). NEDO of Japan developed new exploration system for in-situ coal analysis in the Basic Survey for Coal Resources Project. We put PGNAA logging technique in practical use in collaboration with CSIRO, and are examining applicability to coal resources, rock engineering and environmental research on this project. 3 STATISTICAL CHARACTERIZATION The data interpretation was carried out using the automatic interpretation program LogTrans (Fullagar P. K. et al., 1999) developed jointly by CMTE and CSIRO. The program exploits the contrasts in petrophysical signatures between different classes of rock and performs rapid analysis of multi-parameter logs. The rock types or interpretational classes differ by lithology, stratigraphy, grade, mechanical properties, or combination of these. The program falls in the first category mentioned above, using statistical methods for data interpretation. LogTrans makes the assumption that the physical properties of a given rock type will be statistically invariant over a usefully large volume. In order to obtain the petrophysical calibration for the automatic interpretation of geophysical logs, it is necessary to petrophysically characterize each rock type. For this, it is essential to have reliable data in the control holes based on geological logs and geophysical measurements. Also, a lot of care must be taken to reconcile the geophysical logging depths with drilling depths before the rock types are petrophysically characterized. In this analysis we have used the depths of coal seams as markers. Fig.2 Hypothetical control data set, to illustrate the LogTrans algorithm (modified after Emilsson, 1993). Points A, B, C define (density,gamma) data pairs recorded in noncontrol holes. The LogTrans algorithm can be understood as an extension of the domainal interpretation of scatterplots from two to multiple dimensions (e.g., Emilsson, 1993). Each rock type populates a certain domain in multi dimensional space as shown schematically for a two dimensional case in Fig.2. For each rock class a centered can be defined in the parameter space, representing the typical rock properties for that class. In LogTrans the centered are the class medians or means derived from the control data set. The control data must be representative of the lithologies intersected in the boreholes. It is advisable to have the control holes drilled in the same deposit with the holes to be interpreted. In reality, the domain boundaries, as shown in Fig.2, are not always sharply defined especially if the number of data points is small for a given rock class. This may be due to a gradational rather than discrete geological changes or natural scatter. 4 LITHOLOGY PREDICTIONS FROM GEOPHYSICAL DATA We applied this technique to real data, where NEDO of Japan conducted an exploration program. Four cored holes (BG001, BG002, BG003 and BG004) were drilled and logged. Both conventional geophysical techniques such as

sonic, neutron-neutron, gamma (total natural gamma) and density (backscattered gamma-gamma) and the NEDO s new PGNAA tool employing a 252 Cf neutron source were used to log the boreholes. The cores recovered from the four holes were logged for lithology by geologists. We used an automated geophysical log interpretation program LogTrans, to analyze both conventional and neutron-gamma (PGNAA) logging data for geotechnical characterization. 4.1 Log Data preparation The main purposes of these examinations are to evaluate the feasibility of using conventional geophysical logs and PGNAA parameters for geotechnical characterization of the strata. Rock type recognition is a first step of rock characterization and is a first order approximation for rock strength estimation as different rock types reflect different rock strength in general. Therefore we focus on the rock type recognition in this analysis. The lithological information is based on the geological interpretation of the cores from the four boreholes in the interested area: BG001, BG002, BG003 and BG004. The key rock types are listed in Table 1. Only the rock types with total length larger than 0.5 m are listed in the table and considered as statistically reliable for consequent analysis. Please note that not all the classes such as IR, MIX and BC are present in every borehole. Both geophysical logs and geological classes were carefully checked against each other and necessary corrections such as sensor depth-offset, drill geometry, instrument drift and depth registration (Fullagar et al, 1999) were applied appropriately before they were used for statistical analysis and strata interpretation. Table 1. Rock type classification Rock CLASS ID Descriptions Populations SA Sandstone (Nr. 3805 of points) SI Siltstone 575 SS Interlaminated sandstone and siltstone 4098 CASISI Carbonaceous siltstone and siltstone 62 C Coal 839 BC Bad coal 60 IR Igneous rock 20 CASISA Carbonaceous siltstone and sandstone 39 TU Tuff 38 MIX Mixed coal, carbonaceous siltstone, coaly mudstone 81 CASI Carbonaceous siltstone 50 4.2 Parameter signatures of rock classes The geophysical logging data available for this analysis contained information on sonic, neutron-neutron (porosity), total natural-gamma, density, and parameters from the PGNAA logs such as Fe, Si, Ca, density, H, B, Al and ash. Statistics (medians and spreads) for these log parameters in each rock unit were computed by LogTrans. During the computation of the statistics, a running median filter with a window length of 0.5 meters has been applied to the log parameters to remove those erratic noises in the data. Fig.3,4 present the medians and spreads for sonic and parameter Si of PGNAA logging. The reliability of the statistics is not uniform as indicated by the population size (the last column) in Table 1. There is considerable overlap of the spreads from one rock unit to another, for all parameters. Therefore, with the exception of the coal seam itself, the individual rock units cannot be uniquely identified on the basis of a single parameter but combination of multi-parameters can increase the chance of correct recognition of the rock classes as we will demonstrate in the next section. Medians and spread plots represent a fairly severe test of control data quality. Often problems with either wire line data or geotechnical logging can be recognized and corrected. Establishing control statistics is commonly an iterative process, sometimes involving introduction of new rock classes. After we calculate the statistics (medians and spreads) for the log parameters for each rock class, the next step is to apply these statistics to the geophysical logs to predict the rocks for the four available boreholes.

Fig. 3 Medians and spreads of sonic log for the strata classes in Table 1 from the four control boreholes. 4.3 Results from four control holes Fig.4 Medians and spreads of PGNAA parameter silica for the strata classes in Table 1 from the four control boreholes. We first apply the statistics such as control holes which are used for statistics computations. Fig.5 shows the example of computation. The effectiveness of the interpretation is measured by an overall success rate in percentage. The success rates with different parameters and their combination for all the four boreholes are listed in Table 2. This list is incomplete and it only reflects the tests we have done. Table 2. Overall success rate for rock type prediction using different geophysical parameters Geophysical log parameters used in Interpretation (indicated by X ) Success Sonic Nat-γ Dens. NEUT n-γ n-γ n-γ n-γ n-γ n-γ n-γ n-γ 68.2 X 36.9 X 17.9 X 75.5 X 13.7 X 21.2 X 37.8 X 66.3 X 26.7 X 19.7 X 14.7 X 26.6 X 82.3 X X X X X X X X X X X X 65.0 X X X X X X X X 67.6 X X X X X X X 67.7 X X X X X X 70.6 X X X X X 72.6 X X X X 78.2 X X X X X 83.5 X X X X 84.5 X X X X X X X X X 85.0 X X X X X X X X 83.8 X X X X X X X 85.4 X X X X X X X 85.5 X X X X X X It is clear that the individual log parameter has very limited success rate. Most effective parameters interpretation in this area are sonic (68.2 %), neutron-neutron (75.5%) and neutron- density (66.3%). If we use all the parameters listed in the table, we get a success rate of 82.3%. Evidently, with more parameters, its success rate is increased significantly. However, it is not necessary that more log parameters will give a higher success rate.

It is worth pointing out that a success rate of 72.6% can be achieved by using the PGNAA derived parameters Si, Ca, density and ash and 78.2% if the natural gamma is added. A spectrometric natural gamma log (or total gamma log) can be carried out with the same PGNAA tool by not attaching the neutron source. Compared with the success rate of 83.5% from the conventional log parameters, this is a valuable achievement as it uses only a single tool. If we combine the conventional logs and the PGNAA derived parameters, we can achieve a success rate of 85.5% as shown at the last row. The example of the interpretation results for each control hole is presented in Fig.5. The overall success rates for the four control boreholes are 77.2% (BG001), 82.9% (BG002), 91.3% (BG003) and 91.9% (BG004) as tabulated in the second column in Table 3. Except for the control hole BG001, all other boreholes have very good success rate. This may be caused by the poor classification in original rock classes for the borehole BG001. These incorrect interpretations in one way reflect the different resolution between the geophysical interpreted geology and the geologist logged geology. The same observation can be made for other boreholes. Geophysical log interpretation can normally provide more details than geological logs. 4.4 Results from independent holes Correct interpretation of control boreholes using geophysical logs is a necessary condition for implementation of automated interpretation. A stronger endorsement of the practical viability of computer-aided interpretation is achieved if cored holes, which do not belong to the control hole set, can be interpreted correctly. To illustrate the application of LogTrans to a non-control hole, the statistics were re-calculated with just three of the original four control holes, in a rotational fashion. This may reduce the reliability of our statistics in running GRSTAT due to lack of sufficient data. But with limited available control holes, this is a viable option for testing purposes. All the four holes were then processed using the reduced statistics. The success rates for each borehole are listed in Table 3. The second column represents the success rates of each borehole using the statistics from all the four control holes; the third column presents the success rates by using the reduced statistics from holes: BG001, BG002 and BG003; the forth column is the success rates using the statistics of BG001, BG002 and BG004; the fifth column shows the success rates resulting from the reduced statistics of BG001, BG003 and BG004; and the last column provides the success rates from the statistics of BG002, BG003 and BG004. When the borehole acts as an independent hole, its success rate is presented in red in the table. The example of the interpretation results of these boreholes for the above cases is presented in Fig.6. Table 3. Overall performances of LogTrans strata predictions based on combination of different control boreholes. Ctrl Holes 1+2+3+4 1+2+3 1+2+4 1+3+4 2+3+4 Prediction Hole BG001 77.16 80.10 76.10 81.98 69.51 BG002 82.94 83.95 82.27 84.29 82.10 BG003 91.33 91.53 85.54 93.08 90.21 BG004 91.93 87.27 88.90 91.57 91.84 5 CONCLUSIONS A geotechnical characterization study, based on geophysical wire line logs, has yielded encouraging results. The petrophysical signatures of rock strata units were statistically characterized from the four cored boreholes. The key geophysical log parameters used in this study were the conventional sonic, natural gamma and neutronneutron logs plus the PGNAA derived ash content, silica and density parameters. If we use only the PGNAA derived parameters, an overall success rate of up to 78.2% is achieved. Compared with the success rate of 83.5% from the conventional log parameters, this is a valuable achievement as it uses only a single log data set. If we combine both the conventional and PGNAA data together, an overall success rate of up to 93.1% is obtained. This indicates that the PGNAA logging data can significantly enhance the performance of geotechnical characterization from geophysical logs. It is worth mentioning that the PGNAA tool can be employed for spectrometric natural gamma logging. We expect an improvement in the rock characterization from geophysical log data by using the

spectrometric natural gamma logging instead of conventional total natural gamma logging that was used in this study. Fig.5 An interpretation of control borehole BG003. Shown in order from the left to the right: geological core log, prediction of the rock classes and log parameters used in the interpretation. Fig.6 An interpretations for borehole BG003. The first column is the geological core log,. The rest columns are predictions of the rock classes based on the statistics from control holes 1+2+3+4, 1+2+3, 1+2+4, 1+3+4 and 2+3+4. 6 ACKNOWLEDGEMENT The authors thank Dr. Borsaru M. and Zhou B. for useful discussion and suggestions. 7 REFERENCES Borsaru M., Rojc A. and Stehle R. : The application of the PGNAA technique to in-situ analysis of coal. Exploration and Mining Report 848C.,2001. Emilsson J. :Geophysical multi-parameter logging techniques applied to ore exploration in the Skellefte Field, MSc Thesis, Tekniska Hogskolan I Lulea, Sweden.,1993. Fullagar P. K., Zhou B. and Fallon G. N. :Automated interpretation of geophysical borehole loggs for orebody delineation and grade estimation. Mineral Resources Engineering, 8, 269 284.,1999. Fullagar, P., Zhou, B. and Biggs, M.: Automated geotechnical interpretation of geophysical logs: Presented at KEGS/MGLS Symposium, Toronto, 21-23, August.,2002.