Paper: USING MAPINFO VERTICAL MAPPER INTERPOLATION TECHNIQUES FOR ESTONIAN OIL SHALE RESERVE CALCULATIONS

Paper: Valgma: USING MAPINFO VERTICAL MAPPER INTERPOLATION TECHNIQUES FOR ESTONIAN OIL SHALE RESERVE CALCULATIONS
USING MAPINFO VERTICAL MAPPER INTERPOLATION TECHNIQUES FOR ESTONIAN OIL SHALE RESERVE CALCULATIONS INGO VALGMA Summary A digital map of Estonian oil shale mining was created for joining data of technological, environmental, and social limitations in the deposit with area of 2900 km2. For evaluating potential resource of oil shale its amount, tonnage and energy were calculated. Then the quantity of economical oil shale for power plants and shale oil resource were calculated. Seam thickness was interpolated from analyzed and calculated data points of the oil shale seam and a model was created for visual control. Calculating and interpolating energy rating per square metre is necessary for energy resource evaluation. Energy rating is the most important factor for determining oil shale reserve in the case of using it for electricity generation. In the case of oil production, figures of oil yield and resource in oil shale are the most important figures for determining the value of the deposit. Basing on the models, oil resource has been calculated. Choosing between various interpolations showed that Inverse Distance Weighting gives the most reliable figures for oil shale tonnage with available data. Natural Neighbor Regions and Kriging are the more suitable methods when using detailed data. Introduction Oil shale bed in Estonia is deposited in the depth of 0…100 m with the thickness of 1.4…3.2 m in the area of 2884 km2 (Figure 1). The mineable seam consists of seven kukersite layers and four to six limestone interlayers. The layers are named A…F1. The energy rating of the bed is 15…45 GJ/m2. Figure 1 Baltic oil shale area Oil shale mining in Estonia province started in 1916, it was extracted in surface mines using the opencast method. Underground mining started in 1922. Room-and-pillar mining, which is the only underground oil shale mining method today, started in 1960. During the years 1971…2001 longwall mining with shearers was used in some of the mines. In total ten oil shale surface mines and 13 underground mines have been in operation. Probability of opening new operations is high in the case if oil shale processing or cement productions are becoming more active. The criteria of the oil shale reserve The criteria of the oil shale reserve are energy rating, calorific value of the layers, thickness and depth of the seam, location, available mining technology, world price of competitive fuel and its transporting cost, oil shale mining and transporting cost. In addition, nature protection areas are limiting factors for mining. The economic criterion for determining Estonia’s kukersite* oil shale reserve for electricity generation is the energy rating of the seam in GJ/m2. It is calculated as the sum of the products of thickness, calorific values and densities of all oil shale layers A-F1 and limestone interlayers. A reserve is mineable when energy rating of the block is at least 35 GJ/m2 and subeconomic if energy rating is 25…35 GJ/m2. According to the Balance of Estonian Natural Resources, as an example the oil shale reserve was 5 billion tonnes in the year 2000. Economic reserve was 1.5 billion t and subeconomic 3.5 billion t (Table 1). These figures apply to oil shale usage for electricity generation in power plants and are calculated only by oil shale layers, which is fiction because in most cases total bed is used for combustion. In the case of wide-scale using of oil shale for cement or oil production, the criteria must be changed. Modeling In total 375 data points have been used for modeling (Figure 2). Every data point represents data of one reserve block with 5 to 15 drill holes and has been taken as an average for an area about seven km2. Figure 2 Map of source data from geological investigation. Contours show exploration fields. The northern border of the deposit is an outcrop line Basing on geological data, several grids of oil shale layers and seam were created - those of the height, thickness, in-place tonnage, energy rating, overburden thickness and stripping coefficient. Oil shale resource For evaluating potential resource of oil shale, its amount, tonnage and energy must be calculated. Then the quantity of economical oil shale for power plants and shale oil resource is calculated. It is necessary to follow the criteria for choosing the most appropriate interpolation method. It is not possible to choose right interpolation method beforehand because of numerous options of methods and criteria. Appropriate methods for every operation will be established during calculations. Seam thickness was interpolated from 375 calculated data points of the oil shale seam and a model was created for visual control. Assuming that Estonian oil shale deposit is a combination of current exploration fields, the polygon shown in Figure 3 marks the deposit border covering 2884 km2. The quantity of the oil shale seam could be calculated by extracting thickness and corresponding areas from the created isopachs using SQL query. Inverse distance weighting, triangulation with smoothing, natural neighbor, rectangular interpolation and Kriging methods could be used for interpolation. Reliable data allow to use Voronoi diagram, triangulation and rectangular interpolation. Unreliable data require Inverse Distance Weighting and Kriging. All criteria are summarized in Table 2. Shaded rows show suitable criteria for calculating oil shale tonnage. Natural neighbor is the most suitable method because the initial data were subjectively interpreted average values of exploration blocks. Inverse Distance Weighting Interpolation can also be used because the errors of the initial data exceed smoothing level of the method. Smoothed isopleths give a more clear overview of the deposit and allow extrapolate the data beyond the data points. Table 2 Criteria for choosing interpolation methods. Marks show suitable methods for specific data. Shaded rows show suitable criteria for calculating oil shale tonnage Calculating and interpolating energy rating per square meter is necessary for energy resource evaluation. The graph based on the model (Figure 3) shows the energy ranges of mined oil shale in the deposit, its quantity already mined out and the available resource. Most of mined out oil shale seam has had energy rating more than 35 GJ/m2 (Figure 4). Energy rating of the seam is used as an essential argument for economical calculations. Figure 3 Energy rating of oil shale, GJ/m2. The resource over 35 GJ/m2 is economic reserve. Bold conurs in north show mined out area. Figure 4 Oil shale resource in Estonian deposit on PJ. Most of the mined out seam has contained energy more than 35 GJ/m2 Models enable to present the graphs of energy rating showing its extent and ranges. Energy rating is the most important factor for determining oil shale reserve in the case of using it for electricity generation. In the case of oil production, figures of oil yield and resource in oil shale are the most important figures for determining the value of the deposit. Basing on the models, oil resource has been calculated and is presented in Figure 5. Figure 5 Shale oil resource, Th t in ranges of oil yield Choosing between various interpolation methods showed that Inverse Distance Weighting (IDW) gives the most reliable figures for oil shale tonnage with available initial data. Natural Neighbor regions (NN) and Kriging are the more suitable interpolation methods when using more detailed data and blocks. Rectangular Interpolation is unsuitable in the case of irregular mesh of the initial data. All methods give 2,3m for average oil shale thickness except rect. interpolation that gives 2,1m and 11% deviation. Other methods give deviation from 1 to 3 %, which are smaller than errors of initial data. Results of the study The main results are data models of the deposit such as isopleths and 3D drawings of mining conditions like seam thickness, seam depth, calorific value of oil shale and oil yield. The main possible calculations basing on oil shale bedding models are choosing optimum mining location, dynamics and statistics of the mining phenomenon, evaluation of oil shale quantity, energy and oil reserve. Inverse Distance Weighting method gives satisfactory data for the deposit overview (Table 3), other suitable methods can be used for modeling with all initial drill hole data in case of resource calculations in specific location. Table 3 Data of Estonian oil shale deposit. Calculated with inverse distance weighting method. This study is financed by EstSF Grant No. 4870 Oil Shale Resource. References References and additional information on Estonian oil shale can be found on page http://www.ttu.ee/maeinst/mgis 1. Valgma I. Geographical Information System for Oil Shale Mining - MGIS. Thesis on Mining Engineering. http://www.ttu.ee/maeinst/mgis Tallinn Technical University. 2002 2. Valgma, I., Map of oil shale mining history in Estonia, Proc. II. 5th Mining History Congress, Greece, Milos Conference Centre- George Eliopoulos, 2001, 198…193 3. Valgma I. Mapping potential areas of ground subsidence in Estonian underground oil shale mining district. Proceedings of the 2nd International Conference “Environment. Technology. Resources”. Rezekne, Latvia 25-27 June 1999, 227...232 4. Valgma I. Post-stripping processes and the landscape of mined areas in Estonian oil shale open-casts. Oil Shale, 2000, Vol. 17, No. 2, 201…212 5. Valgma I. Using MapInfo Professional and Vertical Mapper for mapping Estonian oil shale deposit and analysing technological limit of overburden thickness. Proceedings of International Conference on GIS for Earth Science Applications, Institute for Geology, Geotechnics and Geophysics, Slovenia Ljubljana 17.-21. May 1998, 187…194 * In addition to kukersite oil shale in Estonia, there are occurrences of another kind of oil shale – dictyonema argillite, mined and used in Sillamäe for extracting uranium in 1948…1953.

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