Iyan Haryanto
Faculty of Geological Engineering, Padjadjaran University

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Combining geological surface data and geostatistical model for Enhanced Subsurface geological model Muhammad Kurniawan Alfadli; Nanda Natasia; Iyan Haryanto
Journal of Geological Sciences and Applied Geology Vol 2, No 2 (2017): Journal of Geological Sciences and Applied Geology
Publisher : Faculty of Geological Engineering, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/gsag.v2i2.13451

Abstract

AbstractThis paper emphasis the use of geological information gathered from surface mapping and subsurface data, the method that used was mainly utilizing statistical information and deterministic model interpreted. Comparison model was built to calculate the deviation between realization, the calculation was furthered can be used as a uncertainty to make a better decision to choose the best realization. Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets. In geology, geostatistical is used for numerical calculation of subsurface approach with several data such as geophysical or geological observation. Geostatistics modelling have several parameters setup to obtain best approach model. The parameters namely lithological boundary, geological structure and random seed number. Process for modelling used 23 drilling data with various lithological type. Two type geostatistical method is applied for the data that is Indicator Kriging (IK) and Sequential Indicator Simulation (SIS) with grid size 1000x1000 and 2000x1500. Distribution of lithology begin without parameters. Resulted shown that the model not appropriate with geological surface data and mismatch lithology position. To obtained better model, several geological information is included before geostatistical calculation. From regional geology surface data is obtained geological formation within lithological information. Then, geological structure describes the geology fault and formation boundary.Keywords: Geostatistics, subsurface, enhanced model, variance, random seed number