Original scientific paper

https://doi.org/10.4154/GC.2011.21

Defining depositional environment by using neural network

Janina Horvath ; Department of Geology and Paleontology, University of Szeged

Abstract

Traditional techniques of identification of a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. However, application of Kohonen’s Self Organized Map (SOM) approach may be regarded to be a potential method for pattern recognition problems. A combination of Indicator Kriging and SOM for log-porosity and sand content data coming from quantitative well-log interpretations is used for identifying the spatial pattern of some delta-plain sub-environments. The basic high-dimensional property fields are defined by 3D shapes of well known depositional facieses. Many parameters as log-porosity and sand content data can be used to determine geo-property as a lithological pattern using SOM. This step of method can discover spatial patterns as clusters in unstructured data set because SOM is based on clustering algorithm. However, this approach not necessarily makes sure, that the resulting disjunctive clusters can show any meaningful depositional geometry. So at last the final geometry is given using Indicator Kriging method, which uses threshold values derived from property values of clusters.<-->Traditional techniques to identify a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. The application of Kohonen’s Self Organized Map (SOM) approach may be useful for the interpretation of a depositional rock body through well-log data. SOM is based on a clustering algorithm and this method can be used to discover spatial patterns occurring as clusters in unstructured data sets. An example of the application of SOM is presented whereby clusters through SOM can indicate the contours of well-known depositional patterns such as sub-environments.

Keywords

clustering method; depositional environment; neural network; pattern recognition

Hrčak ID:

78316

URI

https://hrcak.srce.hr/78316

Publication date:

26.10.2011.

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