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.
Highlights
The neural network approach is a well-known development tool, which became popular within the last couple of decades
Supervised and unsupervised trainable networks are used in many different fields of geology, especially petroleum geology or facies analysis e.g. the unsupervised network as a tool for lithofacies identification (CHANG et al, 2002), application of a supervised neural network for predicting permeability from porosity (ROGERS et al, 1995), using a supervised neural network tool in reservoir characterization (AHMED et al, 1997)
Traditional techniques to identify depositional environments from core data are difficult as they require a large dataset and/or are less precise than some results gained by using mathematical models for mapping
Summary
The neural network approach is a well-known development tool, which became popular within the last couple of decades. The main emphasis is given to the following issues: (1) Demonstration of how a known depositional geometry outlined by sand-content contours can be honored by using SOM analysis of points of three petrophysical grids. This analysis relies on good average porosity, net pore volume and hydraulic conductivity grids; (2) Description of an example of the application of this robust approach in re-recognizing the known distributary mouth bar shape in a particular Pannonian reservoir
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