Characterization of well logs using K-mean cluster analysis

Amjad Ali,Chen Sheng-Chang

doi:10.1007/s13202-020-00895-4

Amjad Ali, Chen Sheng-Chang

Open Access

https://doi.org/10.1007/s13202-020-00895-4

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Abstract

The identification process of different lithologies, hydrocarbons, and water-saturated zones in oil and gas industries involves petrophysical studies that are carried out by geoscientists using different software packages. This study aims to propose a method by integrating mean cluster analysis and well logs to identify dominant lithologies, pore fluids, and fluids contact. For this purpose, initially, K-mean cluster analysis is applied to density log and P-wave velocity data of three wells in order to group them into different clusters. Based on centroids of each cluster, different lithologies have been identified. The density log equation has been utilized to compute the porosity of each cluster, and the mean of each density log cluster is used as matrix density. Next, sonic log equation has been inverted to compute the fluid velocity and the mean of each P-wave velocity cluster is used as matrix velocity. For the fluid density, sonic and density log equations are jointly inverted to compute the fluid velocity of each cluster. The fluid bulk modulus and acoustic impedance are computed using fluid density and velocity. Based on the results of K-mean cluster analysis, different lithologies (shale, sandstone, and limestone) have been recognized successfully. In well-1, hydrocarbon and water-saturated zones are successfully identified and fluids contact has been established in the zone of interest. However, well-2 and well-3 did not show any indications of the presence of hydrocarbon in the respective zones.

Highlights

In the oil and gas exploration industry, generally, petrophysicists interpret well logs on the basis of their previous professional experience to identify hydrocarbon, water saturation zones, and fluids contact
Gutierrez et al (2000) used a rock physics model to identify pore fluids from the sonic log and Mukerji et al (2001) integrated rock physics and seismic data through geostatistics in order to reduce the uncertainty of seismic reservoir characterization while Mark Sams (2001) used the geostatistical inversion for lithological and impedance modeling for reservoir characterization
The porosity, fluid velocity, fluid bulk modulus, and AI are computed in order to identify main lithologies, a potential hydrocarbon reservoir, pore fluids, and fluids contact

Summary

Introduction

In the oil and gas exploration industry, generally, petrophysicists interpret well logs on the basis of their previous professional experience to identify hydrocarbon, water saturation zones, and fluids contact. The characterization of pore fluids and fluids contact in a reservoir is very important for volumetric computation of reserves estimation in a hydrocarbon reservoir (Chombart 1960). Gutierrez et al (2000) used a rock physics model to identify pore fluids from the sonic log and Mukerji et al (2001) integrated rock physics and seismic data through geostatistics in order to reduce the uncertainty of seismic reservoir characterization while Mark Sams (2001) used the geostatistical inversion for lithological and impedance modeling for reservoir characterization.

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