Reservoir Characterization of an Unconventional Reservoir by Integrating Microseismic, Seismic, and Well Log Data

Author:

Maity D1,Aminzadeh F1

Affiliation:

1. University of Southern California

Abstract

Abstract Varied data types including geophysical data as well as well logs have been used frequently in the past to characterize reservoirs. However, the use of microseismic data as a potential source of useful information and its integration with conventional seismic data for reservoir characterization is an area of opportunity where properties predicted from the microseismic data can be used as a vital source of information which can then be tied with the overall characterization scheme in a seamless manner. In this paper we discuss the characterization scheme followed for an unconventional reservoir associated with a promising geothermal prospect. The field involves microseismic data acquisition being done continually as part of the field monitoring operations and extensive well control due to the presence of large number of injection and production wells and finally a 3D conventional seismic survey done to try and better define the reservoir. We have applied an integrated approach with these data sources in order to better characterize the reservoir in question using novel data analysis schemes where necessary to get optimum results. The approach shared in this paper can be applied to any type of reservoir setting with microseismic, seismic and well log data being available. What we present is a workflow to integrate these data types to generate useful property predictions including important rock property estimates with the aim of obtaining useable reservoir property maps to aid in reservoir development. This approach shows how a modest data acquisition program can still lead to useful characterization of reservoirs particularly with the inclusion of microseismic data in the workflow. We have used novel methods as part of our workflow including multi-attribute analysis, geostatistical techniques and soft computing techniques such as ANN1 based property prediction and mapping which are discussed in brief.

Publisher

SPE

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