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Integrated Fracture Characterization and Associated Error Evaluation Using Geophysical Data for Unconventional Reservoirs

Maity, Debotyam; Chen, Qiaosi; and Aminzadeh, Fred
[email protected]

This study discusses a new workflow for fracture characterization using microseismic and seismic data along with independent reservoir information (such as well log data). The framework is ideally suited for unconventional environments such as Monterey Shale where modern technologies such as the use of hydraulic fracturing and passive seismic monitoring allow application of the proposed workflow.

In this paper, we demonstrate how reservoir property estimates can be made from such studies by using an artificial neural network (ANN) based property modeling approach to independently combine the different properties estimated from passive seismic data (such as phase velocities and associated rock properties) as well as property maps obtained from conventional seismic data using attribute analysis. New fracture identifier properties have been defined and the models have been used to characterize fracture zones, reservoir connectivity and reservoir compartmentalization for a representative unconventional reservoir. Production/ injection trends have been used to validate the said observations. In order to circumvent the issue of data with multiple scales (low resolution passive and high resolution active data), Sequential Gaussian Simulation (SGS) has been used to improve the final property estimates and independently, error analysis has been carried out to better quantify the final results and enhance definition of the uncertainties in the analysis and interpretations made.


AAPG Search and Discovery Article #90162©2013 Pacific Section AAPG, SPE and SEPM Joint Technical Conference, Monterey, California, April 19-25, 2013