Sponsored by Apache Corporation
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By utilizing a three-dimensional microseismic sensor network around target volumes undergoing multistage stimulations, and carrying out Seismic Moment Tensor Inversion (SMTI) and source behaviour derived analyses, it is possible to construct the Discrete Fracture Network (DFN). The generated DFN provides insight on the fracture orientations, failure mechanisms and their rupture characteristics. Specifically, we can identify differences in the DFN behaviour by utilizing topological node and branch counting approaches in conjunction with volumetric scanlines. Our observations, based on a sample data set as recorded in a North American shale play, utilizing an extended recording network, coupled with quantification of the derived fracture plane orientations, provides a basis for understanding the development of the DFN. Utilizing topological approaches, the network complexity can be established volumetrically and used to identify zones for enhancement in production. The behaviour of these zones are reflected in the branch behaviour that can lead to an increase in percolation leading to enhanced flow. Additionally, the derived DFN and its growth appear to be related to the ability to stimulate bedding planes. By so doing, there is an enhancement in activated surface area as well as an increase in containment which further can lead to production. Our results suggest that these zones can be sub-divided into primary, secondary and non-stimulated or a non-contributing zone of isolated fractures as suggested by hybrid analytical models based on trilinear flow. Overall, the use of volumetric scanlines and topology provides an opportunity to identify the appropriate reservoir model to describe the DFN behaviour, constrain reservoir models, and in Rate Transient Analysis (RTA).
Speaker Biography: Ted Urbancic, ESG Solutions
Dr. Ted Urbancic, a founder and current Chief Technology Officer at ESG Solutions, has over 30 years of experience examining and interpreting seismicity associated with mining and petroleum applications. He is a pioneer in the development of passive seismic monitoring in industrial applications, authoring over 150 publications ranging from understanding the fundamental aspects of seismicity to characterizing rock and reservoir behavior by integrating passive seismic data with numerical modeling, engineering and geomechanical data, and has received top 10 paper recognition on multiple occasions from the AAPG. Over the past 15 years, Ted has been integral in building ESG’s passive seismic hydraulic fracture monitoring capabilities and in promoting passive seismic tools for enhanced reservoir characterization. Currently, Ted oversees work by ESG’s Innovation and Technology Group, which spearheads all R&D related to passive seismic analysis, integration of new technologies, enhancement of geomechanical and reservoir/mine management systems in the mining and oil & gas sectors. Ted holds a Ph.D. in Seismology from Queen’s University, Kingston Canada and currently also holds an Adjunct Professorship in the Department of Geological Sciences. He is a member of numerous professional societies, including AAPG, AGU, CSEG, EAGE, SEG, SPE, SSA, and on the organizing committees for the 2014, 2016, and 2018 EAGE Passive Seismic Workshops.