Geomechanical and geometric seismic attributes in an interpretation workflow for characterization of unconventional reservoirs

Trinchero, Eduardo; Vernengo, Luis; Cardoso, Joaquín

doi:10.1190/tle32040386.1

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Overlaid on the TOC display is the most-positive-curvature attribute, using a transparency. Although the seismic signature of some of the bigger and more coherent lineaments (marked with magenta arrows) confirms them as faults, many of the consistent and weaker lineaments (indicated with orange arrows) have been interpreted as eolian events associated with megadunes (Cardoso et al, 2012;Trinchero et al, 2013).…”

Section: Characterization Of the Vaca Muerta Formationmentioning

confidence: 99%

Reducing uncertainty in characterization of Vaca Muerta Formation Shale with poststack seismic data

Sharma

Chopra

Vernengo³

et al. 2015

The Leading Edge

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The Late Jurassic-Early Cretaceous Vaca Muerta (VM) Formation in the Neuquén Basin has served as an important source rock for many of the conventional oil and gas fields in Argentina. With the interest in developing and exploiting shale resources in the country, many companies there have undertaken characterization of the VM Formation in terms of the elements of shale plays. Among other characteristics, shale plays can be identified based on the total-organic-carbon (TOC) content; better TOC leads to better production. However, there is no way of measuring it directly using seismic data, and it can be estimated only indirectly. Considering the influence of TOC on compressional and shear velocities and density, geoscientists have attempted to compute it using the linear or nonlinear relationship it might have with P-impedance. Understanding the uncertainty in using such a relationship for characterizing the VM Formation, a different approach has been followed for characterizing it. Because a linear relationship seems to exist between gamma ray (GR) and TOC, in addition to P-impedance, gamma ray is another parameter of interest for characterizing the VM Formation. Using P-impedance and GR volumes, a Bayesian classification approach has been followed to obtain a reservoir model with different facies based on TOC and the associated uncertainty with it. As the first step, different facies were defined, based on the cutoff values for GR and P-impedance computed from well-log data. Then Gaussian ellipses were used to capture the distribution of data in a crossplot of GR versus P-impedance. Next, 2D probability density functions (PDFs) were created from the ellipses for each of the facies. Combining these PDFs with GR and P-impedance volumes, different facies were identified on the 3D volume. Poststack model-based inversion was used to compute the P-impedance volume, and the probabilistic neural-network (PNN) approach was used to compute GR volume. Derived P-impedance and GR volumes correlated nicely at blind wells on the 3D volume, which gave confidence in the characterization of the VM Formation. An overlay of the discontinuity detail in terms of curvature lineaments on the determined TOC content at the level of interest helps in obtaining a more complete picture, which is useful for the planning of horizontal wells.

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Section: Characterization Of the Vaca Muerta Formationmentioning

confidence: 99%

Reducing uncertainty in characterization of Vaca Muerta Formation Shale with poststack seismic data

Sharma

Chopra

Vernengo³

et al. 2015

The Leading Edge

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overlaid on the TOC display is the most-positive curvature attribute using transparency. While the seismic signature of some of the bigger and coherent lineaments as marked with magenta arrows confirms them as faults, many of the consistent and weaker lineaments indicated with orange arrows have been interpreted as Aeolian events associated with mega dunes (Cardoso et al, 2012;Trinchero et al, 2013). The characteristics of the sedimentary events that deposited the basal part of the VM sequence have been studied based on the geology of the area as well as the well data.…”

Section: Claudio Sylwanmentioning

confidence: 99%

Reducing uncertainty in characterization of Vaca Mureta shale with post-stack seismic data

Sharma¹,

Chopra²,

Trinchero³

et al. 2015

SEG Technical Program Expanded Abstracts 2015

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The Late Jurassic-Early Cretaceous Vaca Muerta (VM) Formation in the Neuquén Basin has served as an important source rock for many of the conventional oil and gas fields in Argentina. With the interest in developing and exploiting the shale resources in the country, many companies there have undertaken the characterization of the VM Formation in terms of the elements of shale plays.Amongst other characteristics, shale plays can be identified based on the total organic carbon (TOC) content, as better TOC leads to the better production. However, there is no way of measuring TOC directly using seismic data, and it can only be estimated in an indirect way. Considering the influence of TOC on compressional, shear velocities and density, geoscientists have attempted to compute it using the linear or nonlinear relationship it may have with P-impedance. Understanding the uncertainty in using such a relationship for characterizing VM Formation, a different approach has been followed for characterizing it. As a linear relationship seems to exist between GR and TOC, in addition to P-impedance, gamma ray (GR) is another parameter of interest for characterizing the VM Formation.In this study, using P-impedance and GR volumes, a Bayesian classification approach has been followed to obtain a reservoir model with different facies based on TOC and the associated uncertainty with it. As the first step, we defined different facies based on the cutoff values for GR and P-impedance computed from well-log data. Having defined the different facies, Gaussian ellipses were used to capture the distribution of data in a cross-plot of GR versus P-impedance. Next, 2D probability density functions (PDF's) were created from the ellipses for each of the facies. Combining these PDF's with GR and P-impedance volumes, different facies were identified on the 3D volume. Post-stack modelbased inversion was used to compute the P-impedance volume while probabilistic neural network (PNN) approach was used to compute GR volume. Both derived P-impedance and GR volumes correlated well at blind wells on the 3D volume, which lent confidence in the characterization of VM Formation. An overlay of the discontinuity detail in terms of curvature lineaments on the determined TOC content at the level of interest helps in getting a more complete picture, which is helpful for the planning of horizontal wells.

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“…Other recent studies have shown ways of combining conventional seismic attributes with geometric and geomechanical attributes to characterize unconventional shale environments (Trinchero et al, 2013). Attribute integration methods can range from simple corendering techniques (Gupta et al, 2011) to complex neural networks-based property modeling approaches (Clifford and Aminzadeh, 2011).…”

Section: Introductionmentioning

confidence: 99%

Novel fracture zone identifier attribute using geophysical and well log data for unconventional reservoirs

Maity

Aminzadeh

2015

Interpretation

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We have characterized a promising geothermal prospect using an integrated approach involving microseismic monitoring data, well logs, and 3D surface seismic data. We have used seismic as well as microseismic data along with well logs to better predict the reservoir properties to try and analyze the reservoir for improved mapping of natural and induced fractures. We used microseismic-derived velocity models for geomechanical modeling and combined these geomechanical attributes with seismic and log-derived attributes for improved fracture characterization of an unconventional reservoir. We have developed a workflow to integrate these data to generate rock property estimates and identification of fracture zones within the reservoir. Specifically, we introduce a new "meta-attribute" that we call the hybrid-fracture zone-identifier attribute (FZI). The FZI makes use of elastic properties derived from microseismic as well as log-derived properties within an artificial neural network framework. Temporal analysis of microseismic data can help us understand the changes in the elastic properties with reservoir development. We demonstrate the value of using passive seismic data as a fracture zone identification tool despite issues with data quality.

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Geomechanical and geometric seismic attributes in an interpretation workflow for characterization of unconventional reservoirs

Cited by 5 publications

References 4 publications

Reducing uncertainty in characterization of Vaca Muerta Formation Shale with poststack seismic data

Reducing uncertainty in characterization of Vaca Muerta Formation Shale with poststack seismic data

Reducing uncertainty in characterization of Vaca Mureta shale with post-stack seismic data

Novel fracture zone identifier attribute using geophysical and well log data for unconventional reservoirs

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