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

Abstract

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.

A Comprehensive Evaluation of Shale Oil Reservoir Quality

“…As maturity increases, oil undergoes secondary cracking, yielding hydrocarbons with lower polarity, smaller molecular weights, and improved fluidity. Natural gamma radiation (GR) effectively identifies hydrocarbon source rocks, and within the context of organic source rock properties, both GR and thermal maturity (Ro) reflect hydrocarbon maturity, information obtainable from geological data [21]. Ro indicates the hydrocarbon generation potential of shale oil and is instrumental in evaluating its fluidity.…”

Source Rock Evaluation from Rock to Seismic Data: An Integrated Machine-Learning-Based Work Flow and Application in the Brazilian Presalt (Santos Basin)

“…Several authors have applied different methods to characterize the petrophysical and elastic properties of shales rich in organic matter and attempt to predict their occurrence and spatial variation using seismic data (e.g., [11][12][13]). The conventional seismic approach to source rock characterization is performed by calibrating linear regressions between acoustic impedance (P-impedance, the product of compressional wave velocity and density) and the TOC using well logs and extrapolating these relationships using seismic data to generate volumetric estimations of this property [8].…”