Articles written in Journal of Earth System Science
Volume 130 All articles Published: 25 August 2021 Article ID 0176 Brief Communication
The characterization of the reservoir rock's geomechanical properties is critical to address wellbore instabilities and subsidence-related issues. To address these issues, lab-derived dynamic and static elastic properties are essential to match the
Volume 131 All articles Published: 18 February 2022 Article ID 0055 BriefCommunication
This paper describes a case study that converts pre-stack seismic data into meaningful rock properties by employing non-parametric probability density functions through a probabilistic modelling approach. This study used the simultaneous pre-stack inversion method to transform pre-stack seismic data into seismic attributes like compressional impedance, shear impedance, density, and V$_P$/V$_S$ ratio. Then cross plot analysis was conducted on selected wireline log data to identify reservoir lithofacies zones based on the ranges of properties like P-impedance and V$_P$/V$_S$ ratio. Hydrocarbon zone was identified with the range of V$_P$/V$_S$ ratio between 1.15 and 1.82 and ZP from 3800 to 12400 ((m/s) 9 (g/cc)).Water bearing sand zone was separated with V$_P$/V$_S$ ratio with 1.85–2.12 and ZP with 3500–14900 ((m/s)9 (g/cc)), and 3500–14900 ((m/s)9(g/cc)) of Z$_P$, V$_P$/V$_S$ ratio between 2.14 and 3.1 was used to characterize the shale zone. A non-parametric kernel density estimator is used on cross-plot data points to generate a probability density function for each lithofacies. These non-parametric PDFs were incorporated with seismic attributes using a probabilistic modelling approach based on Bayes’ classification to generate a lithofacies model. The application of methodology provides a better insight into predicting and discriminating lithofacies in the study area.
$\bullet$ Applied seismic inversion to obtain seismic elastic attributes such as compressional impedance (ZP), shear impedance (ZS), VP/VS ratio, and density.
$\bullet$ Shale, water-bearing zone, and hydrocarbon zone were identified using the cross plot analysis of well log data.
$\bullet$ Probability density functions (PDFs) for lithologies were generated on cross-plot space using the non-parameter statistical classification.
$\bullet$ Finally, hydrocarbon zones were identified using the Bayes' rule by combining the seismic data with PDFs.
Volume 131, 2022
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