Estimating reservoir zone from seismic reflection data using maximum-likelihood sparse spike inversion technique: a case study from the Blackfoot field (Alberta, Canada)

Satya Prakash Maurya,Nagendra Pratap Singh

doi:10.1007/s13202-018-0600-y

Satya Prakash Maurya, Nagendra Pratap Singh

Open Access

https://doi.org/10.1007/s13202-018-0600-y

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Abstract

Seismic inversion involves extracting qualitative as well as quantitative information from seismic reflection data that can be analyzed to enhance geological and geophysical interpretation which is more subtle in a traditional seismic data interpretation. Among many approaches that have been made to improve interpretation of post-stack seismic data, a great effort has been made to use maximum likelihood (ML), sparse spike inversion (SSI) along with multi-attribute analysis (MAA) aimed to increase the resolution power of interpreting seismic reflection data and mapping into the subsurface lithology. These methods are applied to the Blackfoot seismic reflection data to estimate reservoir. The methods were first applied to the composite trace close to well locations and were inverted for acoustic impedance (AI). The results depict that the inverted AI matches very well with the well log AI. The statistical analysis demonstrates good performance of the algorithm. Thereafter, the entire seismic section was inverted to acoustic impedance section. The analysis of the inverted impedance section shows an anomaly zone in between 1060 and 1075 ms time and characterize it as reservoir. Further, the multi-attribute analysis is performed to estimate porosity and density in the inter-well region. The inverted porosity section shows a high porosity anomaly and a low density anomaly in between 1060 and 1075 ms time intervals which corroborated well with the low impedance zone and confirm the presence of a reservoir.

Highlights

Seismic inversion is a procedure that helps extract the high-resolution subsurface model of the physical characteristics of rocks and fluids from low-resolution seismic reflection data with the integration of well log data (Krebs et al 2009)
The maximum-likelihood sparse spike inversion technique used the theory of maximum-likelihood deconvolution (MLD) which aims to generate reflectivity series from the seismic data (Russell 1988; Russell and Hampson 1991; Maurya and Sarkar 2016; Maurya et al 2018a, b)
The application of the MLD algorithm developed by Kormylo and Mendel to the real data is very complex and a modification was given by Hampson and Russell in 1985 which was easy to use

Summary

Introduction

Seismic inversion is a procedure that helps extract the high-resolution subsurface model of the physical characteristics of rocks and fluids from low-resolution seismic reflection data with the integration of well log data (Krebs et al 2009). The seismic inversion techniques have been widely used as a tool to locate hydrocarbon-bearing strata in the subsurface from the seismic data (Bosch et al 2010; Maurya and Singh 2015). The maximum-likelihood sparse spike inversion technique used the theory of maximum-likelihood deconvolution (MLD) which aims to generate reflectivity series from the seismic data (Russell 1988; Russell and Hampson 1991; Maurya and Sarkar 2016; Maurya et al 2018a, b). The application of the MLD algorithm developed by Kormylo and Mendel to the real data is very complex and a modification was given by Hampson and Russell in 1985 which was easy to use. The application of the MLD algorithm developed by Kormylo and Mendel to the real data is very complex and a modification was given by Hampson and Russell in 1985 which was easy to use. Hampson and Russell (1985) described that the MLD could be used to estimate reflectivity series from the broadband seismic reflection data and the reflectivity can be transformed to the acoustic impedance which is more meaningful

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