Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging
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ORIGINAL RESEARCH PAPER
Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada S. P. Maurya1 · K. H. Singh2 · N. P. Singh1 Received: 10 November 2017 / Accepted: 18 May 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018
Abstract In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000–8000 m/s g/ cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region. Keywords Seismic inversion · Model-based inversion · Colored inversion · Single attribute analysis · Multi-attribute analysis · Probabilistic neural network
Introduction Geostatistical methods are routinely used for predicting various geophysical parameters from seismic and well log data. These methods uses colored inversion and model based inversion derived acoustic impedances as external attributes and seismic derived attributes as internal attributes for the geostatistical analysis (Doyen 1988). The inversion methods * N. P. Singh [email protected] S. P. Maurya [email protected] K. H. Singh [email protected] 1
Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, U.P. 221005, India
Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai 400076, India
2
are implemented on the post-stack seismic data from the Blackfoot field, Alberta, Canada. Seismic inversion is a technique which extracts models representing the petrophysical characteristics of the subsurface. The technique often uses seismic data along with well-log data to compute the petro-physical properties, viz. velocity, density, acoustic impedance, elastic impedance, and porosity (Chen and Sidney 1997). The seismic i
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