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. Maurya
  • K. H. Singh
  • N. P. Singh
Original Research Paper


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.


Seismic inversion Model-based inversion Colored inversion Single attribute analysis Multi-attribute analysis Probabilistic neural network 



One of the author (S.P. Maurya) is indebted to Science and Engineering Research Board, Department of Science and Technology, New Delhi for financial supports in form of research project (Grant No. PDF/2016/000888) under National Post-doctoral Fellowship scheme. Authors also acknowledge the CGG Veritas for providing seismic and well log data of Blackfoot field, Alberta, Canada.


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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Geophysics, Institute of ScienceBanaras Hindu UniversityVaranasiIndia
  2. 2.Department of Earth SciencesIndian Institute of Technology BombayMumbaiIndia

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