Semi-Automatic Facies Up-scaling Technique for Litho-Seismic Classification - Application to a field located in Western Offshore Africa
S. Sengupta, T. Cadoret, F. Pivot and G. Federle
Event name: IPTC 2014: International Petroleum Technology Conference
Session: Session 31: E&P GEOSCIENCE - Seismic Reservoir Architecture
Publication date: 19 January 2014
Info: Extended abstract, PDF ( 1.08Mb )
Price: € 20
A classical objective of reservoir characterization using seismic attributes is to provide a lithology-related attributes that may be used as an aid for interpretation and ultimately as an input for the geological model building. For such an objective, supervised classification techniques take advantage of well data in order to constrain the learning phase of the classification methodology. The output can be pseudo-petrophysical cubes or facies probability cubes. Total has developed an internal tool which enables conversion of inverted seismic attributes (such as acoustic impedance and Poisson’s ratio) into ‘lithocubes’ describing the probability to find some selected lithologies. These lithocubes are seismic attributes and hence have the same resolution as the seismic. In order to achieve proper on supervised classification it is essential to have the facies description compatible in terms of scale with the seismic attributes cubes. This up-scaling can be performed qualitatively without implicit use of the petro-elastic quantitative information such as Vp, Vs and density. Moreover, there could be significant variability in the up-scaling results according to the choices made by the operator performing a manual up-scaling (facies grouping and minimum thickness of each layer corresponding to a given facies for example). This paper describes a semi-automatic workflow capable of limiting the subjective influence of the operator on the final up-scaled well facies. By using quantitative criteria based on the petroelastic behavior of each facies it is possible to help the operator to achieve objective up-scaling choices. Ultimately, this workflow allows to improve the reliability of classified lithocubes.