Litho - fluid prediction
The discrimination of fluid content and lithology in a reservoir is an important characterization that has a bearing on reservoir development and its management. Following are different approaches that can be used for identifying lithology and fluid content of a reservoir.
Rock physics analysis
where rock physics template is used for delineation of litho and fluid trends.
Crossplot between P-impedance and VP-VS ratio for data from Atlantis well, and for the interval between the Stø and Kobbe markers, with a rock physics template overlaid on it. The cluster of points with low impedance and low VP-VS ratio exhibits high porosity and high gas saturation, and are enclosed in red and purple polygons. Their back projection on to the well log curves is shown on right, and they highlight the intervals between Stø and Kobbe markers. (Adapted from Chopra et al. (2017); Data courtesy of TGS, Asker)
Crossplot between P-impedance and VP-VS ratio derived from seismic prestack simultaneous impedance inversion before frequency enhancement, and for the interval between the Stø and Kobbe markers. The same rock physics template shown in (a) is overlaid on it. The cluster of points exhibit an overall shape similar to the cluster we see for well data, but they do not seem scattered enough within the red and purple polygons Thereafter, the back projection of different polygons on the seismic section(shown below) facilitates us to map individual facies on 3D seismic data. (Adapted from Chopra et al. (2017); Data courtesy of TGS, Asker)
Petro-physical approach
Petro-physical properties such as Vclay, Sw, porosity are derived first using well-log data, thereafter, attempts are made to derive them from seismic data. For doing so, first step should be generating cross-plot matrix using well-log data. In the cross-plot matrix analysis, we generate several cross-plots of different attributes that can be derived seismically color-coded with petro-physical properties for finding out the best combination of attributes that can be used to differentiate between lithology and fluid content of a reservoir.
Cross-plot matrix generated using well-log data showing several cross-plots of different seismically derived attributes color-coded with petro-physical properties
Alternatively, petro-physical properties can be estimated via extended elastic impedance approach or multi-attribute analysis performed on attributes derived using pre-stack simultaneous inversion and can be used for facies classification as shown below:
(Adapted from Chopra et al., 2020)
Spatial distribution of different facies at the level of interest (Adapted from Chopra et al., 2020)
Challenges in estimation of lithology and fluid content of unconventional shale plays
While in rock physics analysis elastic response is modelled using mineral fractions, water saturation and porosity, there are huge uncertainties in their estimation for unconventional plays. Shear sonic curve required for modelling is not available mostly. Therefore, an uncalibrated rock-physics model in the complex environment will lead to large uncertainties in calculated properties.
A statistical approach of lithology and fluid content of unconventional shale plays
A crossplot of neutron-porosity and density-porosity allows us to extract Vsh and effective porosity. Considering the uncertainties associated with their estimation such a crossplot can be used to define different facies as highlighted below.
(Adapted from Sharma et. al., 2020)
Authentication
(Adapted from Sharma et. al., 2020)
Application on seismic data
(Adapted from Sharma et. al., 2020)
(Adapted from Sharma et. al., 2020)
References
Alaskari, G.M.K and A. Roozmeh, 2017, Determination of shale types using well logs, Int J of Petrochem Sci and Eng., 2 (5), 274-280.
Chopra, S., R. K. Sharma, G. K. Grech, and B. E. Kjølhamar, 2017, Characterization of shallow high-amplitude seismic anomalies in the Hoop Fault Complex, Barents Sea, Interpretation, Vol. 5, No. 4 (November 2017); p. T607–T622.
Chopra, S., R. K. Sharma, M. Trulsvik, A.C. Ramirez, D. Went and B. Kjolhamar, 2020, Reservoir characterization over Lille Prinsen and Ivar Aasen fields in the Norwegian North Sea using OBN seismic data – a case study, submitted for publication in Interpretation.
Sharma, R. K., S. Chopra, and L. R. Lines, 2020, Seismic reservoir characterization of Bone Spring and Wolfcamp formations in the Delaware Basin: challenges and uncertainty in characterization using rock physics – a case study: Part-2, published in Interpretation, T1057-T1069.