Changes

Reservoirs have distinctively different elastic properties compared to the non-reservoirs (shales, claystones, wet sands, etc.). An example from clastic gas reservoir is shown on Figure 1, where Sand A (clean sandstone) and Sand B (clayey sandstone as indicated by gamma ray log) have different density and wave velocities compared to the claystones, even Sand A and Sand B have different properties due to the presence of clays in Sand B.

Reservoirs have distinctively different elastic properties compared to the non-reservoirs (shales, claystones, wet sands, etc.). An example from clastic gas reservoir is shown on Figure 1, where Sand A (clean sandstone) and Sand B (clayey sandstone as indicated by gamma ray log) have different density and wave velocities compared to the claystones, even Sand A and Sand B have different properties due to the presence of clays in Sand B.

   

   

In order to thoroughly discriminate the reservoir and non-reservoir lithology, the dataset should be derived into but not limited to the following properties[1]:  

In order to thoroughly discriminate the reservoir and non-reservoir lithology, the dataset should be derived into but not limited to the following properties:<ref name="1Goodwayetal">Goodway, B., T. Chen, and J. Downton, 1997, Improved AVO Fluid Detection and Lithology Discrimination using Lamé Petrophysical Parameters; “λρ”, “μρ”, and “λ/μ fluid stack”, from P and S Inversions: SEG Technical Program Expanded Abstracts, p. 183-186.</ref>

# ''AI'' (acoustic impedance) = this property is sensitive towards the change in lithology and fluid content. Gas has the lowest AI as it is acoustically softer compared to water, whereas oil sits in between. As this property is directly affected by the change in compaction and/or diagenetic processes, its sensitivity decreases under specific circumstances and may be unable to differentiate between each lithology.  

# ''AI'' (acoustic impedance) = this property is sensitive towards the change in lithology and fluid content. Gas has the lowest AI as it is acoustically softer compared to water, whereas oil sits in between. As this property is directly affected by the change in compaction and/or diagenetic processes, its sensitivity decreases under specific circumstances and may be unable to differentiate between each lithology.  

::<math>AI = V_p * Rho</math>

::<math>AI = V_p * Rho</math>

[[File:GeoWikiWriteOff2021-Muamamr-Figure4.png|thumbnail|Figure 4. LR-MR crossplot of the utilized dataset.]]   

[[File:GeoWikiWriteOff2021-Muamamr-Figure4.png|thumbnail|Figure 4. LR-MR crossplot of the utilized dataset.]]   

These results suggest the validity of rock physics analysis to identify the presence of reservoir, where it successfully discriminate between Sand A, Sand B, wet sands and claystones. It should be noted that each property has their own functions and limitations. These crossplots can be utilized to determine the rock physics cut-off parameter for each lithology and later applied as a quality control for more advance geophysical methods such as seismic inversion[1], [4], [5].

These results suggest the validity of rock physics analysis to identify the presence of reservoir, where it successfully discriminate between Sand A, Sand B, wet sands and claystones. It should be noted that each property has their own functions and limitations. These crossplots can be utilized to determine the rock physics cut-off parameter for each lithology and later applied as a quality control for more advance geophysical methods such as seismic inversion.<ref name="1Goodwayetal" />[4], [5].

==Predicting Reservoir’s Seismic Response==

==Predicting Reservoir’s Seismic Response==

==References==

==References==

2. Gassmann, F., 1951, Elastic Waves Through a Packing of Spheres, Geophysics, 16(4), pp. 673-685.  

2. Gassmann, F., 1951, Elastic Waves Through a Packing of Spheres, Geophysics, 16(4), pp. 673-685.