Changes

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[1]:  

# ''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>

# ''V_p/V_s'' (velocity ratio)= sensitive towards the change in fluid content even between wet and hydrocarbon bearing sandstones as Vp generally drops due to the presence of gas or oil however, the Vs does not affected much and therefore will produce low Vp/Vs. Velocity ratio is mathematically defined as follow:  

# ''V_p/V_s'' (velocity ratio)= sensitive towards the change in fluid content even between wet and hydrocarbon bearing sandstones as Vp generally drops due to the presence of gas or oil however, the Vs does not affected much and therefore will produce low Vp/Vs. Velocity ratio is mathematically defined as follow:  

V_p/V_s=V_p/V_s

::<math>V_p/V_s = \frac{V_p}{V_s}</math>

# ''LR'' (lambda-rho)= good fluid indicator as it is sensitive to the change in fluid content. Lambda (Lamé parameter) is defined as fluid incompressibility, where gas has the lowest LR as it is highly compressible and least dense, water has the highest LR, whereas oil sits in between. LR is mathematically defined as follow:  

# ''LR'' (lambda-rho)= good fluid indicator as it is sensitive to the change in fluid content. Lambda (Lamé parameter) is defined as fluid incompressibility, where gas has the lowest LR as it is highly compressible and least dense, water has the highest LR, whereas oil sits in between. LR is mathematically defined as follow:  

LR=〖(V_p*Rho)〗^2-〖2(V_s*Rho)〗^2=〖AI〗^2-2〖SI〗^2

::<math>LR =(V_p * Rho)^2 -2(V_s * Rho)^2 =AI^2 - 2SI^2</math>

# ''MR'' (mu-rho)= this property is sensitive towards the change in lithology. Sandstones generally have higher MR compared to shales and claystones due to its higher rigidity (μ). MR is mathematically defined as follow:  

# ''MR'' (mu-rho)= this property is sensitive towards the change in lithology. Sandstones generally have higher MR compared to shales and claystones due to its higher rigidity (μ). MR is mathematically defined as follow:  

MR=〖(V_s*Rho)〗^2=〖SI〗^2

::<math>MR = (V_s * Rho)^2 = SI^2</math>

 

as MR is strongly controlled by the shear impedance (SI) and shear modulus (μ), the magnitude of this property does not really affected by pore fluids, but rather by the lithology.  

as MR is strongly controlled by the shear impedance (SI) and shear modulus (μ), the magnitude of this property does not really affected by pore fluids, but rather by the lithology.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure1.png|thumbnail|Figure 1. Well log section showing the difference of rock physics properties between clean sandstone (Sand A), clayey sandstone (Sand B), and claystone. ]]

[[File:GeoWikiWriteOff2021-Muamamr-Figure1.png|thumbnail|Figure 1. Well log section showing the difference of rock physics properties between clean sandstone (Sand A), clayey sandstone (Sand B), and claystone. ]]

[[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[1], [4], [5].

==Predicting Reservoir’s Seismic Response==

==Predicting Reservoir’s Seismic Response==