Advanced rock physics diagnostic: A new method for cement quantification

Hossain, Zakir; Newton, Paola

doi:10.1190/segam2013-0988.1

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…For a particular depth, the computed brine saturated V P , obtained from soft sand model, is plotted with porosity indicating a gentle increment in velocity values with reduction in porosity. This phenomenon generally mimics the values of the rock affected by either inferior sorting or non-contact cement (Avseth et al 2001;Hossain and MacGregor 2014).On the contrary, brine saturated V P values obtained from CC model and modified HS upper bound show relatively stiffer increase in velocity with reduced porosity and attributed to velocity change with cement deposition directly on grain surface ).…”

Section: T E X T U R E a N D C E M E N T A T I O N O F R O C Ksupporting

confidence: 53%

“…Distribution of data points on porosity -V P plane (Fig. 4b) exhibits a significant decrease in porosity with minimal increase in P-wave velocity; this behaviour can be attributed to porosity loss due to non-contact cementation (Hossain and MacGregor 2014). Clay pore-filling materials might prohibit CC deposition (Avseth et al 2001;Avseth et al 2009).…”

Section: Interpretation Of Rock Texture From Rock Physics Templatementioning

confidence: 95%

“…This phenomenon generally mimics the values of the rock affected by either inferior sorting or non‐contact cement (Avseth et al . ; Hossain and MacGregor ).On the contrary, brine saturated

V_{P}

values obtained from CC model and modified HS upper bound show relatively stiffer increase in velocity with reduced porosity and attributed to velocity change with cement deposition directly on grain surface (Mavko et al . ).…”

Section: Texture and Cementation Of Rockmentioning

confidence: 99%

“…) to infer microstructure of rocks. Hossain and MacGregor () have suggested a method to calculate pore‐filling and contact cement (CC) volume.…”

Section: Texture and Cementation Of Rockmentioning

confidence: 99%

“…Avseth et al (2009) have used diagnostic model (Dvorkin and Nur 1996;Avseth et al 2009) to infer microstructure of rocks. Hossain and MacGregor (2014) have suggested a method to calculate pore-filling and contact cement (CC) volume. Figure 3 demonstrates the methodology used for computing cement volume in this study.…”

Section: Pore-filling Materials Quantification and Distributionmentioning

confidence: 99%

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Quantification and spatial distribution of pore‐filling materials through constrained rock physics template and fluid response modelling in Paleogene clastic reservoir from Cauvery basin, India

Das

Chatterjee

Dasgupta

et al. 2018

Geophysical Prospecting

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Sands belonging to Kamalapuram Formation of Paleocene‐Eocene age are deposited in Cauvery basin as incised valley fill during a regressive cycle. Here we attempt to quantify the influence of diagenesis on pore‐filling materials using rock physics template constrained by geohistory modelling. Primarily, porosity–velocity and acoustic impedance – the ratio of P‐wave and S‐wave velocity (VP/Vs) cross‐plots are used as rock physics templates. Rock physics template has efficiently quantified pore‐filling materials namely; contact cement and non‐contact cement. The estimated contact cement and non‐contact cement are correlated with conventional petrophysical logs within the selected depth interval. Further, this correlation is used to interpret the composition of pore‐filling materials. Shallower depth intervals (I and II) exhibit moderate non‐contact cement (4–5%) and insignificant contact cement (1–2% approx.) depositions. However, deeper interval (III) records a significant amount of pore‐filling materials amounting average of 12% non‐contact cement and 4% contact cement. Pore‐filling materials demonstrate a positive correlation with the depth of burial. The fluid response is substantially affected by the degree of diagenesis, composition and spatial distribution of pore‐filling materials. Shallower depth intervals (1770–1786 m and 1858–1878 m) are relatively more sensitive to fluid changes as it is affected by insignificant contact cement. The depth interval 1770–1786 m shows class II (oil) and class III (gas) amplitude variation with offset anomalies. The sand occurring in depth interval 1858–1878 m demonstrates class IIP (oil) and II (gas) anomaly. The deeper interval (2118–2170 m) is comparatively stiffer and demonstrates class I amplitude variation with offset (oil and gas sand) anomaly.

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Section: T E X T U R E a N D C E M E N T A T I O N O F R O C Ksupporting

confidence: 53%

Section: Interpretation Of Rock Texture From Rock Physics Templatementioning

confidence: 95%

“…This phenomenon generally mimics the values of the rock affected by either inferior sorting or non‐contact cement (Avseth et al . ; Hossain and MacGregor ).On the contrary, brine saturated

V_{P}

Section: Texture and Cementation Of Rockmentioning

confidence: 99%

“…) to infer microstructure of rocks. Hossain and MacGregor () have suggested a method to calculate pore‐filling and contact cement (CC) volume.…”

Section: Texture and Cementation Of Rockmentioning

confidence: 99%

Section: Pore-filling Materials Quantification and Distributionmentioning

confidence: 99%

See 3 more Smart Citations

Quantification and spatial distribution of pore‐filling materials through constrained rock physics template and fluid response modelling in Paleogene clastic reservoir from Cauvery basin, India

Das

Chatterjee

Dasgupta

et al. 2018

Geophysical Prospecting

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Rock physics diagnostics and modelling of the Mangahewa Formation of the Maui B gas field, Taranaki Basin, offshore New Zealand

2022

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Rock-physics diagnostics of a turbidite oil reservoir offshore northwest Australia

Arévalo-López

Dvorkin

2017

GEOPHYSICS

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Interpreting seismic data for petrophysical rock properties requires a rock-physics model that links the petrophysical rock properties to the elastic properties, such as velocity and impedance. Such a model can only be established from controlled experiments in which both groups of rock properties are measured on the same samples. A prolific source of such data is wellbore measurements. We use data from four wells drilled through a clastic offshore oil reservoir to perform rock-physics diagnostics, i.e., to find a theoretical rock-physics model that quantitatively explains the measurements. Using the model, we correct questionable well curves. Moreover, a crucial purpose of rock-physics diagnostics is to go beyond the settings represented in the wells and understand the seismic signatures of rock properties varying in a wider range via forward seismic modeling. With this goal in mind, we use our model to generate synthetic seismic gathers from perturbational modeling to address “what-if” scenarios not present in the wells.

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Advanced rock physics diagnostic: A new method for cement quantification

Cited by 6 publications

References 9 publications

Quantification and spatial distribution of pore‐filling materials through constrained rock physics template and fluid response modelling in Paleogene clastic reservoir from Cauvery basin, India

Quantification and spatial distribution of pore‐filling materials through constrained rock physics template and fluid response modelling in Paleogene clastic reservoir from Cauvery basin, India

Rock physics diagnostics and modelling of the Mangahewa Formation of the Maui B gas field, Taranaki Basin, offshore New Zealand

Rock-physics diagnostics of a turbidite oil reservoir offshore northwest Australia

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