Evaluating abnormal pore pressure (overpressure zones) is a major safety issue in well drilling for oil and gas exploration. Nevertheless, presently, the problem of assessing the dependence of elastic moduli with pressure in unconsolidated media is still an open question even for the simple case of dry materials and packing of identical spherical grains. This is because the effective medium theory (EMT) based on the Hertz-Mindlin contact theory fails to predict the measured trend in laboratory data. To overcome this issue, we test a modified grain contact theory (GCT) model that fits elastic moduli with pressure proposed by Saul et al. (2013). We optimized this method by allowing a non linear relation between the calibration parameter and porosity and a simpler relation between pressure and porosity. We show that this model can predict the Vp and Vs pressure trends using published laboratory datasets by performing a blind test on Santa Cruz agglomerate samples.

Predicting the Pressure Dependence of Elastic Velocities of Dry Granular Assemblies Using a Modified GCT Model

Sajeva, A.
Methodology
;
Capaccioli, S.
Methodology
;
Mazzotti, A.
Supervision
2017-01-01

Abstract

Evaluating abnormal pore pressure (overpressure zones) is a major safety issue in well drilling for oil and gas exploration. Nevertheless, presently, the problem of assessing the dependence of elastic moduli with pressure in unconsolidated media is still an open question even for the simple case of dry materials and packing of identical spherical grains. This is because the effective medium theory (EMT) based on the Hertz-Mindlin contact theory fails to predict the measured trend in laboratory data. To overcome this issue, we test a modified grain contact theory (GCT) model that fits elastic moduli with pressure proposed by Saul et al. (2013). We optimized this method by allowing a non linear relation between the calibration parameter and porosity and a simpler relation between pressure and porosity. We show that this model can predict the Vp and Vs pressure trends using published laboratory datasets by performing a blind test on Santa Cruz agglomerate samples.
2017
978-94-6282-217-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/887069
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