Subsalt Pore Pressure and Imaging Using Rock Physics Guided Velocity Modelling

2014 ◽  
Author(s):  
Y. Liu ◽  
D. Bhaskar ◽  
N.C. Dutta
Keyword(s):  
Pore Pressure ◽  
Rock Physics

scholarly journals Elastic characteristics of overpressure due to smectite-to-illite transition based on micromechanism analysis

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. WA23-WA42
Author(s):  
Xuan Qin ◽  
De-Hua Han ◽  
Luanxiao Zhao
Keyword(s):  
Pore Pressure ◽  
Rock Physics ◽  
Fluid Loss ◽  
Sonic Velocity ◽  
Modeling Framework ◽  
Evolution Characteristics ◽  
Rock Physics Modeling ◽  
Pore Pressure Prediction ◽  
Bulk Volume ◽  
Physics Modeling

Characterizing the elastic signatures of overpressure of shale caused by the smectite-to-illite transition relies on a good understanding of this mechanism and is also necessary for pore-pressure prediction. Methods of pore-pressure prediction in shales that have undergone smectite-to-illite transition are mostly based on empirical fitting without a quantitative interpretation based on a micromechanism analysis. With upscaled wireline-logging data, two trends of smectite-to-illite transition are categorized by using the crossplot of sonic traveltime and density. Trend I associated with a fluid-expansion scenario exhibits a decrease of sonic velocity with little change in the bulk density, whereas trend II induced by a fluid-loss scenario contains an increase of density with little change in the sonic velocity. The fluid expansion typically gives rise to high-magnitude overpressure and tends to happen when the overlying formations have more shaly contents and low permeability. The fluid loss case tends to have relatively deeper overpressure onsets, and its overlying formations tend to have more sandy contents with relatively high permeability. We develop a modeling framework to capture the elastic and pore-pressure evolution characteristics in shale during the smectite-to-illite transition. With proper bulk volume models, the velocity, density, and pore pressure increase of shale can be computed in the fluid expansion, fluid loss, and a mixture of these two scenarios. After calibration with logging data, rock-physics modeling can quantitatively interpret the rock-property evolution characteristics within the smectite-to-illite transition zone.

Seismic monitoring of a CO2 flood in a carbonate reservoir: A rock physics study

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1604-1617 ◽  
Author(s):  
Zhijing Wang ◽  
Michael E. Cates ◽  
Robert T. Langan
Keyword(s):  
High Resolution ◽  
Pore Pressure ◽  
Rock Physics ◽  
Fluid Substitution ◽  
High Porosity ◽  
High Permeability ◽  
Pressure Buildup ◽  
Reservoir Rocks ◽  
Velocity Changes

A carbon dioxide (CO2) injection pilot project is underway in Section 205 of the McElroy field, West Texas. High‐resolution crosswell seismic imaging surveys were conducted before and after CO2 flooding to monitor the CO2 flood process and map the flooded zones. The velocity changes observed by these time‐lapse surveys are typically on the order of −6%, with maximum values on the order of −10% in the vicinity of the injection well. These values generally agree with laboratory measurements if the effects of changing pore pressure are included. The observed dramatic compressional ([Formula: see text]) and shear ([Formula: see text]) velocity changes are considerably greater than we had initially predicted using the Gassmann (1951) fluid substitution analysis (Nolen‐Hoeksema et al., 1995) because we had assumed reservoir pressure would not change from survey to survey. However, the post‐CO2 reservoir pore fluid pressure was substantially higher than the original pore pressure. In addition, our original petrophysical data for dry and brine‐saturated reservoir rocks did not cover the range of pressures actually seen in the field. Therefore, we undertook a rock physics study of CO2 flooding in the laboratory, under the simulated McElroy pressures and temperature. Our results show that the combined effects of pore pressure buildup and fluid substitution caused by CO2 flooding make it petrophysically feasible to monitor the CO2 flood process and to map the flooded zones seismically. The measured data show that [Formula: see text] decreases from a minimum 3.0% to as high as 10.9%, while [Formula: see text] decreases from 3.3% to 9.5% as the reservoir rocks are flooded with CO2 under in‐situ conditions. Such [Formula: see text] and [Formula: see text] decreases, even if averaged over all the samples measured, are probably detectable by either crosswell or high‐resolution surface seismic imaging technologies. Our results show [Formula: see text] is sensitive to both the CO2 saturation and the pore pressure increase, but [Formula: see text] is particularly sensitive to the pore pressure increase. As a result, the combined [Formula: see text] and [Formula: see text] changes caused by the CO2 injection may be used, at least semiquantitatively, to separate CO2‐flooded zones with pore pressure buildup from those regions without pore pressure buildup or to separate CO2 zones from pressured‐up, non‐CO2 zones. Our laboratory results show that the largest [Formula: see text] and [Formula: see text] changes caused by CO2 injection are associated with high‐porosity, high‐permeability rocks. In other words, CO2 flooding and pore pressure buildup decrease [Formula: see text] and [Formula: see text] more in high‐porosity, high‐permeability samples. Therefore, it may be possible to delineate such high‐porosity, high‐permeability streaks seismically in situ. If the streaks are thick enough compared to seismic resolution, they can be identified by the larger [Formula: see text] or [Formula: see text] changes.

Improving hydrocarbon exploration with pore pressure assisted earth-model building

2018 ◽  
Vol 6 (3) ◽  
pp. SG41-SG47
Author(s):  
Yangjun (Kevin) Liu ◽  
Michael O’Briain ◽  
Cara Hunter ◽  
Laura Jones ◽  
Emmanuel Saragoussi
Keyword(s):  
Pore Pressure ◽  
Initial Velocity ◽  
Model Building ◽  
Rock Physics ◽  
Velocity Model ◽  
Hydrocarbon Exploration ◽  
Earth Model ◽  
Well Control ◽  
Physics Model ◽  
Rock Physics Model

In shale-dominated clastic lithology environments, a rock-physics model relating velocity and pore pressure (PP) can be calibrated and used to convert velocity to PP properties. The crossvalidation between velocity and overpressure, which follows the geology, can be used to better understand the model, help to build an initial velocity model, and allow selecting tomography solutions with more confidence. The velocity model developed using this approach is more plausible and more suitable for subsequent PP analysis. We highlight the application of this method in areas with poor seismic illumination and insufficient well control.

Rock Physics Of Pore Pressure

2001 ◽  
Author(s):  
Jack Dvorkin ◽  
Ali Mese
Keyword(s):  
Pore Pressure ◽  
Rock Physics

Pore-pressure-constrained, rock-physics-guided velocity model building method: Alternate solution to mitigate subsalt geohazard

2015 ◽  
Vol 3 (1) ◽  
pp. SE1-SE11 ◽  
Author(s):  
Nader Dutta ◽  
Bhaskar Deo ◽  
Yangjun (Kevin) Liu ◽  
Krishna Ramani ◽  
Jerry Kapoor ◽  
...  
Keyword(s):  
Pore Pressure ◽  
Model Building ◽  
Rock Physics ◽  
Velocity Model ◽  
Image Point ◽  
Earth Model ◽  
Velocity Modeling ◽  
Integrated Method ◽  
Fracture Pressure ◽  
Physics Model

We developed an integrated method that can better constrain subsalt tomography using geology, thermal history modeling, and rock-physics principles. This method, called rock-physics-guided velocity modeling for migration uses predicted pore pressure as a guide to improve the quality of the earth model. We first generated a rock-physics model that provided a range of plausible pore pressure that lies between hydrostatic (lowest possible pressure) and fracture pressure (highest possible pressure). The range of plausible pore pressures was then converted into a range of plausible depth varying velocities as a function of pore pressure that is consistent with geology and rock physics. Such a range of plausible velocities is called the rock-physics template. Such a template (constrained by geology) was then used to flatten the seismic gathers. We call this the pore-pressure scan technique. The outcome of the pore-pressure scan process was an “upper” and “lower” bound of pore pressure for a given earth model. Such velocity bounds were then used as constraints on the subsequent tomography, and further iterations were carried out. The integrated method not only flattened the common image point gathers but also limited the velocity field to its physically and geologically plausible range without well control; for example, in the study area it produced a better image and pore-pressure prognosis below salt. We determined that geologic control is essential, and we used it for stratigraphy, structure, and unconformity, etc. The method had several subsalt applications in the Gulf of Mexico and proved that subsalt pore pressure can be reliably predicted.

scholarly journals Deep Learning a Poro-Elastic Rock Physics Model for Pressure and Saturation Discrimination

Geophysics ◽  
2020 ◽  
pp. 1-55
Author(s):  
Wolfgang Weinzierl ◽  
Bernd Wiese
Keyword(s):  
Pore Pressure ◽  
Co2 Storage ◽  
Rock Physics ◽  
Seismic Attenuation ◽  
Physical Models ◽  
Physical Parameters ◽  
High Porosity ◽  
Hydrocarbon Production ◽  
Physical Attributes ◽  
Physics Model

Determining saturation and pore pressure is relevant for hydrocarbon production as well as natural gas and CO2 storage. In this context seismic methods provide spatially distributed data used to determine gas and fluid migration. A method is developed that allows to determine saturation and reservoir pressure from seismic data, more precisely from rock physical attributes that are velocity, attenuation and density. Two rock physical models based on Hertz-Mindlin-Gassmann and Biot-Gassmann are developed. Both generate poroelastic attributes from pore pressure, gas saturation and other rock-physical parameters. The rock physical models are inverted with deep neural networks to derive e.g. saturation, pore pressure and porosity from rock physical attributes. The method is demonstrated with a 65 m deep unconsolidated high porosity reservoir at the Svelvik ridge, Norway. Tests for the most suitable structure of the neural network are carried out. Saturation and pressure can be meaningfully determined under condition of a gas-free baseline with known pressure and data from an accurate seismic campaign, preferably cross-well seismic. Including seismic attenuation increases the accuracy. The training requires hours, predictions just a few seconds, allowing for rapid interpretation of seismic results.

Fluid-pressure discrimination in anisotropic reservoir rocks — A sensitivity study

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. O1-O11 ◽  
Author(s):  
Alexey Stovas ◽  
Martin Landrø
Keyword(s):  
Pore Pressure ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Fluid Pressure ◽  
Rock Physics ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Sand Layer ◽  
Weak Anisotropy ◽  
Fluid Saturation

We investigate how seismic anisotropy influences our ability to distinguish between various production-related effects from time-lapse seismic data. Based on rock physics models and ultrasonic core measurements, we estimate variations in PP and PS reflectivity at the top reservoir interface for fluid saturation and pore pressure changes. The tested scenarios include isotropic shale, weak anisotropic shale, and highly anisotropic shale layers overlaying either an isotropic reservoir sand layer or a weak anisotropic sand layer. We find that, for transverse isotropic media with a vertical symmetry axis (TIV), the effect of weak anisotropy in the cap rock does not lead to significant errors in, for instance, the simultaneous determination of pore-pressure and fluid-saturation changes. On the other hand, changes in seismic anisotropy within the reservoir rock (caused by, for instance, increased fracturing) might be detectable from time-lapse seismic data. A new method using exact expressions for PP and PS reflectivity, including TIV anisotropy, is used to determine pressure and saturation changes over production time. This method is assumed to be more accurate than previous methods.

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