scholarly journals Probabilistic approach to rock physics modeling

Geophysics ◽  
2014 ◽  
Vol 79 (2) ◽  
pp. D123-D143 ◽  
Author(s):  
Dario Grana
Keyword(s):  
Probability Distribution ◽  
Reservoir Characterization ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Rock Physics ◽  
Random Variable ◽  
Rock Properties ◽  
Fluid Saturation ◽  
Rock Physics Modeling ◽  
Physics Modeling

Rock physics modeling aims to provide a link between rock properties, such as porosity, lithology, and fluid saturation, and elastic attributes, such as velocities or impedances. These models are then used in quantitative seismic interpretation and reservoir characterization. However, most of the geophysical measurements are uncertain; therefore, rock physics equations must be combined with mathematical tools to account for the uncertainty in the data. We combined probability theory with rock physics modeling to make predictions of elastic properties using probability distributions rather than definite values. The method provided analytical solutions of rock physics models in which the input is a random variable whose exact value is unknown but whose probability distribution is known. The probability distribution derived with this approach can be used to quantify the uncertainty in rock physics model predictions and in rock property estimation from seismic attributes. Examples of fluid substitution and rock physics modeling were studied to illustrate the application of the method.

scholarly journals Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description

2020 ◽  
Vol 70 (1) ◽  
pp. 209-220
Author(s):  
Qazi Sohail Imran ◽  
◽  
Numair Ahmad Siddiqui ◽  
Abdul Halim Abdul Latif ◽  
Yasir Bashir ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Depositional Environments ◽  
Well Log ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
And Performance ◽  
Well Log Analysis

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

Accuracy and effectiveness of three-dimensional controlled source electromagnetic data inversions

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. E83-E95
Author(s):  
Richard T. Houck ◽  
Adrian Ciucivara ◽  
Scott Hornbostel
Keyword(s):  
Three Dimensional ◽  
Rock Physics ◽  
Hydrocarbon Reservoirs ◽  
Rock Properties ◽  
Data Sets ◽  
Controlled Source ◽  
Electromagnetic Data ◽  
Fluid Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling

Unconstrained 3D inversion of marine controlled source electromagnetic data (CSEM) data sets produces resistivity volumes that have an uncertain relationship to the true subsurface resistivity at the scale of typical hydrocarbon reservoirs. Furthermore, CSEM-scale resistivity is an ambiguous indicator of hydrocarbon presence; not all resistivity anomalies are caused by hydrocarbon reservoirs, and not all hydrocarbon reservoirs produce a distinct resistivity anomaly. We have developed a method for quantifying the effectiveness of resistivities from CSEM inversion in detecting hydrocarbon reservoirs. Our approach uses probabilistic rock-physics modeling to update information from a preexisting prospect assessment, based on uncertain resistivities from CSEM. The result is an estimate the probability of hydrocarbon presence that accounts for uncertainty in the resistivity and in rock properties. Examples using synthetic and real CSEM data sets demonstrate that the effectiveness of CSEM inversion in identifying hydrocarbon reservoirs depends on the interaction between the uncertainty associated with the inversion-derived resistivity and the range of rock and fluid properties that were expected for the targeted prospect. Resistivity uncertainty that has a small effect on hydrocarbon probability for one set of rock property distributions may have a large effect for a different set of rock properties. Depending on the consequences of this interaction, resistivities from CSEM inversion might reduce the risk associated with predictions of hydrocarbon presence, but they cannot be expected to guarantee a specific well outcome.

scholarly journals Seismic characterization of geothermal sedimentary reservoirs: A field example from the Copenhagen area, Denmark

2020 ◽  
Vol 8 (2) ◽  
pp. T275-T291 ◽  
Author(s):  
Kenneth Bredesen ◽  
Esben Dalgaard ◽  
Anders Mathiesen ◽  
Rasmus Rasmussen ◽  
Niels Balling
Keyword(s):  
Reservoir Characterization ◽  
Energy Resource ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Amplitude Variation With Offset ◽  
Seismic Characterization ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Physics Model ◽  
Rock Physics Model

We have seismically characterized a Triassic-Jurassic deep geothermal sandstone reservoir north of Copenhagen, onshore Denmark. A suite of regional geophysical measurements, including prestack seismic data and well logs, was integrated with geologic information to obtain facies and reservoir property predictions in a Bayesian framework. The applied workflow combined a facies-dependent calibrated rock-physics model with a simultaneous amplitude-variation-with-offset seismic inversion. The results suggest that certain sandstone distributions are potential aquifers within the target interval, which appear reasonable based on the geologic properties. However, prediction accuracy suffers from a restricted data foundation and should, therefore, only be considered as an indicator of potential aquifers. Despite these issues, the results demonstrate new possibilities for future seismic reservoir characterization and rock-physics modeling for exploration purposes, derisking, and the exploitation of geothermal energy as a green and sustainable energy resource.

Rock-physics diagnostics of an offshore gas field

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. MR121-MR132 ◽  
Author(s):  
Uri Wollner ◽  
Yunfei Yang ◽  
Jack P. Dvorkin
Keyword(s):  
Rock Physics ◽  
Rock Properties ◽  
Gas Field ◽  
Amplitude Variation With Offset ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Using Data ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model

Seismic reflections depend on the contrasts of the elastic properties of the subsurface and their 3D geometry. As a result, interpreting seismic data for petrophysical rock properties requires a theoretical rock-physics model that links the seismic response to a rock’s velocity and density. Such a model is based on controlled experiments in which the petrophysical and elastic rock properties are measured on the same samples, such as in the wellbore. Using data from three wells drilled through a clastic offshore gas reservoir, we establish a theoretical rock-physics model that quantitatively explains these data. The modeling is based on the assumption that only three minerals are present: quartz, clay, and feldspar. To have a single rock-physics transform to quantify the well data in the entire intervals under examination in all three wells, we introduced field-specific elastic moduli for the clay. We then used the model to correct the measured shear-wave velocity because it appeared to be unreasonably low. The resulting model-derived Poisson’s ratio is much smaller than the measured ratio, especially in the reservoir. The associated synthetic amplitude variation with offset response appears to be consistent with the recorded seismic angle stacks. We have shown how rock-physics modeling not only helps us to correct the well data, but also allows us to go beyond the settings represented in the wells and quantify the seismic signatures of rock properties and conditions varying in a wider range using forward seismic modeling.

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