Joint estimation of porosity and saturation and of effective stress and saturation for 3D and 4D seismic reservoir characterization using stochastic rock physics modeling and Bayesian inversion

2004 ◽  
Author(s):  
Ran Bachrach ◽  
Nader Dutta
Keyword(s):  
Effective Stress ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Joint Estimation ◽  
Bayesian Inversion ◽  
Seismic Reservoir ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
4D Seismic

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.

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.

Joint estimation of porosity and saturation using stochastic rock-physics modeling

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. O53-O63 ◽  
Author(s):  
Ran Bachrach
Keyword(s):  
Bayesian Estimation ◽  
Seismic Data ◽  
Rock Physics ◽  
Joint Estimation ◽  
Accuracy Comparison ◽  
Posterior Probability Density ◽  
Rock Physics Modeling ◽  
Posterior Probability Density Function ◽  
Reservoir Porosity ◽  
Physics Modeling

Sediment porosity and saturation affect bulk modulus, shear modulus, and density. Consequently, estimating hydrocarbon saturation and reservoir porosity from seismic data is a joint estimation problem: Uncertainty in porosity will lead to errors in saturation prediction, and vice versa. Porosity and saturation can be jointly estimated using stochastic rock-physics modeling and formal Bayesian estimation methodology. Knowledge of shear impedance reduces the uncertainty in porosity and thus also reduces uncertainty in saturation estimation. This study investigates joint estimation of porosity and saturation by using rock-physics, stochastic modeling, and Bayesian estimation theory to derive saturation and porosity maps of expected pay sands. In the field example, the uncertainty in porosity, quantified by the standard deviation (STD) associated with the posterior probability density function (pdf), derived from inversion of seismic data is much less than the uncertainty in the derived saturation. For a typical case, the STD associated with saturation is [Formula: see text] while porosity STD is about 1.34 porosity units given seismic-derived inversion attributes with reasonable accuracy. Comparison of these numbers with prior estimates showed that inversion of seismic data decreased the uncertainty in porosity to 15% of the prior uncertainty while saturation uncertainty was only reduced to 92% of the prior uncertainty. Although these results may vary from one location to another, the methodology is general and can be applied to other locations.

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.

Sign in / Sign up

Export Citation Format

Share Document