Tight Gas Sand Rock Physics Analysis and Hybrid Pre-Stack Iterative Inversion for Reservoir Characterization

2019 ◽  
Author(s):  
S.Z. Sun ◽  
L. Ji ◽  
X. Zhu
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Tight Gas ◽  
Iterative Inversion

scholarly journals Stochastic reservoir characterization using prestack seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 978-993 ◽  
Author(s):  
Jo Eidsvik ◽  
Per Avseth ◽  
Henning Omre ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
A Priori ◽  
Rock Physics ◽  
Real Data ◽  
Spatial Coupling ◽  
The North ◽  
Amplitude Versus Offset ◽  
Likelihood Model ◽  
Avo Attributes ◽  
The Impact

Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in this study to predict the distribution of the reservoir variables, i.e., facies and fluid filling. The prediction problem is cast in a Bayesian setting. The a priori model includes spatial coupling through Markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory, including Gassmann's relation. The likelihood model relating observations to reservoir variables (including lithology facies and pore fluids) is based on approximations to Zoeppritz equations. The model assumptions are summarized in a Bayesian network illustrating the dependencies between the reservoir variables. The posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models. This posterior model is not analytically tractable but can be explored by Markov chain Monte Carlo (MCMC) sampling. Realizations of reservoir variables from the posterior model are used to predict the facies and fluid‐filling distribution in the reservoir. A maximum a posteriori (MAP) criterion is used in this study to predict facies and pore‐fluid distributions. The realizations are also used to present probability maps for the favorable (sand, oil) occurrence in the reservoir. Finally, the impact of seismic AVO attributes—AVO gradient, in particular—is studied. The approach is demonstrated on real data from a turbidite sedimentary system in the North Sea. AVO attributes on the interface between reservoir and cap rock are extracted from 3D seismic AVO data. The AVO gradient is shown to be valuable in reducing the ambiguity between facies and fluids in the prediction.

Dynamic to Static Modeling

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Javad Sharifi
Keyword(s):  
Reservoir Characterization ◽  
Triaxial Compression ◽  
Rock Physics ◽  
Regression Equations ◽  
Compression Tests ◽  
In Situ Tests ◽  
Factors Affecting ◽  
Static Modulus ◽  
Wide Range ◽  
Static Modeling

Dynamic-to-static modulus conversion has long been recognized as a complicated and challenging task in reservoir characterization and seismic geomechanics, and many single- and two-variable regression equations have been proposed. In practice however, the form and constants of the regression equation are variable from case to case. I introduce a methodology for estimating the static moduli called dynamic-to-static modeling (DTS). The methodology was validated by laboratory tests (ultrasonic and triaxial compression tests) to obtain dynamic and quasi-static bulk and Young’s (elasticity) moduli. Next, rock deformation phenomena were simulated considering different parameters affecting the process. The dynamic behavior was further modeled using rock physics methods. Unlike the conventional dynamic-to-static conversion procedures, the method considers a wide range of factors affecting the relationship between the dynamic and static moduli, including strain amplitude, dispersion, rock failure mechanism, pore shape, crack parameters, poromechanics, and upscaling. A comparison between the data from laboratory and in-situ tests and the estimation results indicated promising findings. The accuracy of the results was assessed by the analysis of variance (ANOVA). In addition to modeling the static moduli, DTS can be used to verify the static and dynamic moduli values with appropriate accuracy when core data is not available.

Using rock physics models to validate rock composition from multimineral log analysis

Geophysics ◽  
2021 ◽  
pp. 1-69
Author(s):  
Liwei Cheng ◽  
Manika Prasad ◽  
Reinaldo J. Michelena ◽  
Ali Tura ◽  
Shamima Akther ◽  
...  
Keyword(s):  
Quality Control ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Inequality Constraint ◽  
Effective Medium ◽  
Carbonate Reservoir ◽  
Log Analysis ◽  
Evaluation Tool ◽  
Rock Composition ◽  
Effective Medium Models

Multimineral log analysis is a quantitative formation evaluation tool for geological and petrophysical reservoir characterization. Rock composition can be estimated by solving equations that relate log measurements to the petrophysical endpoints of minerals and fluids. Due to errors in log data and uncertainties in petrophysical endpoints of constituents, we propose using effective medium models from rock physics as additional independent information to validate or constrain the results. In this paper, we examine the Voigt-Reuss (VR) bound model, self-consistent approximation (SCA), and differential effective medium (DEM). The VR bound model provides the first-order quality control of multimineral results. We first show a conventional carbonate reservoir study with intervals where the predicted effective medium models from multimineral results are inconsistent with the measured elastic properties. We use the VR bound model as an inequality constraint in multimineral analysis for plausible alternative solutions. SCA and DEM models provide good estimates in low porosity intervals and imply geological information for the porous intervals. Then, we show a field case of the Bakken and Three Forks formations. A linear interpolation of the VR bound model helps validate multimineral results and approximate the elastic moduli of clay. There are two major advantages to use our new method (a) rock physics effective medium models provide independent quality control of petrophysical multimineral results, and (b) multimineral information leads to realistic rock physics models.

A Revised Gassmann Fluid Replacement Model Based on Neutron-Density Logs in Shaly Sandstone Reservoirs

2021 ◽  
pp. 1-13
Author(s):  
Shantanu Chakraborty ◽  
Samit Mondal ◽  
Rima Chatterjee
Keyword(s):  
Oil And Gas ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Oil And Gas Industry ◽  
Effective Porosity ◽  
Fluid Replacement ◽  
Neutron Density ◽  
Gas Industry ◽  
Sandstone Reservoirs ◽  
Shaly Sandstone

Summary Fluid-replacement modeling (FRM) is a fundamental step in rock physics scenario modeling. The results help to conduct forward modeling for prediction of seismic signatures. Further, the analysis of the results improves the accuracy of quantitative interpretation and leads to an updated reservoir characterization. While modeling for different possible reservoir pore fluid scenarios, the quality of the results largely depends on the accuracy of the FRM. Gassmann (1951)fluid-replacement modeling (GFRM) is one of the widely adopted methods across the oil and gas industry. However, the Gassmann method assumes the reservoir as clean sandstone with connected pores. This causes Gassmann fluid-replacement results to overestimate the fluid effect in shaly sandstones. This study uses neutron and density logs to correct the overestimated results in shaly sandstone reservoirs. Due to the nature of these recordings, both of these log readings have close dependencies on the presence of shale. When the logs are plotted in a justified scale, the differences between the logs provide an accurate measurement of shaliness within the reservoir. The study has formulated a weight factor using the logs, which has further been used to scale the overestimated Gassmann-modeled fluid effect. The results of the revised method are independent of type of clay presence and associated effective porosity. Moreover, the corrected FRM results from the revised Gassmann method shows good agreement with rock physical interpretation of shaly sandstone reservoirs.

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