Lithology Discrimination Using Elastic Rock Properties and Seismic Inversion in the Leduc Reservoir, NE Alberta, Canada

2014 ◽  
Author(s):  
E.P. Ardakani ◽  
T.J. Podivinsky
Keyword(s):  
Seismic Inversion ◽  
Rock Properties ◽  
Elastic Rock

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

Cementation exponent as a geometric factor for the elastic properties of granular rocks

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. MR341-MR349
Author(s):  
Tongcheng Han ◽  
Zhoutuo Wei ◽  
Li-Yun Fu
Keyword(s):  
Elastic Properties ◽  
Geophysical Methods ◽  
Effective Medium ◽  
Geometric Factor ◽  
Rock Properties ◽  
Modeling Approaches ◽  
The Earth ◽  
Elastic Rock ◽  
Effective Medium Models ◽  
Electromagnetic Surveys

A geometric factor properly describing the microstructure of a rock is compulsory for effective medium models to accurately predict the elastic and electrical rock properties, which, in turn, are of great importance for interpreting data acquired by seismic and electromagnetic surveys, two of the most important geophysical methods for understanding the earth. Despite the applications of cementation exponent for the successful modeling of electrical rock properties, however, there has been no demonstration of cementation exponent as the geometric factor for the elastic rock properties. We have developed a workflow to model the elastic properties of clean and normal granular rocks through the combination of effective medium modeling approaches using cementation exponent as the geometric factor. Based on the dedicated modeling approaches, we find that cementation exponent can be adequately used as a geometric factor for the elastic properties of granular rocks. Further results highlight the effects of cementation exponent on the elastic and joint elastic-electrical properties of granular rocks. The results illustrate the promise of cementation exponent as a geometric link for the joint elastic-electrical modeling to better characterize the earth through integrated seismic and electromagnetic surveys.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

Amplitude-preserving reverse time migration: From reflectivity to velocity and impedance inversion

Geophysics ◽  
2014 ◽  
Vol 79 (6) ◽  
pp. S271-S283 ◽  
Author(s):  
Yu Zhang ◽  
Andrew Ratcliffe ◽  
Graham Roberts ◽  
Lian Duan
Keyword(s):  
Seismic Inversion ◽  
Rock Properties ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Low Frequencies ◽  
Depth Imaging ◽  
Additional Information ◽  
Velocity Models ◽  
Time Migration ◽  
Inversion Techniques

Conventional methods of prestack depth imaging aim at producing a structural image that delineates the interfaces of the geologic variations or the reflectivity of the earth. However, it is the underlying impedance and velocity changes that generate this reflectivity that are of more interest for characterizing the reservoir. Indeed, the need to generate a better product for geologic interpretation leads to the subsequent application of traditional seismic-inversion techniques to the reflectivity sections that come from typical depth-imaging processes. The drawback here is that these seismic-inversion techniques use additional information, e.g., from well logs or velocity models, to fill the low frequencies missing in traditional seismic data due to the free-surface ghost in marine acquisition. We found that with the help of broadband acquisition and processing techniques, the bandwidth gap between the depth-imaging world and seismic inversion world is reducing. We outlined a theory that shows how angle-domain common-image gathers produced by an amplitude-preserving reverse time migration can estimate impedance and velocity perturbations. The near-angle stacked image provides the impedance perturbation estimate whereas the far-angle image can be used to estimate the velocity perturbation. In the context of marine acquisition and exploration, our method can, together with a ghost compensation technique, be a useful tool for seismic inversion, and it is also adaptable to a full-waveform inversion framework. We developed synthetic and real data examples to test that the method is reliable and provides additional information for interpreting geologic structures and rock properties.

scholarly journals Geophysical imaging of porosity variations in the Danish North Sea chalk

2008 ◽  
Vol 15 ◽  
pp. 17-20 ◽  
Author(s):  
Tanni Abramovitz
Keyword(s):  
North Sea ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Well Log ◽  
Reservoir Properties ◽  
Seismic Reflection Data ◽  
Log Data ◽  
The North ◽  
The North Sea

More than 80% of the present-day oil and gas production in the Danish part of the North Sea is extracted from fields with chalk reservoirs of late Cretaceous (Maastrichtian) and early Paleocene (Danian) ages (Fig. 1). Seismic reflection and in- version data play a fundamental role in mapping and characterisation of intra-chalk structures and reservoir properties of the Chalk Group in the North Sea. The aim of seismic inversion is to transform seismic reflection data into quantitative rock properties such as acoustic impedance (AI) that provides information on reservoir properties enabling identification of porosity anomalies that may constitute potential reservoir compartments. Petrophysical analyses of well log data have shown a relationship between AI and porosity. Hence, AI variations can be transformed into porosity variations and used to support detailed interpretations of porous chalk units of possible reservoir quality. This paper presents an example of how the chalk team at the Geological Survey of Denmark and Greenland (GEUS) integrates geological, geophysical and petrophysical information, such as core data, well log data, seismic 3-D reflection and AI data, when assessing the hydrocarbon prospectivity of chalk fields.

Estimation of subsurface rock properties from seismic inversion and geo-statistical methods over F3-block, Netherland

2020 ◽  
pp. 1-15
Author(s):  
Prabodh Kumar Kushwaha ◽  
Satya Prakash Maurya ◽  
Piyush Rai ◽  
Nagendra Pratap Singh
Keyword(s):  
Statistical Methods ◽  
Seismic Inversion ◽  
Rock Properties
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