Application of Geostatistical Seismic AVA Inversion for Shale Reservoir Characterization and Brittleness Prediction with Machine Learning

Cyz, M.; Azevedo, Leonardo; Malinowski, M.

doi:10.3997/2214-4609.201900691

Cited by 2 publications

(6 citation statements)

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“…The geostatistical shale rock physics seismic AVA inversion was applied to a ∼60-km 2 subset of a high-quality wide-azimuth 3-D seismic data from the Baltic Basin, 12. Cross section through average elastic properties (V P , V S , and RHOB) from (Left) geostatistical shale rock physic inversion compared with (Right) results of geostatistical elastic seismic AVA inversion run in a previous study [38].…”

Section: Real Case Applicationmentioning

confidence: 99%

“…Finally, we compare the inverted volumes of elastic properties resulting from the proposed method with the results of a previous study with geostatistical seismic AVA inversion run on the same data set [38] (see Fig. 12).…”

Section: Real Case Applicationmentioning

confidence: 99%

“…Then, we show the results of its application to an onshore shale gas reservoir in Northern Poland [37]. The resulting models are compared against those obtained by a geostatistical seismic AVA inversion using the same data set [38].…”

Section: Introductionmentioning

confidence: 99%

“…. Cross section through average elastic properties (V P , V S , and RHOB) from (Left) geostatistical shale rock physic inversion compared with (Right) results of geostatistical elastic seismic AVA inversion run in a previous study[38].…”

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confidence: 99%

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Direct Geostatistical Seismic Amplitude Versus Angle Inversion for Shale Rock Properties

Cyz

Azevedo

2021

IEEE Trans. Geosci. Remote Sensing

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The purpose of seismic reservoir characterization is to predict the spatial distribution of the subsurface rock properties from a set of direct and indirect measurements. Commonly, rock property volumes are obtained in a two-steps approach. First, the elastic properties are inverted from seismic reflection data and then used to compute rock properties volumes by applying a calibrated rock physics models. Such an approach is not only time consuming but may also lead to biased results as the uncertainties related to seismic inversion may not be propagated through the entire geomodeling workflow. Here, we propose an iterative geostatistical shale rock physics seismic AVA inversion method to invert seismic reflection data directly for shale rock properties. The workflow consists of three main steps starting from shale properties model generation of brittleness, total organic carbon (TOC), and porosity using stochastic sequential simulation and cosimulation and calculation of a volume of shale. In the following step, elastic property volumes are calculated based on a calibrated shale rock physics model using the self-consistent approximation. The elastic models are then used for the calculation of the synthetic seismic data. In the final step, the misfit between synthetic and real data is calculated and used as part of the stochastic update of the model parameter space. The whole process is repeated until a minimum misfit between the observed and synthetic seismic is achieved. The proposed method is successfully tested on an onshore Lower Paleozoic shale gas reservoir in Northern Poland, and the predicted shale rock properties agree with those observed at a blind-well location.

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Section: Real Case Applicationmentioning

confidence: 99%

Section: Real Case Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Direct Geostatistical Seismic Amplitude Versus Angle Inversion for Shale Rock Properties

Cyz

Azevedo

2021

IEEE Trans. Geosci. Remote Sensing

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“…These analyses were used to quantify the hydraulic fracturing potential of two source intervals located within Silurian and Ordovician strata (Figure 1). Previous research and recent studies covering the parametrical characterization of the research area have been well investigated by numerous authors [2][3][4][5][6][7][8][9][10]. These studies can be divided into three general topics:…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary Characterization of Unconventional Reservoirs Based on Correlation of Well and Seismic Data

Kaczmarczyk

Słota-Valim

2020

Energies

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Combinatorial analysis of key petrophysical parameters can provide valuable information about subsurface hydrocarbon reservoirs. This is particularly important for reservoirs with unconventional rock formations that, due to the low permeability, need to be stimulated by fracturing treatment to provide fluid flow to the exploitation wellbore. In this article, based on data from unconventional shale formations (N Poland), we outline how independent sets of elastic and petrophysical parameters and other reservoir features can be co-analyzed to estimate the fracture susceptibility of shale intervals, which are characterized by a high total organic carbon (TOC) content and high porosity. These features were determined by analysis of each horizon’s elastic and mineralogical brittleness index (BI). These two variants were calculated first in 1D; integrated with the seismic data and finally compared with other parameters such as acoustic impedance, ratio of compressional and shear wave velocities, porosity, and density; and then presented and analyzed using cross plots that highlighted the key relationships between them. The overall BI trends were similar in both horizontal and vertical directions. The highest BI values were observed in the southeast of the analyzed area (Source I) and in the southeast and northwest of the area (Source II). These results can form the basis for predictive modeling of reservoir properties aiding effective reservoir exploration.

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Application of Geostatistical Seismic AVA Inversion for Shale Reservoir Characterization and Brittleness Prediction with Machine Learning

Cited by 2 publications

References 0 publications

Direct Geostatistical Seismic Amplitude Versus Angle Inversion for Shale Rock Properties

Direct Geostatistical Seismic Amplitude Versus Angle Inversion for Shale Rock Properties

Multidisciplinary Characterization of Unconventional Reservoirs Based on Correlation of Well and Seismic Data

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