Mineralogy-based brittleness prediction from surface seismic data: Application to the Barnett Shale

2014 ◽  
Vol 2 (4) ◽  
pp. T255-T271 ◽  
Author(s):  
Roderick Perez Altamar ◽  
Kurt Marfurt
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Barnett Shale ◽  
Elastic Parameters ◽  
Well Location ◽  
Data Application ◽  
Empirical Calibration ◽  
Two Measures ◽  
Prestack Seismic Inversion ◽  
Microseismic Event

Differentiating brittle and ductile rocks from surface seismic data is the key to efficient well location and completion. Brittleness average estimates based only on elastic parameters are easy to use but require empirical calibration. In contrast, brittleness index (BI) estimates are based on mineralogy laboratory measurements and, indeed, cannot be directly measured from surface seismic data. These two measures correlate reasonably well in the quartz-rich Barnett Shale, but they provide conflicting estimates of brittleness in the calcite-rich Viola, Forestburg, Upper Barnett, and Marble Falls limestone formations. Specifically, the BI accurately predicts limestone formations that form fracture barriers to be ductile, whereas the brittleness average does not. We used elemental capture spectroscopy and elastic logs measured in the same cored well to design a 2D [Formula: see text] to brittleness template. We computed [Formula: see text] and [Formula: see text] volumes through prestack seismic inversion and calibrate the results with the [Formula: see text] template from well logs. We then used microseismic event locations from six wells to calibrate our prediction, showing that most of the microseismic events occur in the brittle regions of the shale, avoiding more ductile shale layers and the ductile limestone fracture barriers. Our [Formula: see text] to brittleness template is empirical and incorporates basin- and perhaps even survey-specific correlations of mineralogy and elastic parameters through sedimentation, oxygenation, and diagenesis. We do not expect this specific template to be universally applicable in other mudstone rock basins; rather, we recommend interpreters generate similar site-specific templates from logs representative of their area, following the proposed workflow.

Accuracy Comparison of Elastic Impedance Formula

2012 ◽  
Vol 466-467 ◽  
pp. 400-404
Author(s):  
Jin Zhang ◽  
Huai Shan Liu ◽  
Si You Tong ◽  
Lin Fei Wang ◽  
Bing Xu
Keyword(s):  
Seismic Data ◽  
Poisson Ratio ◽  
Seismic Inversion ◽  
P Wave ◽  
S Wave ◽  
Elastic Parameters ◽  
Accuracy Comparison ◽  
Elastic Impedance ◽  
Prestack Seismic Inversion ◽  
Lamé Coefficients

Elastic impedance (EI) inversion is one of the prestack seismic inversion methods, which can obtain P-wave and S-wave velocity, density, Poisson ratio, Lame coefficients and other elastic parameters. But there have been many EI formulas nowadays, so which formula should be used in inversion is an urgent problem. This paper divides these formulas into two categories, and use several forward modeling to test the accuracy of these EI formulas. It shows that using the first kind of EI formulas in near offset seismic data can get high precision results.

scholarly journals Direct inversion for reservoir parameters from prestack seismic data

2020 ◽  
Vol 17 (6) ◽  
pp. 993-1004
Author(s):  
Fanchang Zhang ◽  
Jingyang Yang ◽  
Chuanhui Li ◽  
Dong Li ◽  
Yang Gao
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Richards Equation ◽  
Bayesian Theorem ◽  
Amplitude Variation With Offset ◽  
Direct Inversion ◽  
Avo Inversion ◽  
Prestack Inversion ◽  
Data Application ◽  
Rock Model

Abstract Reliably estimating reservoir parameters is the final target in reservoir characterisation. Conventionally, estimating reservoir characters from seismic inversion is implemented by indirect approaches. The indirect estimation of reservoir parameters from inverted elastic parameters, however, will produce large bias due to the propagation of errors in the procedure of inversion. Therefore, directly obtaining reservoir parameters from prestack seismic data through a rock-physical model and prestack amplitude variation with offset (AVO) inversion is proposed. A generalised AVO equation in terms of oil-porosity (OP), sand indicator (SI) and density is derived by combining a physical rock model and the Aki–Richards equation in a whole system. This makes it possible to perform direct inversion for reservoir parameters. Next, under Bayesian theorem, we develop a robust prestack inversion approach based on the new AVO equation. Tests on synthetic seismic gathers show that it can dramatically reduce the prediction error of reservoir parameters. Furthermore, field data application illustrates that reliable reservoir parameters can be directly obtained from prestack inversion.

Elastic pre-stack seismic inversion through Discrete Cosine Transform reparameterization and Convolutional Neural Networks

Geophysics ◽  
2020 ◽  
pp. 1-70
Author(s):  
Mattia ALEARDI ◽  
Alessandro Salusti
Keyword(s):  
Discrete Cosine Transform ◽  
Elastic Properties ◽  
Seismic Data ◽  
Orthogonal Transformation ◽  
Seismic Inversion ◽  
Joint Probability Distribution ◽  
Elastic Model ◽  
Inversion Algorithm ◽  
Elastic Parameters ◽  
Cosine Transform

We develop a pre-stack inversion algorithm that combines a Discrete Cosine Transform (DCT) reparameterization of data and model spaces with a Convolutional Neural Network (CNN). The CNN is trained to predict the mapping between the DCT-transformed seismic data and the DCT-transformed 2-D elastic model. A convolutional forward modeling based on the full Zoeppritz equations constitutes the link between the elastic properties and the seismic data. The direct sequential co-simulation algorithm with joint probability distribution is used to generate the training and validation datasets under the assumption of a stationary non-parametric prior and a Gaussian variogram model for the elastic properties. The DCT is an orthogonal transformation that is here used as an additional feature extraction technique that reduces the number of unknown parameters in the inversion and the dimensionality of the input and output of the network. The DCT reparameterization also acts as a regularization operator in the model space and allows for the preservation of the lateral and vertical continuity of the elastic properties in the recovered solution. We also implement a Monte Carlo simulation strategy that propagates onto the estimated elastic model the uncertainties related to both noise contamination and network approximation. We focus on synthetic inversions on a realistic subsurface model that mimics a real gas-saturated reservoir hosted in a turbiditic sequence. We compare the outcomes of the implemented algorithm with those provided by a popular linear inversion approach and we also assess the robustness of the CNN inversion to errors in the estimated source wavelet and to erroneous assumptions about the noise statistic. Our tests confirm the applicability of the proposed approach, opening the possibility of estimating the subsurface elastic parameters and the associated uncertainties in near real-time while satisfactorily preserving the assumed spatial variability and the statistical properties of the elastic parameters.

Prestack Seismic Inversion Based on Anisotropic Markov Random Field

2018 ◽  
Vol 56 (2) ◽  
pp. 1069-1079 ◽  
Author(s):  
Qiang Guo ◽  
Hongbing Zhang ◽  
Feilong Han ◽  
Zuoping Shang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Seismic Inversion ◽  
Markov Random ◽  
Prestack Seismic Inversion

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

scholarly journals Batch seismic inversion using the iterative ensemble Kalman smoother

2021 ◽  
Author(s):  
Michael Gineste ◽  
Jo Eidsvik
Keyword(s):  
Seismic Data ◽  
Time Windows ◽  
Seismic Inversion ◽  
Efficient Estimation ◽  
Solution Strategy ◽  
Adaptive Procedure ◽  
Waveform Data ◽  
Kalman Smoother ◽  
Seismic Waveform ◽  
Single Batch

AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data.

Porosity estimation based on rock skeleton general formula and comprehensive pore structure parameter – An application to a tight-sand reservoir

2021 ◽  
pp. 1-59
Author(s):  
Kai Lin ◽  
Xilei He ◽  
Bo Zhang ◽  
Xiaotao Wen ◽  
Zhenhua He ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Structure Parameters ◽  
Pore Structure Parameter ◽  
Elastic Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Porosity Estimation

Most of current 3D reservoir’s porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with pore structure parameters such as pore aspect ratio, consolidate coefficient, critical porosity, etc. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock physics model fails properly address the effects of pore structure parameters on the elastic parameters. However, most of current rock physics models only consider one pore structure parameter such as pore aspect ratio or consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we propose a comprehensive pore structure (CPS) parameter set that is generalized from the current popular rock physics models. The new CPS set is based on the first order approximation of current rock physics models that consider the effect of pore aspect ratio on elastic parameters. The new CPS set can accurately simulate the behavior of current rock physics models that consider the effect of pore structure parameters on elastic parameters. To demonstrate the effectiveness of proposed parameters in porosity estimation, we use a theoretical model to demonstrate that the proposed CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir by using neural network algorithm and a rock physics model that is commonly used in porosity estimation. The comparison demonstrates that predicted porosity has higher correlation with the porosity logs at the blind well locations.

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