Improving thin-bed resolution: Application of a sparse-layer inversion on 3D seismic observations from the In Salah carbon dioxide storage project

2015 ◽  
Vol 3 (3) ◽  
pp. SS65-SS71
Author(s):  
Rui Zhang ◽  
Thomas M. Daley ◽  
Donald Vasco
Keyword(s):  
Carbon Dioxide ◽  
Fracture Zone ◽  
Seismic Data ◽  
Subsurface Layer ◽  
Damage Zone ◽  
Seismic Inversion ◽  
Earth Model ◽  
Carbon Dioxide Storage ◽  
3D Seismic ◽  
Injection Interval

The In Salah carbon dioxide storage project in Algeria has injected more than 3 million tons of carbon dioxide into a thin water-filled tight-sand formation. Interferometric synthetic aperture radar range change data revealed a double-lobe pattern of surface uplift, which has been interpreted as the existence of a subvertical fracture, or damage, zone. The reflection seismic data found a subtle linear push-down feature located along the depression between the two lobes thought to be due to the injection of carbon dioxide. Understanding of the [Formula: see text] distribution within the injection interval and migration within the fracture zone requires a precise subsurface layer model from the injection interval to above the top of the fracture zone. To improve the resolution of the existing seismic model, we applied a sparse-layer seismic inversion, with basis pursuit decomposition on the 3D seismic data between 1.0 and 1.5 s. The inversion results, including reflection coefficients and band-limited impedance cubes, provided improved subsurface imaging for two key layers (seismic horizons) above the injection interval. These horizons could be used as part of a more detailed earth model to study the [Formula: see text] storage at In Salah.

Characterization of a fracture zone using seismic attributes at the In Salah CO2 storage project

2015 ◽  
Vol 3 (2) ◽  
pp. SM37-SM46 ◽  
Author(s):  
Rui Zhang ◽  
Donald Vasco ◽  
Thomas M. Daley ◽  
William Harbert
Keyword(s):  
Carbon Dioxide ◽  
Fracture Zone ◽  
Seismic Data ◽  
Co2 Storage ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Lower Velocity ◽  
Surface Uplift ◽  
3D Seismic Data

The In Salah carbon dioxide storage project in Algeria has injected more than 3 million tons of carbon dioxide into a water-filled tight-sand formation. During injection, interferometric synthetic aperture radar (InSAR) reveals a double-lobed pattern of up to a 20-mm surface uplift above the horizontal leg of an injection well. Interpretation of 3D seismic data reveals the presence of a subtle linear push-down feature located along the InSAR determined surface depression between the two lobes, which we interpreted to have to be caused by anomalously lower velocity from the fracture zone and the presence of [Formula: see text] displacing brine in this feature. To enhance the seismic interpretation, we calculated many poststack seismic attributes, including positive and negative curvatures as well as ant track, from the 3D seismic data. The maximum positive curvature attributes and ant track found the clearest linear features, with two parallel trends, which agreed well with the ant-track volume and the InSAR observations of the depression zone. The seismic attributes provided a plausible characterization of the fracture zone extent, including height, width, and length (80, 350, and 3500 m, respectively), providing important information for further study of fracture behavior due to the [Formula: see text] injection at In Salah. We interpreted the pattern of depression between two surface-deformation lobes as caused by the opening of a subvertical fracture or damage zone at depth above the injection interval, which allowed injected [Formula: see text] to migrate upward. Our analysis corroborated previous interpretation of surface uplift as due to the injection of [Formula: see text] in this well.

Blind Inversion of Multidimensional Seismic Data Using Sequential Tikhonov and Total Variation Regularizations (STTVR)

Geophysics ◽  
2021 ◽  
pp. 1-56
Author(s):  
Saber jahanjooy ◽  
Mohammad Ali Riahi ◽  
Hamed Ghanbarnejad Moghanloo
Keyword(s):  
Total Variation ◽  
Tikhonov Regularization ◽  
Seismic Data ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Earth Model ◽  
Rapid Changes ◽  
Nonlinear Relation ◽  
Ill Posed ◽  
Seismic Sections

The acoustic impedance (AI) model is key data for seismic interpretation, usually obtained from its nonlinear relation with seismic reflectivity. Common approaches use initial geological and seismic information to constraint the AI model estimation. When no accurate prior information is available, these approaches may dictate false results at some parts of the model. The regularization of ill-posed underdetermined problems requires some constraints to restrict the possible results. Available seismic inversion methods mostly use Tikhonov or total variation (TV) regularizations with some adjustments. Tikhonov regularization assumes smooth variation in the AI model, and it is incurious about the rapid changes in the model. TV allows rapid changes, and it is more stable in presence of noisy data. In a detailed realistic earth model that AI changes gradually, TV creates a stair-casing effect, which could lead to misinterpretation. This could be avoided by using TV and Tikhonov regularization sequentially in the alternating direction method of multipliers (ADMM) and creating the AI model. The result of implementing the proposed algorithm (STTVR) on 2D synthetic and real seismic sections shows that the smaller details in the lithological variations are accounted for as well as the general trend. STTVR can calculate major AI variations without any additional low-frequency constraints. The temporal and spatial transition of the calculated AI in real seismic data is gradual and close to a real geological setting.

Reflectivity decomposition: Theory and application

2021 ◽  
pp. 1-57
Author(s):  
Chen Liang ◽  
John Castagna ◽  
Marcelo Benabentos
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reflection Coefficients ◽  
3D Seismic ◽  
Decomposition Theory ◽  
Midland Basin ◽  
Reflection Seismic ◽  
Sparse Inversion ◽  
Reflectivity Inversion

Sparse reflectivity inversion of processed reflection seismic data is intended to produce reflection coefficients that represent boundaries between geological layers. However, the objective function for sparse inversion is usually dominated by large reflection coefficients which may result in unstable inversion for weak events, especially those interfering with strong reflections. We propose that any seismogram can be decomposed according to the characteristics of the inverted reflection coefficients which can be sorted and subset by magnitude, sign, and sequence, and new seismic traces can be created from only reflection coefficients that pass sorting criteria. We call this process reflectivity decomposition. For example, original inverted reflection coefficients can be decomposed by magnitude, large ones removed, the remaining reflection coefficients reconvolved with the wavelet, and this residual reinverted, thereby stabilizing inversions for the remaining weak events. As compared with inverting an original seismic trace, subtle impedance variations occurring in the vicinity of nearby strong reflections can be better revealed and characterized when only the events caused by small reflection coefficients are passed and reinverted. When we apply reflectivity decomposition to a 3D seismic dataset in the Midland Basin, seismic inversion for weak events is stabilized such that previously obscured porous intervals in the original inversion, can be detected and mapped, with good correlation to actual well logs.

Estimation of Carbon Dioxide emissions along an active fault by using geoelectrical measurements

2020 ◽  
Author(s):  
Ester Piegari ◽  
Rosa Di Maio ◽  
Rosanna Salone ◽  
Claudio De Paola
Keyword(s):  
Carbon Dioxide ◽  
Seismic Data ◽  
Carbon Dioxide Emissions ◽  
Active Fault ◽  
Damage Zone ◽  
Thick Layer ◽  
Integrated Approach ◽  
Upward Migration ◽  
Passive Seismic ◽  
Zone Characteristic

<p>In the last twenty years, a growing interest is noticed in quantifying non-volcanic degassing, which could represent a significant input of CO<sub>2</sub> into the atmosphere. Large emissions of non-volcanic carbon dioxide usually take place in seismically active zones, where the existence of a positive spatial correlation between gas discharges and extensional tectonic regimes has been confirmed by seismic data. Extensional stress plays a key role in creating pathways for the rising of gases at micro- and macro-scales, increasing the rock permeability and connecting the deep crust to the earth surface. Geoelectrical investigations, which are very sensitive to permeability changes, provide accurate volumetric reconstructions of the physical properties of the rocks and, therefore, are fundamental not only for the definition of the seismic-active zone geometry, but also for understanding the processes that govern the flow of fluids along the damage zone. In this framework, we present the results of an integrated approach where geoelectrical and passive seismic data are used to construct a 3D geological model, whose simulated temporal evolution allowed the estimation of CO<sub>2</sub> flux along an active fault in the area of Matese Ridge (Southern Apennines, Italy). By varying the geometry of the source system and the permeability values of the damage zone, characteristic times for the upward migration of CO<sub>2</sub> through a thick layer of silts and clays have been estimated and CO<sub>2</sub> fluxes comparable with the observed values in the investigated area have been predicted. These findings are promising for gas hazard, as they suggest that numerical simulations of different CO<sub>2</sub> degassing scenarios could forecast possible critical variations in the amount of CO<sub>2</sub> emitted near the fault.</p>

Tomographic filtering of mantle circulation models via the generalised inverse: A way to account for seismic data uncertainty

2020 ◽  
Author(s):  
Bernhard S.A. Schuberth ◽  
Roman Freissler ◽  
Christophe Zaroli ◽  
Sophie Lambotte
Keyword(s):  
Seismic Data ◽  
Inverse Operator ◽  
Synthetic Data ◽  
Circulation Model ◽  
Model Space ◽  
Seismic Inversion ◽  
Data Uncertainty ◽  
Earth Model ◽  
Model Parameters ◽  
Noise Component

<p>For a comprehensive link between seismic tomography and geodynamic models, uncertainties in the seismic model space play a non-negligible role. More specifically, knowledge of the tomographic uncertainties is important for obtaining meaningful estimates of the present-day thermodynamic state of Earth's mantle, which form the basis of retrodictions of past mantle evolution using the geodynamic adjoint method. A standard tool in tomographic-geodynamic model comparisons nowadays is tomographic filtering of mantle circulation models using the resolution operator <em><strong>R</strong></em> associated with the particular seismic inversion of interest. However, in this classical approach it is not possible to consider tomographic uncertainties and their impact on the geodynamic interpretation. </p><p>Here, we present a new method for 'filtering' synthetic Earth models, which makes use of the generalised inverse operator <strong>G</strong><sup>†</sup>, instead of using <em><strong>R</strong></em>. In our case, <strong>G</strong><sup>†</sup> is taken from a recent global SOLA Backus–Gilbert <em>S</em>-wave tomography. In contrast to classical tomographic filtering, the 'imaged' model is constructed by computing the <em>Generalised-Inverse Projection</em> (GIP) of synthetic data calculated in an Earth model of choice. This way, it is possible to include the effects of noise in the seismic data and thus to analyse uncertainties in the resulting model parameters. In order to demonstrate the viability of the method, we compute a set of travel times in an existing mantle circulation model, add specific realisations of Gaussian, zero-mean seismic noise to the synthetic data and apply <strong>G</strong><sup>†</sup>. <br> <br>Our results show that the resulting GIP model without noise is equivalent to the mean model of all GIP realisations from the suite of synthetic 'noisy' data and also closely resembles the model tomographically filtered using <em><strong>R</strong></em>. Most important, GIP models that include noise in the data show a significant variability of the shape and amplitude of seismic anomalies in the mantle. The significant differences between the various GIP realisations highlight the importance of interpreting and assessing tomographic images in a prudent and cautious manner. With the GIP approach, we can moreover investigate the effect of systematic errors in the data, which we demonstrate by adding an extra term to the noise component that aims at mimicking the effects of uncertain crustal corrections. In our presentation, we will finally discuss ways to construct the model covariance matrix based on the GIP approach and point out possible research directions on how to make use of this information in future geodynamic modelling efforts.</p>

Preconditioning point-source/point-receiver high-density 3D seismic data for lacustrine shale characterization in a loess mountain area

2017 ◽  
Vol 5 (2) ◽  
pp. SF177-SF188 ◽  
Author(s):  
Wei Wang ◽  
Xiangzeng Wang ◽  
Hongliu Zeng ◽  
Quansheng Liang
Keyword(s):  
Data Quality ◽  
Seismic Data ◽  
Ordos Basin ◽  
Seismic Inversion ◽  
High Density ◽  
Seismic Survey ◽  
3D Seismic ◽  
Noise Characteristics ◽  
Lacustrine Shale ◽  
3D Seismic Data

In the study area, southeast of Ordos Basin in China, thick lacustrine shale/mudstone strata have been developed in the Triassic Yanchang Formation. Aiming to study these source/reservoir rocks, a 3D full-azimuth, high-density seismic survey was acquired. However, the surface in this region is covered by a thick loess layer, leading to seismic challenges such as complicated interferences and serious absorption of high frequencies. Despite a specially targeted seismic processing workflow, the prestack Kirchhoff time-migrated seismic data were still contaminated by severe noise, hindering seismic inversion and geologic interpretation. By taking account of the particular data quality and noise characteristics, we have developed a cascade workflow including three major methods to condition the poststack 3D seismic data. First, we removed the sticky coherent noise by a local pseudo [Formula: see text]-[Formula: see text]-[Formula: see text] Cadzow filtering. Then, we diminished the random noise by a structure-oriented filtering. Finally, we extended the frequency bandwidth with a spectral-balancing method based on the continuous wavelet transform. The data quality was improved after each of these steps through the proposed workflow. Compared with the original data, the conditioned final data show improved interpretability of the shale targets through geometric attribute analysis and depositional interpretation.

Quantitative application of post-stack inversion of organic-rich shale based on rock physics linear relationship and 3D seismic data, Perth Basin, Western Australia

2014 ◽  
Vol 54 (2) ◽  
pp. 532
Author(s):  
Yazeed Altowairqi ◽  
Reza Rezaee ◽  
Milovan Urosevic
Keyword(s):  
Shale Gas ◽  
Seismic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
3D Seismic ◽  
Physical Relationship ◽  
Seismic Properties ◽  
Attribute Analysis ◽  
Exploration And Production ◽  
Compressional Velocity

Unconventional resources such as shale gas have been an extremely important exploration and production target. To understand the seismic responses of the shale gas plays, the use of rock physical relationship is important, which is constrained with geology and formation-evaluation analysis. Since organic-rich shale seismic properties remains poorly understood, seismic inversion can be used to identify the organic-rich shale from barren shale. This approach helps identify and map spatial distributions and of the organic rich shales. This study shows the acoustic impedance (AI), which is the product of compressional velocity and density, decreases nonlinearly with increasing total organic carbon (TOC) content. TOC is obtained using Roc-Eval pyrolysis for more than 120 core shale samples for the Perth Basin. By converting the AI data to TOC precent on the seismic data, we therefore can map lateral distribution, thickness, and variation in TOC profile. This extended abstract presents a case study of the northern Perth Basin 3D seismic with application of different approaches of seismic inversion and multi-attribute analysis with the rock physical relationships.

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