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.

Geopressure prediction using seismic data: Current status and the road ahead

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 2012-2041 ◽  
Author(s):  
N. C. Dutta
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Best Practice ◽  
Seismic Velocity ◽  
Rock Physics ◽  
Rock Properties ◽  
Current Status ◽  
Seismic Attribute ◽  
Attribute Analysis ◽  
Exploration And Production

The subject of seismic detection of abnormally high‐pressured formations has received a great deal of attention in exploration and production geophysics because of increasing exploration and production activities in frontier areas (such as the deepwater) and a need to lower cost without compromising safety and environment, and manage risk and uncertainty associated with very expensive drilling. The purpose of this review is to capture the “best practice” in this highly specialized discipline and document it. Pressure prediction from seismic data is based on fundamentals of science, especially those of rock physics and seismic attribute analysis. Nonetheless, since the first seismic application in the 1960s, practitioners of the technology have relied increasingly on empiricism, and the fundamental limitations of the tools applied to detect such hazardous formations were lost. The most successful approach to seismic pressure prediction is one that combines a good understanding of rock properties of subsurface formations with the best practice for seismic velocity analysis appropriate for rock physics applications, not for stacking purposes. With the step change that the industry has seen in the application of the modern digital computing technology to solving large‐scale exploration and production problems using seismic data, the detection of pressured formations can now be made with more confidence and better resolution. The challenge of the future is to break the communication and the “language barrier” that still exists between the seismologists, the rock physicists, and the drilling community.

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.

Using AI/ML to Explore & Develop Quickly and Efficiently

2021 ◽  
Author(s):  
Anthony Aming
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Seismic Data ◽  
3D Seismic ◽  
Structure Amplitude ◽  
Zero Crossing ◽  
Petroleum Systems ◽  
Exploration And Production ◽  
Data Volume ◽  
3D Seismic Data

Abstract See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.

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.

scholarly journals A new depositional model for the Tuaheni Landslide Complex, Hikurangi Margin, New Zealand

2020 ◽  
Vol 500 (1) ◽  
pp. 551-566 ◽  
Author(s):  
Benjamin Couvin ◽  
Aggeliki Georgiopoulou ◽  
Joshu J. Mountjoy ◽  
Lawrence Amy ◽  
Gareth J. Crutchley ◽  
...  
Keyword(s):  
Seismic Data ◽  
Lateral Spreading ◽  
3D Seismic ◽  
Depositional Processes ◽  
Attribute Analysis ◽  
Internal Structures ◽  
Show Evidence ◽  
Levee System ◽  
Central Ridge ◽  
International Ocean Discovery Program

AbstractThe Tuaheni Landslide Complex (TLC) is characterized by areas of compression upslope and extension downslope. It has been thought to consist of a stack of two genetically linked landslide units identified from seismic data. We used 3D seismic reflection, bathymetry data and International Ocean Discovery Program Core U1517C (Expedition 372) to understand the internal structures, deformation mechanisms and depositional processes of the TLC deposits. Units II and III of U1517C correspond to the two chaotic units in 3D seismic data. In the core, Unit II shows deformation, whereas Unit III appears more like an in situ sequence. Variance attribute analysis showed that Unit II is split into lobes around a coherent stratified central ridge and is bounded by scarps. By contrast, we found that Unit III is continuous beneath the central ridge and has an upslope geometry, which we interpreted as a channel–levee system. Both units show evidence of lateral spreading due to the presence of the Tuaheni Canyon removing support from the toe. Our results suggest that Units II and III are not genetically linked, are separated substantially in time and had different emplacement mechanisms, but they fail under similar circumstances.

Improving seismic QP estimation using rock-physics constraints

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. MR187-MR198 ◽  
Author(s):  
Yi Shen ◽  
Jack Dvorkin ◽  
Yunyue Li
Keyword(s):  
Seismic Data ◽  
Closed Form Solution ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Analytical Relation ◽  
Gas Reservoir ◽  
Seismic Section ◽  
Data Set ◽  
Attenuation Model ◽  
Text Model

Our goal is to accurately estimate attenuation from seismic data using model regularization in the seismic inversion workflow. One way to achieve this goal is by finding an analytical relation linking [Formula: see text] to [Formula: see text]. We derive an approximate closed-form solution relating [Formula: see text] to [Formula: see text] using rock-physics modeling. This relation is tested on well data from a clean clastic gas reservoir, of which the [Formula: see text] values are computed from the log data. Next, we create a 2D synthetic gas-reservoir section populated with [Formula: see text] and [Formula: see text] and generate respective synthetic seismograms. Now, the goal is to invert this synthetic seismic section for [Formula: see text]. If we use standard seismic inversion based solely on seismic data, the inverted attenuation model has low resolution and incorrect positioning, and it is distorted. However, adding our relation between velocity and attenuation, we obtain an attenuation model very close to the original section. This method is tested on a 2D field seismic data set from Gulf of Mexico. The resulting [Formula: see text] model matches the geologic shape of an absorption body interpreted from the seismic section. Using this [Formula: see text] model in seismic migration, we make the seismic events below the high-absorption layer clearly visible, with improved frequency content and coherency of the events.

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.

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