CRPP - Comprehensive Rock Properties Predictor - A New Method for Predicting Rock Properties from the Seismic Data

2012 ◽  
Author(s):  
M. Gruszczyk
Keyword(s):  
Seismic Data ◽  
New Method ◽  
Rock Properties

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

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 fresh look at Gulf of Mexico tectonics: Testing rotations and breakup mechanisms from the perspective of seismically constrained potential-fields modeling and plate kinematics

2020 ◽  
Vol 8 (4) ◽  
pp. SS31-SS45
Author(s):  
Daniel Minguez ◽  
E. Gerald Hensel ◽  
Elizabeth A. E. Johnson
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Data ◽  
Gravity Data ◽  
Seafloor Spreading ◽  
Plate Motion ◽  
Rock Properties ◽  
Satellite Gravity ◽  
Spreading Model ◽  
Breakup Mechanism ◽  
Mantle Exhumation

Interpretation of recent, high-quality seismic data in the Gulf of Mexico (GOM) has led to competing hypotheses regarding the basin’s rift to drift transition. Some studies suggest a fault-controlled mechanism that ultimately results in mantle exhumation prior to seafloor spreading. Others suggest voluminous magmatic intrusion accommodates the terminal extension phase and results in the extrusion of volcanic seaward dipping reflectors (SDRs). Whereas it has been generally accepted that the plate motions between the rift and drift phases of the GOM are nearly perpendicular to each other, it has not been greatly discussed if the breakup mechanism plays a role in accommodating the transition in plate motion. We have developed a plate kinematic and crustal architecture hypothesis to address the transition from rift to drift in the GOM. We support the proposition of a fault-controlled breakup mechanism, in which slip on a detachment between the crust and mantle may have exhumed the mantle. However, we stress that this mechanism is not exclusive of synrift magmatism, though it does imply that SDRs observed in the GOM are not in this case indicative of a volcanic massif separating attenuated continental and normal oceanic crust. We support our hypothesis through a geometrically realistic 2D potential field model, which includes a magnetic seafloor spreading model constrained by recent published seismic data and analog rock properties. The 2D model suggests that magnetic anomalies near the continent-ocean transition may be related to removal of the lower continental crust during a phase of hyperextension prior to breakup, ending in mantle exhumation. The kinematics of breakup, derived from recent satellite gravity data and constrained by our spreading model and the global plate circuit, suggests that this phase of hyperextension accommodated the change in plate motion direction and a diachronous breakup across the GOM.

Fast-Track Rock Properties From Broadband Seismic Data

2014 ◽  
Author(s):  
Axel Geisslinger ◽  
Azim Salleh
Keyword(s):  
Seismic Data ◽  
Fast Track ◽  
Rock Properties

Seismic inversion for geologic fractures and fractured media

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. C145-C161 ◽  
Author(s):  
Xiaoqin Cui ◽  
Edward S. Krebes ◽  
Laurence R. Lines
Keyword(s):  
Seismic Data ◽  
Wave Reflection ◽  
Rock Properties ◽  
Reflection And Transmission ◽  
Transmission Coefficients ◽  
Avo Inversion ◽  
Impedance Contrast ◽  
Fractured Medium ◽  
Pp Wave ◽  
Contact Part

Amplitude variation with offset (AVO) inversion attempts to use the available surface seismic data to estimate the density, P-wave velocity, and S-wave velocity of the earth model. Under linear slip interface theory, synthetic seismograms for models with fractures prove that fractures are also reflection generators. Consequently, observed reflections are not necessarily due to lithologic variations only, but they could be due in part to the effect of fractures. To obtain approximate equations for AVO inversion for fractured media, denoted by AVO with fracture (AVOF), we derived new equations for PP-wave reflection and transmission coefficients that are based on nonwelded contact boundary conditions. In particular, along with the fracture compliances, azimuth has also been taken into account in the equations because the fractures can have any orientation. The new approximate AVOF equations for a horizontally fractured medium with impedance contrast are developed by simplifying the equations for the new PP-wave reflection and transmission coefficients. In the new approximate AVOF equations, the reflection coefficients are divided into a welded contact part (a conventional impedance contrast part) and a nonwelded contact part (a fracture part). This makes the equations flexible enough to separately invert for the rock properties of the fracture and the background medium in the case of a fractured medium with impedance contrast. The new approximate AVOF equations state that fractures could cause the seismic reflectivity to be frequency dependent, and that the fractures not only influence the wave amplitude but also change the wave phase. The linear least-squares and nonlinear conjugate gradient inversion algorithms are applied to estimate the elastic reflectivity using the new approximate AVOF equations. The inverted results for seismic data for a horizontally fractured medium with impedance contrast are evaluated to find a more accurate delineation of the subsurface rock properties.

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.

Geophysics Bright Spots

2020 ◽  
Vol 39 (3) ◽  
pp. 212-213
Author(s):  
Jyoti Behura
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Cutting Edge ◽  
New Method ◽  
Seismic Data Processing ◽  
Light Bulb ◽  
Colorado School ◽  
Bright Spots ◽  
Outside The Box

Welcome to a new collection of Geophysics Bright Spots. I remember reading the first Bright Spots column as a student at Colorado School of Mines. Steve Hill, who conceived the wonderful idea of initiating this column, was my instructor there for a course on seismic data processing. He is a brilliant teacher — always challenging his students to think outside the box and ever open to discussions and debates. Through this column, he exposed readers to cutting-edge research in the field of geophysics while providing a new and important platform for authors to reach industry practitioners. Below is a list of research the editors found interesting in the latest issue of Geophysics. If any of them pique your interest, please read the full Geophysics article. Maybe a light bulb will go off in your head for a new method or algorithm.

Sign in / Sign up

Export Citation Format

Share Document