Improved estimation of elastic attributes from prestack seismic data for reservoir characterization

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. R41-R53
Author(s):  
Yijie Zhou ◽  
Franklin Ruiz ◽  
Yequan Chen ◽  
Fan Xia
Keyword(s):  
Nonlinear Optimization ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Layer Structure ◽  
High Sensitivity ◽  
Optimization Techniques ◽  
Earth Model ◽  
L1 Norm ◽  
Linear Inversion ◽  
Elastic Impedance

Seismic derivable elastic attributes, e.g., elastic impedance, lambda-rho, mu-rho, and Poisson impedance (PI), are routinely being used for reservoir characterization practice. These attributes could be derived from inverted [Formula: see text], [Formula: see text], and density, and usually indicate high sensitivity to reservoir lithology and fluid. Due to the high sensitivity of such elastic attributes, errors or measurement noise associated with the acquisition, processing, and inversion of prestack seismic data will propagate through the inversion products, and will lead to even larger errors in the computed attributes. To solve this problem, we have developed a two-step cascade workflow that combines linear inversion and nonlinear optimization techniques for the improved estimation of elastic attributes and better prediction and delineation of reservoir lithology and fluids. The linear inversion in the first step is an inversion scheme with a sparseness assumption, based on L1-norm regularization. This step is used to select the major reflective layer locations, followed in the second step by a nonlinear optimization process with the predefined layer structure. The combination of these two procedures produces a reasonable blocky earth model with consistent elastic properties, including the ones that are sensitive to reservoir lithology and fluid change, and thus provides an accurate approach for seismic reservoir characterization. Using PI, as one of the target elastic attributes, as an example, this workflow has been successfully applied to synthetic and field data examples. The results indicate that our workflow improves the estimation of elastic attributes from the noisy prestack seismic data and may be used for the identification of the reservoir lithology and fluid.

Reservoir characterization over the Lille Prinsen and Ivar Aasen fields in the Norwegian North Sea using ocean-bottom-node seismic data — A case study

2021 ◽  
pp. 1-64
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Mikal Trulsvik ◽  
Adriana Citlali Ramirez ◽  
David Went ◽  
...  
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Late Triassic ◽  
Ocean Bottom ◽  
Impedance Inversion ◽  
High Area ◽  
Elastic Impedance

An integrated workflow is proposed for estimating elastic parameters within the Late Triassic Skagerrak Formation, the Middle Jurassic Sleipner and Hugin Formations, the Paleocene Heimdal Formation and Eocene Grid Formation in the Utsira High area of the Norwegian North Sea. The proposed workflow begins with petrophysical analysis carried out at the available wells. Next, model-based prestack simultaneous impedance inversion outputs were derived, and attempts were made to estimate the petrophysical parameters (volume of shale, porosity, and water saturation) from seismic data using extended elastic impedance. On not obtaining convincing results, we switched over to multiattribute regression analysis for estimating them, which yielded encouraging results. Finally, the Bayesian classification approach was employed for defining different facies in the intervals of interest.

Anisotropic 3D elastic full-wavefield inversion to directly estimate elastic properties and its role in interpretation

2021 ◽  
Vol 40 (4) ◽  
pp. 277-286
Author(s):  
Haiyang Wang ◽  
Olivier Burtz ◽  
Partha Routh ◽  
Don Wang ◽  
Jake Violet ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
P Wave ◽  
Quality Estimation ◽  
Amplitude Variation ◽  
Linear Inversion ◽  
Amplitude Variation With Offset ◽  
P Wave Velocity ◽  
Wavefield Inversion

Elastic properties from seismic data are important to determine subsurface hydrocarbon presence and have become increasingly important for detailed reservoir characterization that aids to derisk specific hydrocarbon prospects. Traditional techniques to extract elastic properties from seismic data typically use linear inversion of imaged products (migrated angle stacks). In this research, we attempt to get closer to Tarantola's visionary goal for full-wavefield inversion (FWI) by directly obtaining 3D elastic properties from seismic shot-gather data with limited well information. First, we present a realistic 2D synthetic example to show the need for elastic physics in a strongly elastic medium. Then, a 3D field example from deepwater West Africa is used to validate our workflow, which can be practically used in today's computing architecture. To enable reservoir characterization, we produce elastic products in a cascaded manner and run 3D elastic FWI up to 50 Hz. We demonstrate that reliable and high-resolution P-wave velocity can be retrieved in a strongly elastic setting (i.e., with a class 2 or 2P amplitude variation with offset response) in addition to higher-quality estimation of P-impedance and VP/VS ratio. These parameters can be directly used in interpretation, lithology, and fluid prediction.

scholarly journals Amplitude Variation with Angle Inversion for New Parameterized Porosity and Fluid Bulk Modulus

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shengjun Li ◽  
Bingyang Liu ◽  
Jianhu Gao ◽  
Huaizhen Chen
Keyword(s):  
Reflection Coefficient ◽  
Bulk Modulus ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Signal To Noise Ratio ◽  
Empirical Relationship ◽  
Amplitude Variation ◽  
Fluid Identification ◽  
Saturated Rock ◽  
Elastic Impedance

Estimating porosity and fluid bulk modulus is an important goal of reservoir characterization. Based on the model of fluid substitution, we first propose a simplified bulk modulus of a saturated rock as a function of bulk moduli of minerals and fluids, in which we employ an empirical relationship to replace the bulk modulus of dry rock with that of minerals and a new parameterized porosity. Using the simplified bulk modulus, we derive a PP-wave reflection coefficient in terms of the new parameterized porosity and fluid bulk modulus. Focusing on reservoirs embedded in rocks whose lithologies are similar, we further simplify the derived reflection coefficient and present elastic impedance that is related to porosity and fluid bulk modulus. Based on the presented elastic impedance, we establish an approach of employing seismic amplitude variation with offset/angle to estimate density, new parameterized porosity, and fluid bulk modulus. We finally employ noisy synthetic seismic data and real datasets to verify the stability and reliability of the proposed inversion approach. Test on synthetic seismic data illustrates that the proposed inversion approach can produce stable inversion results in the case of signal-to-noise ratio (SNR) of 2, and applying the approach to real datasets, we conclude that reliably results of porosity and fluid bulk modulus are obtained, which is useful for fluid identification and reservoir characterization.

Backscattered Electron Imaging of GaAs/AlGaAs Super Lattice Structures with an Ultra-High- Resolution SEM

1988 ◽  
Vol 46 ◽  
pp. 204-205
Author(s):  
Kazumichi Ogura ◽  
Michael M. Kersker
Keyword(s):  
High Resolution ◽  
Layer Structure ◽  
High Sensitivity ◽  
Beam Current ◽  
Electron Beam Current ◽  
Lattice Structures ◽  
Super Lattice ◽  
Different Types ◽  
Backscattered Electron ◽  
Electron Imaging

Backscattered electron (BE) images of GaAs/AlGaAs super lattice structures were observed with an ultra high resolution (UHR) SEM JSM-890 with an ultra high sensitivity BE detector. Three different types of super lattice structures of GaAs/AlGaAs were examined. Each GaAs/AlGaAs wafer was cleaved by a razor after it was heated for approximately 1 minute and its crosssectional plane was observed.First, a multi-layer structure of GaAs (100nm)/AlGaAs (lOOnm) where A1 content was successively changed from 0.4 to 0.03 was observed. Figures 1 (a) and (b) are BE images taken at an accelerating voltage of 15kV with an electron beam current of 20pA. Figure 1 (c) is a sketch of this multi-layer structure corresponding to the BE images. The various layers are clearly observed. The differences in A1 content between A1 0.35 Ga 0.65 As, A1 0.4 Ga 0.6 As, and A1 0.31 Ga 0.69 As were clearly observed in the contrast of the BE image.

scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

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