Low Frequency Model Update for Thin-bed Coals - Integrating Spectral Decomposition and Inversion for Enhanced Imaging

Author(s):  
B. Hak ◽  
P. V. Angelov ◽  
P. R. Mesdag ◽  
R. van Eykenhof
2015 ◽  
Author(s):  
P.R. Mesdag* ◽  
M.R. Saberi ◽  
C. Mangat ◽  
P. Geoph

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>


Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. R11-R28 ◽  
Author(s):  
Kun Xiang ◽  
Evgeny Landa

Seismic diffraction waveform energy contains important information about small-scale subsurface elements, and it is complementary to specular reflection information about subsurface properties. Diffraction imaging has been used for fault, pinchout, and fracture detection. Very little research, however, has been carried out taking diffraction into account in the impedance inversion. Usually, in the standard inversion scheme, the input is the migrated data and the assumption is taken that the diffraction energy is optimally focused. This assumption is true only for a perfectly known velocity model and accurate true amplitude migration algorithm, which are rare in practice. We have developed a new approach for impedance inversion, which takes into account diffractive components of the total wavefield and uses the unmigrated input data. Forward modeling, designed for impedance inversion, includes the classical specular reflection plus asymptotic diffraction modeling schemes. The output model is composed of impedance perturbation and the low-frequency model. The impedance perturbation is estimated using the Bayesian approach and remapped to the migrated domain by the kinematic ray tracing. Our method is demonstrated using synthetic and field data in comparison with the standard inversion. Results indicate that inversion with taking into account diffraction can improve the acoustic impedance prediction in the vicinity of local reflector discontinuities.


1980 ◽  
Vol 68 (2) ◽  
pp. 482-488 ◽  
Author(s):  
Mark H. Holmes

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. A45-A50
Author(s):  
Zhishuai Zhang ◽  
Zijun Fang ◽  
Joe Stefani ◽  
James DiSiena ◽  
Dimitri Bevc ◽  
...  

We modeled cross-well strain/strain rate responses of fiber optic sensing, including distributed strain sensing (DSS) and low-frequency distributed acoustic sensing (DAS), to hydraulic stimulation. DSS and low-frequency DAS have been used to measure strain or the strain rate to characterize hydraulic fractures. However, the current application of DSS/DAS is limited to acquisition, processing, and qualitative interpretations. The lack of geomechanical models hinders the development of the technology toward quantitative interpretation and inversion. We have developed a strategy to use the displacement discontinuity method to model the strain field around kinematically propagating fractures. For a horizontal monitoring well, modeling results were able to explain the heart-shaped extending pattern before a fracture hit, the polarity flip due to fracture interaction during stimulation, and the V-shaped pattern when a fracture does not intersect with the monitoring well. For a vertical monitoring well, modeling shows the different characters of strain rate responses when a fracture is near and far away from a vertical monitoring well. We also investigated the effects of fractures with various geometries such as elliptic and layered fractures. We compared and verified the modeling with field data from the Hydraulic Fracturing Test Site 2, a research experiment performed in the Permian Basin. Our modeling work can be used to identify patterns in field observations. The results also help to improve acquisition design and lay the groundwork for quantitative interpretation and inversion.


2020 ◽  
Vol 86 (5) ◽  
Author(s):  
A. Crestetto ◽  
F. Deluzet ◽  
D. Doyen

The purpose of this paper is to bridge kinetic plasma descriptions and low-frequency single-fluid models. More specifically, the asymptotics leading to magnetohydrodynamic regimes starting from the Vlasov–Maxwell system are investigated. The analogy with the derivation, from the Vlasov–Poisson system, of a fluid representation for ions coupled to the Boltzmann relation for electrons is also outlined. The aim is to identify asymptotic parameters explaining the transitions from one microscopic description to a macroscopic low-frequency model. These investigations provide groundwork for the derivation of multi-scale numerical methods, model coupling or physics-based preconditioning.


2020 ◽  
Vol 148 (6) ◽  
pp. 3992-4001
Author(s):  
Shuyuan Du ◽  
Jingpu Cao ◽  
Shihong Zhou ◽  
Yubo Qi ◽  
Lei Jiang ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1059
Author(s):  
Yingjie Tang ◽  
Zheren Zhang ◽  
Zheng Xu

Damping circuits are installed inside the converter valve to limit commutation overshoots. They have significant effects on the valve’s turn-off performances, which should be carefully considered in parameter design. First, the calculation models for the turn-off process are discussed, including the conventional low frequency model and the broadband model. Then, it is found that high-frequency equipment parameters have significant effects on the transient valve voltage, which means that the conventional analytical methods based on low-frequency models is not suitable for damping circuit parameter design. The relationships between the turn-off performances and damping circuit parameters have also been analyzed in detail with the broadband model. To achieve better economic efficiency, this paper proposes a novel method for damping circuit parameter optimization, which combines the electromagnetic transient (EMT) calculation and the numerical optimization. Last, the case study is carried out based on a practical ±1100 kV ultra-high-voltage direct-current (UHVDC) transmission project, which proves the reliability and flexibility of the proposed method.


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