AVO inversion of Troll Field data

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1589-1602 ◽  
Author(s):  
Arild Buland ◽  
Martin Landrø ◽  
Mona Andersen ◽  
Terje Dahl
Keyword(s):  
Least Squares ◽  
Field Data ◽  
Forward Modeling ◽  
S Wave ◽  
Data Set ◽  
Wave Velocities ◽  
Avo Inversion ◽  
Common Depth Point ◽  
Elastic Inversion ◽  
Water Bottom

A stratigraphic elastic inversion scheme has been applied to a data set from the Troll East Field, offshore Norway. The objective of the present work is to obtain estimates of the P‐ and S‐wave velocities and densities of the subsurface. The inversion is carried out on τ − p transformed common depth‐point (CMP) gathers. The forward modeling is performed by convolving a wavelet with the reflectivity that includes water‐bottom multiples, transmission effects, and absorption and array effects. A damped Gauss‐Newton algorithm is used to minimize a least‐squares misfit function. Inversion results show good correlation between the estimated [Formula: see text] ratios and the lithologies in the wells. The [Formula: see text] ratio is estimated to 2.1–3.0 for shale and 1.6–2.0 for sandstones. In the reservoir, the [Formula: see text] ratio is estimated to 1.55 in the gas sand and to 1.62 below the fluid contact.

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

EXTRACTING SUB-SURFACE INFORMATION FROM LONG OFFSET P-WAVE DATA—A CHEAPER ALTERNATIVE TO MULTICOMPONENT OBC FOR EXPLORATION?

2002 ◽  
Vol 42 (1) ◽  
pp. 627
Author(s):  
R.G. Williams ◽  
G. Roberts ◽  
K. Hawkins
Keyword(s):  
Seismic Energy ◽  
Higher Order ◽  
P Wave ◽  
S Wave ◽  
Additional Information ◽  
Wave Data ◽  
Wave Velocities ◽  
Avo Inversion ◽  
Long Offset ◽  
Multicomponent Data

Seismic energy that has been mode converted from pwave to s-wave in the sub-surface may be recorded by multi-component surveys to obtain information about the elastic properties of the earth. Since the energy converted to s-wave is missing from the p-wave an alternative to recording OBC multi-component data is to examine p-wave data for the missing energy. Since pwave velocities are generally faster than s-wave velocities, then for a given reflection point the converted s-wave signal reaches the surface at a shorter offset than the equivalent p-wave information. Thus, it is necessary to record longer offsets for p-wave data than for multicomponent data in order to measure the same information.A non-linear, wide-angle (including post critical) AVO inversion has been developed that allows relative changes in p-wave velocities, s-wave velocities and density to be extracted from long offset p-wave data. To extract amplitudes at long offsets for this inversion it is necessary to image the data correctly, including correcting for higher order moveout and possibly anisotropy if it is present.The higher order moveout may itself be inverted to yield additional information about the anisotropy of the sub-surface.

Derivation of seismic cone interval velocities utilizing forward modeling and the downhill simplex method

2002 ◽  
Vol 39 (5) ◽  
pp. 1181-1192 ◽  
Author(s):  
Erick J Baziw
Keyword(s):  
Elastic Constants ◽  
Simplex Method ◽  
Forward Modeling ◽  
S Wave ◽  
Cone Penetration ◽  
Interval Velocity ◽  
Wave Velocities ◽  
Downhill Simplex Method ◽  
Downhill Simplex ◽  
Interval Velocities

The seismic cone penetration test (SCPT) has proven to be a very valuable geotechnical tool in facilitating the determination of low strain (<10–4%) in situ compression (P) and shear (S) wave velocities. The P- and S-wave velocities are directly related to the soil elastic constants of Poisson's ratio, shear modulus, bulk modulus, and Young's modulus. The accurate determination of P- and S-wave velocities from the recorded seismic cone time series is of paramount importance to the evaluation of reliable elastic constants. Furthermore, since the shear and compression wave velocities are squared in deriving the elastic constants, small variations in the estimated velocities can cause appreciable errors. The standard techniques implemented in deriving SCPT interval velocities rely upon obtaining reference P- and S-wave arrival times as the probe is advanced into the soil profile. By assuming a straight ray travel path from the source to the SCPT seismic receiver and calculating the relative reference arrival time differences, interval SCPT velocities are obtained. The forward modeling – downhill simplex method (FMDSM) outlined in this paper offers distinct advantages over conventional SCPT velocity profile estimation methods. Some of these advantages consist of the allowance of ray path refraction, greater sophistication in interval velocity determination, incorporation of measurement weights, and meaningful interval velocity accuracy estimators.Key words: seismic cone penetration testing (SCPT), downhill simplex method (DSM), forward modeling, Fermat's principle, weighted least squares (l2 norm), cost function.

The impact of common‐offset migration on porosity estimation by AVO inversion

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 755-762 ◽  
Author(s):  
Arild Buland ◽  
Martin Landrø
Keyword(s):  
P Wave ◽  
High Porosity ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
The North ◽  
Avo Inversion ◽  
Time Migration ◽  
The Impact ◽  
Porosity Estimation

The impact of prestack time migration on porosity estimation has been tested on a 2-D seismic line from the Valhall/Hod area in the North Sea. Porosity is estimated in the Cretaceous chalk section in a two‐step procedure. First, P-wave and S-wave velocity and density are estimated by amplitude variation with offset (AVO) inversion. These parameters are then linked to porosity through a petrophysical rock data base based on core plug analysis. The porosity is estimated both from unmigrated and prestack migrated seismic data. For the migrated data set, a standard prestack Kirchhoff time migration is used, followed by simple angle and amplitude corrections. Compared to modern high‐cost, true amplitude migration methods, this approach is faster and more practical. The test line is structurally fairly simple, with a maximum dip of 5°; but the results differ significantly, depending on whether migration is applied prior to the inversion. The maximum difference in estimated porosity is of the order of 10% (about 50% relative change). High‐porosity zones estimated from the unmigrated data were not present on the porosity section estimated from the migrated data.

Measurement of the shear slowness of slow formations from monopole logging-while-drilling sonic logs

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. D45-D52
Author(s):  
Yuanda Su ◽  
Xinding Fang ◽  
Xiaoming Tang
Keyword(s):  
Numerical Modeling ◽  
Wave Velocity ◽  
Fluid Velocity ◽  
S Wave ◽  
Data Set ◽  
Acoustic Logging ◽  
Wave Velocities ◽  
Logging While Drilling ◽  
Sonic Logs ◽  
Refracted Waves

Acoustic logging-while-drilling (LWD) is used to measure formation velocity/slowness during drilling. In a fast formation, in which the S-wave velocity is higher than the borehole-fluid velocity, monopole logging can be used to obtain P- and S-wave velocities by measuring the corresponding refracted waves. In a slow formation, in which the S-wave velocity is less than the borehole-fluid velocity, because the fully refracted S-wave is missing, quadrupole logging has been developed and used for S-wave slowness measurement. A recent study based on numerical modeling implies that monopole LWD can generate a detectable transmitted S-wave in a slow formation. This nondispersive transmitted S-wave propagates at the formation S-wave velocity and thus can be used for measuring the S-wave slowness of a slow formation. We evaluate a field example to demonstrate the applicability of monopole LWD in determining the S-wave slowness of slow formations. We compare the S-wave slowness extracted from a monopole LWD data set acquired in a slow formation and the result derived from the quadrupole data recorded in the same logging run. The results indicated that the S-wave slowness can be reliably determined from monopole LWD sonic data in fairly slow formations. However, we found that the monopole approach is not applicable to very slow formations because the transmitted S-wave becomes too weak to detect when the formation S-wave slowness is much higher than the borehole-fluid slowness.

Least-squares Gaussian beam migration

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. S87-S100 ◽  
Author(s):  
Hao Hu ◽  
Yike Liu ◽  
Yingcai Zheng ◽  
Xuejian Liu ◽  
Huiyi Lu
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Spatial Resolution ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Seismic Acquisition ◽  
Gaussian Beam Migration ◽  
Least Squares Migration

Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic acquisition, balance the subsurface illumination, and improve the spatial resolution of the image, but it requires iterations of migration and demigration to obtain the desired subsurface reflectivity model. The computational efficiency and accuracy of migration and demigration operators are crucial for applying the algorithm. We have developed a test of the feasibility of using the Gaussian beam as the wavefield extrapolating operator for the LSM, denoted as least-squares Gaussian beam migration. Our method combines the advantages of the LSM and the efficiency of the Gaussian beam propagator. Our numerical evaluations, including two synthetic data sets and one marine field data set, illustrate that the proposed approach could be used to obtain amplitude-balanced images and to broaden the bandwidth of the migrated images in particular for the low-wavenumber components.

scholarly journals Image domain least-squares migration with a Hessian matrix estimated by non-stationary matching filters

2019 ◽  
Vol 17 (1) ◽  
pp. 148-159 ◽  
Author(s):  
Song Guo ◽  
Huazhong Wang
Keyword(s):  
Least Squares ◽  
Numerical Experiments ◽  
Field Data ◽  
Hessian Matrix ◽  
Computational Cost ◽  
Image Deblurring ◽  
Data Set ◽  
Image Domain ◽  
Slow Convergence ◽  
Least Squares Migration

Abstract Assuming that an accurate background velocity is obtained, least-squares migration (LSM) can be used to estimate underground reflectivity. LSM can be implemented in either the data domain or image domain. The data domain LSM (DDLSM) is not very practical because of its huge computational cost and slow convergence rate. The image domain LSM (IDLSM) might be a flexible alternative if estimating the Hessian matrix using a cheap and accurate approach. It has practical potential to analyse convenient Hessian approximation methods because the Hessian matrix is too huge to compute and save. In this paper, the Hessian matrix is approximated with non-stationary matching filters. The filters are calculated to match the conventional migration image to the demigration/remigration image. The two images are linked by the Hessian matrix. An image deblurring problem is solved with the estimated filters for the IDLSM result. The combined sparse and total variation regularisations are used to produce accurate and reasonable inversion results. The numerical experiments based on part of Sigsbee model, Marmousi model and a 2D field data set illustrate that the non-stationary matching filters can give a good approximation for the Hessian matrix, and the results of the image deblurring problem with combined regularisations can provide high-resolution and true-amplitude reflectivity estimations.

Sign in / Sign up

Export Citation Format

Share Document