How Reliable is Statistical Wavelet Estimation?

Edgar, J.A.; Baan, Mirko van der

doi:10.3997/2214-4609.20144669

Cited by 3 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The associated synthetic trace can then be calculated through convolution of this reflectivity series with a suitable source waveform. Traditionally, such a source waveform is deterministically estimated from the field seismic data using well logs (e.g., Edgar and van der Baan ). However, for shallow‐water, very‐high‐resolution seismic reflection data, the source waveforms are either intrinsically known (in the case of Chirp data; Gutowski et al ) or can easily be derived from real, far‐field measurements of the downgoing wavefield during static field trials (for Boomer and Sparker data).…”

Section: Inversion Approachmentioning

confidence: 99%

Deriving shallow‐water sediment properties using post‐stack acoustic impedance inversion

Vardy

2015

Near Surface Geophysics

View full text Add to dashboard Cite

In contrast to the use of marine seismic reflection techniques for reservoir‐scale applications, where seismic inversion for quantitative sediment analysis is common, shallow‐waterm, very‐high‐resolution seismic reflection data are seldom used for more than qualitative reflection interpretation. Here, a quantitative analysis of very‐high‐resolution marine seismic reflection profiles from a shallow‐water (<50 m water depth) fjord in northern Norway is presented. Acquired using Sparker, Boomer, and Chirp sources, the failure plane of multiple local landslides correlates with a composite reflection that reverses polarity to the south. Using a genetic algorithm, a 1D post‐stack acoustic impedance inversion of all three profiles is performed, calibrating against multi‐sensor core logger (MSCL) data from cores. Using empirical relationships the resulting impedance profiles are related to remote sediment properties, including: P‐wave velocity; density; mean grain size; and porosity. The composite reflector is consistently identified by all three data sources as a finer‐grained (by one ϕ), lower density (c. 0.2 g/cm3 less than background) thin bed, with an anomalous low velocity zone (at least 100 m/s lower than background) associated with the polarity reversal to the south. Such a velocity contrast is consistent with an accumulation of shallow free gas trapped within the finer‐grain, less permeable layer. This study represents the first application of acoustic impedance inversion to very‐high‐resolution seismic reflection data and demonstrates the potential for directly relating seismic reflection data with sediment properties using a variety of commonly used shallow seismic profiling sources.

show abstract

Section: Inversion Approachmentioning

confidence: 99%

Deriving shallow‐water sediment properties using post‐stack acoustic impedance inversion

Vardy

2015

Near Surface Geophysics

View full text Add to dashboard Cite

show abstract

“…Step 1 Seismic inversion as used here and described above requires that the wavelet be known (Cui and Margrave 2014;Edgar and Baan 2011;Yi et al 2013). The wavelet is extracted using both the wells KA and TE keeping the phase constant and using the window length of 150 ms, and taper length of 20 ms.…”

Section: Model-based Seismic Inversion Of 3d Seismic Volumementioning

confidence: 99%

Assessment of Paleocene to lower Oligocene formations and basement to estimate the potential hydrocarbon reservoirs using seismic inversion: a case study in the Upper Assam Shelf, India

Rai

Singha

Chatterjee

2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

The upper Assam shelf is a self-slope basin in north-east India, filled with nearly 7 km of sedimentary rocks of tertiary period with the granite basement and various oil fields along the border of the Naga thrust. The major producing fields are structural and strati-structural. The study area is placed in between the Mikir hills and Naga thrust. The objective of the study is to identify potential hydrocarbon reservoir zones in the geologically complex south upper of the Assam shelf using estimates of acoustic impedance and porosity derived by 3D post-stack seismic inversion. Well data, such as sonic velocity and density logs, from two wells (namely, KA and TE) are used in the inversion and validation of results. Inversion results are used to build a geological model in the form of acoustic impedance from which we derive 3D porosity cube which are used for hydrocarbon potential in the Paleocene to lower Oligocene sands, and the Precambrian basement. Although the amplitude maps provide an indication of potential reservoirs, the extent of these zones are much better identified in the inverted impedance maps and the corresponding estimated high-porosity zones. The analysis predicted the potential reservoir rocks in the Sylhet, Kopili and Barail formations, in which the Sylhet and Kopili appear to have good potential zones. Near the vicinity of the Naga thrust belt, the proximity of potential reservoir is predicted in the Kopili, Sylhet formation and in the fractured basement, respectively.

show abstract

“…). The use of controlled‐phase acquisition and processing strategies can help control seismic phase (Trantham ), but mismatches between recorded seismic data and synthetic traces constructed from well log data occur very frequently (Edgar and Van der Baan ). The wavelet estimation methods fall mainly into two categories: (1) purely statistical ways and (2) the use of well log data (White and Simm ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the seismic wavelet through homomorphic deconvolution and well log data: application on well‐to‐seismic tie procedure

Macedo

Figueiredo

Sousa

et al. 2019

Geophysical Prospecting

View full text Add to dashboard Cite

A B S T R A C TWavelet estimation and well-tie procedures are important tasks in seismic processing and interpretation. Deconvolutional statistical methods to estimate the proper wavelet, in general, are based on the assumptions of the classical convolutional model, which implies a random process reflectivity and a minimum-phase wavelet. The homomorphic deconvolution, however, does not take these premises into account. In this work, we propose an approach to estimate the seismic wavelet using the advantages of the homomorphic deconvolution and the deterministic estimation of the wavelet, which uses both seismic and well log data. The feasibility of this approach is verified on well-to-seismic tie from a real data set from Viking Graben Field, North Sea, Norway. The results show that the wavelet estimated through this methodology produced a higher quality well tie when compared to methods of estimation of the wavelet that consider the classical assumptions of the convolutional model.

show abstract

How Reliable is Statistical Wavelet Estimation?

Cited by 3 publications

References 13 publications

Deriving shallow‐water sediment properties using post‐stack acoustic impedance inversion

Deriving shallow‐water sediment properties using post‐stack acoustic impedance inversion

Assessment of Paleocene to lower Oligocene formations and basement to estimate the potential hydrocarbon reservoirs using seismic inversion: a case study in the Upper Assam Shelf, India

Estimation of the seismic wavelet through homomorphic deconvolution and well log data: application on well‐to‐seismic tie procedure

Contact Info

Product

Resources

About