Surface‐related multiple elimination on land seismic data—Strategies via case studies

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 719-734 ◽  
Author(s):  
Panos G. Kelamis ◽  
D. J. Verschuur
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Input Data ◽  
Multiple Attenuation ◽  
Processing Strategies ◽  
Processing Step ◽  
Subsequent Elimination ◽  
Seismic Amplitudes ◽  
Multiple Elimination

Three processing strategies for the estimation and subsequent elimination of surface‐related multiple energy on land seismic data are presented. They can be applied in a prestack mode (to shot and common‐midpoint gathers) or in a poststack mode. The algorithm for the multiple attenuation is based on wave theoretical principles in which the data are used as a prediction operator. The estimated multiples are then adaptively subtracted from the input data to obtain primary‐only data. A processing step prior to applying multiple elimination is an important component of these methodologies, particularly in the prestack analysis. Its aim is to regularize the data, improve the S/N ratio, and balance the seismic amplitudes. This results in smooth prediction operators. The effectiveness of these schemes in suppressing multiples is demonstrated with a number of case studies involving processing land seismic data.

scholarly journals Practical Implementation of Multiple Attenuation Methods on 2D Deepwater Seismic Data : Seram Sea Case Study

2017 ◽  
Vol 32 (1) ◽  
Author(s):  
Tumpal Bernhard Nainggolan ◽  
Deny Setiady
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Complex Structure ◽  
Least Square ◽  
Inversion Method ◽  
Practical Implementation ◽  
Proper Solution ◽  
Multiple Attenuation ◽  
Multiple Elimination ◽  
Water Bottom

Some deepwater multiple attenuation processing methods have been developed in the past with partial success. The success of surface multiple attenuation relies on good water bottom reflections for most deepwater marine situations. It brings the bigger ability to build an accurate water bottom multiple prediction model. Major challenges on 2D deepwater seismic data processing especially such a geologically complex structure of Seram Sea, West Papua – Indonesia are to attenuate surface related multiple and to preserve the primary data. Many multiple attenuation methods have been developed to remove surface multiple on these seismic data including most common least-squares, prediction-error filtering and more advanced Radon transform.Predictive Deconvolution and Surface Related Multiple Elimination (SRME) method appears to be a proper solution, especially in complex structure where the above methods fail to distinguish interval velocity difference between primaries and multiples. It does not require any subsurface info as long as source signature and surface reflectivity are provided. SRME method consists of 3 major steps: SRME regularization, multiple modeling and least-square adaptive subtraction. Near offset regularization is needed to fill the gaps on near offset due to unrecorded near traces during the acquisition process. Then, isolating primaries from multiples using forward modeling. Inversion method by subtraction of input data with multiple models to a more attenuated multiple seismic section.Results on real 2D deepwater seismic data show that SRME method as the proper solution should be considered as one of the practical implementation steps in geologically complex structure and to give more accurate seismic imaging for the interpretation.Keywords : multiple attenuation, 2D deepwater seismic, Radon transform, Surface Related Multiple Elimination (SRME). Banyak metode atenuasi pengulangan ganda dikembangkan pada pengolahan data seismik dengan tingkat keberhasilan yang rendah pada masa lalu. Keberhasilan dalam atenuasi pengulangan ganda permukaan salah satunya bergantung pada hasil gelombang pantul pada batas dasar laut dan permukaan pada hampir seluruh survei seismik laut. Hal tersebut menentukan keakuratan dalam membuat model prediksi pengulangan ganda dasar laut dan permukaan air. Tantangan utama dalam pemrosesan data seismik 2D laut dalam khususnya struktur geologi kompleks seperti Laut Seram, Papua Barat – Indonesia adalah pada kegiatan menekan pengulangan ganda permukaan sekaligus mempertahankan data primer. Beberapa metode yang dikembangkan untuk menghilangkan pengulangan ganda permukaan pada data seismik seperti least-square, filter prediksi kesalahan dan transformasi Radon.Dekonvolusi Prediktif dan Metode Surface Related Multiple Elimination (SRME) digunakan sebagai solusi yang baik pada struktur kompleks dimana metode-metode lain gagal untuk memisahkan perbedaan kecepatan interval data primer dan pengulangan ganda. Metode tersebut tidak membutuhkan informasi bawah permukaan selain parameter sumber dan reflektivitas permukaan. Metode SRME terdiri dari 3 tahapan utama : regularisasi SRME, pemodelan pengulangan ganda dan pengurangan adaktif least-square. Regularisasi near offset diperlukan untuk mengisi kekosongan pada near offset yang disebabkan oleh adanya sejumlah tras terdekat yang tidak terekam selama akuisisi. Pemodelan maju digunakan untuk memisahkan data primer dan pengulangan ganda kemudian inversi dengan pengurangan input data dengan model multiple.Hasil pada data seismik 2D laut dalam menunjukkan bahwa metode SRME layak diterapkan sebagai salah satu pengembangan metode atenuasi multiple permukaan serta untuk meningkatkan akurasi data seismik terutama untuk struktur geologi kompleks.Kata kunci : peredaman pengulangan ganda (multiple), seismik 2D laut dalam, transformasi Radon, Surface Related Multiple Attenuation (SRME).

Multiple attenuation in complex geology with a pattern-based approach

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. V97-V107 ◽  
Author(s):  
Antoine Guitton
Keyword(s):  
Seismic Data ◽  
Prediction Error ◽  
Input Data ◽  
Multiple Model ◽  
Ideal Case ◽  
Multiple Attenuation ◽  
Time Space ◽  
Multiple Prediction ◽  
Complex Geology ◽  
Space Variant

Primaries (signal) and multiples (noise) often exhibit different kinematics and amplitudes (i.e., patterns) in time and space. Multidimensional prediction-error filters (PEFs) approximate these patterns to separate noise and signal in a least-squares sense. These filters are time-space variant to handle the nonstationarity of multioffset seismic data. PEFs for the primaries and multiples are estimated from pattern models. In an ideal case where accurate pattern models of both noise and signal exist, the pattern-based method recovers the primaries while preserving their amplitudes. In the more general case, the pattern model of the multiples is obtained by using the data as prediction operators. The pattern model of the primaries is obtained by convolving the noise PEFs with the input data. In this situation, 3D PEFs are preferred to separate (in prestack data) the multiples properly and to preserve the primaries. Comparisons of the proposed method with adaptive subtraction with an [Formula: see text] norm demonstrate that for a given multiple model, the pattern-based approach generally attenuates the multiples and recovers the primaries better. In addition, tests on a 2D line from the Gulf of Mexico demonstrate that the proposed technique copes fairly well with modeling inadequacies present in the multiple prediction.

scholarly journals Efficient 2D multiple attenuation using SRME with curvelet-domain subtraction

2022 ◽  
Vol 43 (1) ◽  
Author(s):  
Szu-Ying Lai ◽  
Yunung Nina Lin ◽  
Ho-Han Hsu
Keyword(s):  
Finite Number ◽  
Least Squares ◽  
Seismic Data ◽  
Parameter Determination ◽  
Prediction Errors ◽  
Geological Condition ◽  
Multiple Model ◽  
Multiple Attenuation ◽  
Time Space ◽  
Multiple Elimination

AbstractSurface Related Multiple Elimination (SRME) usually suffers the issue of either over-attenuation that damages the primaries or under-attenuation that leaves strong residual multiples. This dilemma happens commonly when SRME is combined with least-squares subtraction. Here we introduce a more sophisticated subtraction approach that facilitates better separation of multiples from primaries. Curvelet-domain subtraction transforms both the data and the multiple model into the curvelet domain, where different frequency bands (scales) and event directions (orientations) are represented by a finite number of curvelet coefficients. When combined with adaptive subtraction in the time–space domain, this method can handle model prediction errors to achieve effective subtraction. We demonstrate this method on two 2D surveys from the TAiwan Integrated GEodynamics Research (TAIGER) project. With a careful parameter determination flow, our result shows curvelet-domain subtraction outperforms least-squares subtraction in all geological settings. We also present one failed case where specific geological condition hinders proper multiple subtraction. We further demonstrate that even for data acquired with short cables, curvelet-domain subtraction can still provide better results than least-squares subtraction. We recommend this method as the standard processing flow for multi-channel seismic data.

Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. O9-O17 ◽  
Author(s):  
Upendra K. Tiwari ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Elastic Model ◽  
Quality Factors ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Estimated Parameters ◽  
Model Parameterization ◽  
The Earth ◽  
Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

Finding Approximate Shape Regularities for Reverse Engineering

2001 ◽  
Vol 1 (4) ◽  
pp. 282-290 ◽  
Author(s):  
F. C. Langbein ◽  
B. I. Mills ◽  
A. D. Marshall ◽  
R. R. Martin
Keyword(s):  
Reverse Engineering ◽  
Small Groups ◽  
Input Data ◽  
Boundary Representation ◽  
Range Data ◽  
Engineering Systems ◽  
Post Processing ◽  
Processing Step ◽  
3D Range Data

Current reverse engineering systems can generate boundary representation (B-rep) models from 3D range data. Such models suffer from inaccuracies caused by noise in the input data and algorithms. The quality of reverse engineered geometric models can be improved by finding candidate shape regularities in such a model, and constraining the model to meet a suitable subset of them, in a post-processing step called beautification. This paper discusses algorithms to detect such approximate regularities in terms of similarities between feature objects describing properties of faces, edges and vertices, and small groups of these elements in a B-rep model with only planar, spherical, cylindrical, conical and toroidal faces. For each group of similar feature objects they also seek special feature objects which may represent the group, e.g. an integer value which approximates the radius of similar cylinders. Experiments show that the regularities found by the algorithms include the desired regularities as well as spurious regularities, which can be limited by an appropriate choice of tolerances.

LONG-PERIOD SURFACE-RELATED MULTIPLE SUPPRESSION IN 2D MARINE SEISMIC DATA USING PREDICTIVE DECONVOLUTION AND COMBINATION OF SURFACE-RELATED MULTIPLE ELIMINATION AND PARABOLIC RADON FILTERING

2021 ◽  
Author(s):  
Pimpawee Sittipan ◽  
Pisanu Wongpornchai
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Petroleum Reservoirs ◽  
The United States ◽  
Multiple Reflections ◽  
Long Period ◽  
Predictive Deconvolution ◽  
Short Period ◽  
Marine Seismic ◽  
Multiple Elimination

Some of the important petroleum reservoirs accumulate beneath the seas and oceans. Marine seismic reflection method is the most efficient method and is widely used in the petroleum industry to map and interpret the potential of petroleum reservoirs. Multiple reflections are a particular problem in marine seismic reflection investigation, as they often obscure the target reflectors in seismic profiles. Multiple reflections can be categorized by considering the shallowest interface on which the bounces take place into two types: internal multiples and surface-related multiples. Besides, the multiples can be categorized on the interfaces where the bounces take place, a difference between long-period and short-period multiples can be considered. The long-period surface-related multiples on 2D marine seismic data of the East Coast of the United States-Southern Atlantic Margin were focused on this research. The seismic profile demonstrates the effectiveness of the results from predictive deconvolution and the combination of surface-related multiple eliminations (SRME) and parabolic Radon filtering. First, predictive deconvolution applied on conventional processing is the method of multiple suppression. The other, SRME is a model-based and data-driven surface-related multiple elimination method which does not need any assumptions. And the last, parabolic Radon filtering is a moveout-based method for residual multiple reflections based on velocity discrimination between primary and multiple reflections, thus velocity model and normal-moveout correction are required for this method. The predictive deconvolution is ineffective for long-period surface-related multiple removals. However, the combination of SRME and parabolic Radon filtering can attenuate almost long-period surface-related multiple reflections and provide a high-quality seismic images of marine seismic data.

scholarly journals ATENUASI WATER-BOTTOM MULTIPLE DENGAN METODE TRANSFORMASI PARABOLIC RADON

2016 ◽  
Vol 12 (3) ◽  
pp. 145
Author(s):  
Subarsyah Subarsyah ◽  
Tumpal Benhard Nainggolan
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Wide Distribution ◽  
Seismic Section ◽  
Optimal Separation ◽  
Multiple Attenuation ◽  
Parabolic Radon Transform ◽  
Water Bottom

Interferensi water-bottom multipel terhadap reflektor primer menimbulkan efek bersifat destruktif yang menyebabkan penampang seismik menjadi tidak tepat akibat kehadiran reflektor semu. Teknik demultiple perlu diaplikasikan untuk mengatenuasi multipel. Transformasi parabolic radon merupakan teknik atenuasi multipel dengan metode pemisahan dalam domain radon. Multipel sering teridentifikasi pada penampang seismik. Untuk memperbaiki penampang seismik akan dilakukan dengan metode transformasi parabolic radon. Penerapan metode ini mengakibatkan reflektor multipel melemah dan tereduksi setelah dilakukan muting dalam domain radon terhadap zona multipel. Beberapa reflektor primer juga ikut melemah akibat pemisahan dalam domain radon yang kurang optimal, pemisahan akan optimal membutuhkan distribusi offset yang lebar. Kata kunci: Parabolic radon, multipel, atenuasi Water-bottom mutiple interference often destructively interfere with primary reflection that led to incorrect seismic section due to presence apparent reflector. Demultiple techniques need to be applied to attenuate the multiple. Parabolic Radon transform is demultiple attenuation technique that separate multiple and primary in radon domain. Water-bottom mutiple ussualy appear and easly identified on seismic data, parabolic radon transform applied to improve the seismic section. Application of this method to data showing multiple reflectors weakened and reduced after muting multiple zones in the radon domain. Some of the primary reflector also weakened due to bad separation in radon domain, optimal separation will require a wide distribution of offsets. Keywords: Parabolic radon, multiple, attenuation

3D PETROPHYSICS FOR HAWE: CASE STUDIES

2021 ◽  
Author(s):  
Alexander Kolomytsev ◽  
◽  
Yulia Pronyaeva Pronyaeva ◽  
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Vertical Direction ◽  
Geological Structure ◽  
Current Layer ◽  
Radial Model ◽  
The Difference ◽  
Geological Concept ◽  
Property Changes

Most conventional log interpretation technics use the radial model, which was developed for vertical wells and work well in them. But applying this model to horizontal wells can result in false conclusions. The reasons for this are property changes in vertical direction and different depth of investigation (DOI) of logging tools. DOI area probably can include a response from different layers with different properties. All of this complicates petrophysical modeling. The 3D approach for high angle well evaluation (HAWE) is forward modeling in 3D. For this modeling, it is necessary to identify the geological concept near the horizontal well section using multiscale data. The accuracy of modeling depends on the details of the accepted geological model based on the data of borehole images, logs, geosteering inversion, and seismic data. 3D modeling can be applied to improve the accuracy of reservoir characterization, well placement, and completion. The radial model is often useless for HAWE because LWD tools have different DOI and the invasion zone was not formed. But the difference between volumetric and azimuthal measurements is important for comprehensive interpretation because various formations have different properties in vertical directions. Resistivity tools have the biggest DOI. It is important to understand and be able to determine the reason for changes in log response: a change in the properties of the current layer or approaching the layers with other properties. For this, it is necessary to know the distance to the boundaries of formations with various properties and, therefore, to understand the geological structure of the discovered deposits, and such information on the scale of well logs can be obtained either by modeling or by using extra deep resistivity inversion (mapping). The largest amount of multidisciplinary information is needed for modeling purposes - from images and logs to mapping and seismic data. Case studies include successful examples from Western Siberia clastic formations. In frame of the cases, different tasks have been solved: developed geological concept, updated petrophysical properties for STOIIP and completion, and provided solutions during geosteering. Multiscale modeling, which includes seismic, geosteering mapping data, LWD, and imagers, has been used for all cases.

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