Curvelet-based Gather Conditioning for Effective Depth Imaging of Legacy Seismic Data -Case Study from Central Poland

2014 ◽  
Author(s):  
M. Cyz ◽  
A. Górszczyk ◽  
M. Malinowski ◽  
P. Krzywiec ◽  
M. Mulińska ◽  
...  
Keyword(s):  
Seismic Data ◽  
Depth Imaging ◽  
Central Poland ◽  
Effective Depth

Time reprocessing and depth imaging of vintage seismic data: the Southern Adriatic Sea case study

2020 ◽  
Author(s):  
Edy Forlin ◽  
Giuseppe Brancatelli ◽  
Nicolò Bertone ◽  
Anna Del Ben ◽  
Riccardo Geletti
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Adriatic Sea ◽  
Imaging Techniques ◽  
Low Cost ◽  
Velocity Model ◽  
Seismic Section ◽  
P Waves ◽  
Depth Imaging

<p>Nowadays depth imaging of seismic data, using different migration schemes (rays tracing or waves equation methods) and different techniques for velocity model building (i.e. grid or layer-based tomography, isotropic or anisotropic velocity field) is a standard approach for the earth’s subsurface characterization. When dealing with low fold vintage data, acquired with outdated technologies, modern processing algorithms may fail. On the other hand, the reprocessing of these old data with modern techniques may lead to an improvement of quality and resolution, allowing a more accurate interpretation of the investigated geological features. It is important to note that a lot of vintage data were acquired in areas with no recent surveys or currently subject to exploration restrictions. Therefore, available vintage data could be of great importance for all the stakeholders involved in geophysical exploration. We present a case study about the reprocessing of low fold marine seismic data that were acquired in 1971 in the Otranto Channel (Southern Adriatic Sea, Italy).</p><p>The first part of the work consists of a modern broadband sequence processing in the time domain, that allowed us to obtain a pre-stack time migrated seismic section; in the second part, depth imaging has been achieved through a pre-stack depth migration (PSDM). Reliable interval p-waves velocity model has been obtained using two tomographic approaches: grid tomography and layer-based tomography; for both, we carried out several iterations of the refinement loop, consisting of migration, ray tomography, residual velocity analysis, velocity model update.</p><p>The results show significant improvements compared to the original vintage section, in terms of resolution and signal to noise ratio. Moreover, depth imaging and velocity modeling added further information (e.g., reliable interval p-waves velocity model, real geometry and thickness of the main geological units). This study confirms that applying the up-to-date processing and imaging techniques to vintage data, their geophysical and geological value is enhanced and renewed at a relatively low cost.</p>

PROCESSING AND IMAGING OF MARINE SEISMIC DATA FROM THE JEQUITINHONHA BASIN (BAHIA, BRAZIL)

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Lourenildo W.B. Leite ◽  
J. Mann ◽  
Wildney W.S. Vieira
Keyword(s):  
Seismic Data ◽  
Sedimentary Basins ◽  
Study Results ◽  
Marine Seismic ◽  
Present Case Study

ABSTRACT. The present case study results from a consistent processing and imaging of marine seismic data from a set collected over sedimentary basins of the East Brazilian Atlantic. Our general aim is... RESUMO. O presente artigo resulta de um processamento e imageamento consistentes de dados sísmicos marinhos de levantamento realizado em bacias sedimentares do Atlântico do Nordeste...

Novel geometric classification of 3D seismic and its application to the Habshan clinoforms of Western Oman

2021 ◽  
Vol 40 (3) ◽  
pp. 186-192
Author(s):  
Thomas Krayenbuehl ◽  
Nadeem Balushi ◽  
Stephane Gesbert
Keyword(s):  
Sequence Stratigraphy ◽  
Seismic Data ◽  
Northwestern Part ◽  
Full Potential ◽  
Seismic Sequence ◽  
3D Seismic ◽  
Level Curve ◽  
Seismic Sequence Stratigraphy

The principles and benefits of seismic sequence stratigraphy have withstood the test of time, but the application of seismic sequence stratigraphy is still carried out mostly manually. Several tool kits have been developed to semiautomatically extract dense stacks of horizons from seismic data, but they stop short of exploiting the full potential of seismo-stratigraphic models. We introduce novel geometric seismic attributes that associate relative geologic age models with seismic geomorphological models. We propose that a relative sea level curve can be derived from the models. The approach is demonstrated on a case study from the Lower Cretaceous Kahmah Group in the northwestern part of Oman where it helps in sweet-spotting and derisking elusive stratigraphic traps.

Seismic solutions utilizing existing in-mine infrastructure for mineral exploration: A case study from Maseve platinum mine, South Africa

2022 ◽  
Vol 41 (1) ◽  
pp. 54-61
Author(s):  
Moyagabo K. Rapetsoa ◽  
Musa S. D. Manzi ◽  
Mpofana Sihoyiya ◽  
Michael Westgate ◽  
Phumlani Kubeka ◽  
...  
Keyword(s):  
South Africa ◽  
Seismic Data ◽  
Mineral Exploration ◽  
Ground Surface ◽  
Seismic Survey ◽  
Noisy Environment ◽  
Borehole Data ◽  
Passive Seismic ◽  
Below Ground

We demonstrate the application of seismic methods using in-mine infrastructure such as exploration tunnels to image platinum deposits and geologic structures using different acquisition configurations. In 2020, seismic experiments were conducted underground at the Maseve platinum mine in the Bushveld Complex of South Africa. These seismic experiments were part of the Advanced Orebody Knowledge project titled “Developing technologies that will be used to obtain information ahead of the mine face.” In these experiments, we recorded active and passive seismic data using surface nodal arrays and an in-mine seismic land streamer. We focus on analyzing only the in-mine active seismic portion of the survey. The tunnel seismic survey consisted of seven 2D profiles in exploration tunnels, located approximately 550 m below ground surface and a few meters above known platinum deposits. A careful data-processing approach was adopted to enhance high-quality reflections and suppress infrastructure-generated noise. Despite challenges presented by the in-mine noisy environment, we successfully imaged the platinum deposits with the aid of borehole data and geologic models. The results open opportunities to adapt surface-based geophysical instruments to address challenging in-mine environments for mineral exploration.

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

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