Prestack structural interpretation of seismic data: A case study

1991 ◽  
Author(s):  
J. A. C. Jacobs ◽  
Anne Jardin ◽  
Florence Delprat‐Jannaud ◽  
Roelef Versteeg ◽  
Patrick Lailly
Keyword(s):  
Seismic Data ◽  
Structural Interpretation

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.

Study on Seismic Detection Applied in Mine Advance and Side

2014 ◽  
Vol 580-583 ◽  
pp. 1649-1652
Author(s):  
Yun Bing Hu ◽  
Yao Wang ◽  
Yan Qing Wu ◽  
Zi Xuan Liu
Keyword(s):  
Seismic Data ◽  
Mine Field ◽  
Qinshui Basin ◽  
Radon Transformation ◽  
Hazard Detection ◽  
Seismic Detection

This paper mainly discusses that the tunnel seismic advance detection is applied in mine geo-hazard detection. Separating the field seismic data of mine by radon transformation and migrating the data through advance way and side way, we accomplished advance and side detection simultaneously by one-time shot seismic data. Case study was carried on at Qinshui basin Yangmei No.5 mine field, and detection results mainly coincide with out-crops, demonstrating that our method can be one reference in mine geo-hazard detection.

scholarly journals Appraising structural interpretations using seismic data — Theoretical elements

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. N29-N40
Author(s):  
Modeste Irakarama ◽  
Paul Cupillard ◽  
Guillaume Caumon ◽  
Paul Sava ◽  
Jonathan Edwards
Keyword(s):  
Seismic Data ◽  
Structural Model ◽  
Synthetic Data ◽  
Target Region ◽  
Structural Interpretation ◽  
Vertical Seismic Profiling ◽  
Seismic Image ◽  
Vsp Data ◽  
Phase Shift Data ◽  
Seismic Images

Structural interpretation of seismic images can be highly subjective, especially in complex geologic settings. A single seismic image will often support multiple geologically valid interpretations. However, it is usually difficult to determine which of those interpretations are more likely than others. We have referred to this problem as structural model appraisal. We have developed the use of misfit functions to rank and appraise multiple interpretations of a given seismic image. Given a set of possible interpretations, we compute synthetic data for each structural interpretation, and then we compare these synthetic data against observed seismic data; this allows us to assign a data-misfit value to each structural interpretation. Our aim is to find data-misfit functions that enable a ranking of interpretations. To do so, we formalize the problem of appraising structural interpretations using seismic data and we derive a set of conditions to be satisfied by the data-misfit function for a successful appraisal. We investigate vertical seismic profiling (VSP) and surface seismic configurations. An application of the proposed method to a realistic synthetic model shows promising results for appraising structural interpretations using VSP data, provided that the target region is well-illuminated. However, we find appraising structural interpretations using surface seismic data to be more challenging, mainly due to the difficulty of computing phase-shift data misfits.

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