Semantic segmentation network for 3D seismic fault system detection

We analyzed a 1991 3D seismic data located offshore Florida and applied seismic attribute analysis to identify geological structures. Initially, the seismic data appears to have a high signal-to-noise-ratio, being of an older vintage of quality, and appears to reveal variable amplitude subparallel horizons. Additional geophysical analysis, including seismic attribute analysis, reveals that the data has excessive denoising, and that the continuous features are actually a network of polygonal faults. The polygonal faults were identified in two tiers using variance, curvature, dip magnitude, and dip azimuth seismic attributes. Inline and crossline sections show continuous reflectors with a noisy appearance, where the polygonal faults are suppressed. In the variance time slices, the polygonal fault system forms a complex network that is not clearly imaged in the seismic amplitude data. The patterns of polygonal fault systems in this legacy dataset are compared to more recently acquired 3D seismic data from Australia and New Zealand. It is relevant to emphasize the importance of seismic attribute analysis to improve accuracy of interpretations, and also to not dismiss older seismic data that has low accurate imaging, as the variable amplitude subparallel horizons might have a geologic origin.

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Structure of the footwall of a listric fault system revealed by 3D seismic data from the Niger Delta

Basin Research ◽

10.1111/j.1365-2117.2011.00514.x ◽

2011 ◽

Vol 24 (1) ◽

pp. 107-123 ◽

Cited By ~ 6

Author(s):

Dominic Maloney ◽

Richard Davies ◽

Jonathan Imber ◽

Stephen King

Keyword(s):

Seismic Data ◽

Niger Delta ◽

Fault System ◽

3D Seismic ◽

Listric Fault ◽

3D Seismic Data

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Seismic fault detection with convolutional neural network

Geophysics ◽

10.1190/geo2017-0666.1 ◽

2018 ◽

Vol 83 (5) ◽

pp. O97-O103 ◽

Cited By ~ 48

Author(s):

Wei Xiong ◽

Xu Ji ◽

Yue Ma ◽

Yuxiang Wang ◽

Nasher M. AlBinHassan ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Field Data ◽

Data Sets ◽

3D Seismic ◽

Learning Tools ◽

Seismic Fault ◽

Seismic Image ◽

Coherence Estimation ◽

Seismic Images

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.

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SEISMIC DELINEATION OF STRUCTURE-CONTROLLED CARBONATE CEMENT AND POTENTIAL ECONOMIC IMPLICATIONS, ANGEL FIELD, NORTH WEST SHELF

The APPEA Journal ◽

10.1071/aj94017 ◽

1995 ◽

Vol 35 (1) ◽

pp. 280

Author(s):

S. Ryan-Grigor ◽

J.P. Schulz-Rojahn

Keyword(s):

Seismic Data ◽

High Amplitude ◽

Fault System ◽

3D Seismic ◽

Economic Implications ◽

Northern Margin ◽

North West ◽

The North ◽

Migration Pathways ◽

3D Seismic Data

Major carbonate-cemented zones occur in Late Jurassic Angel Formation sandstones of marine mass flow origin that contain large hydrocarbon reserves in the Angel Field, Dampier Sub-basin. Preliminary results suggest that poikilotopic dolomite cement is dominant. The carbonate-cemented zones are identifiable from wireline log response and 3D seismic data, and occur in discrete intervals with a cumulative thickness of approximately 165m at Angel-2. These intervals produce a zone of high amplitude reflections of about 100 ms two-way time. Field-wide seismic mapping indicates that these carbonate-cemented zones sharply abut the northern margin of a major east-west trending strike-slip fault system that traverses this field. The carbonate-cemented zones extend in a wedge-like shape towards the northeast and concentrate along the crest of the main structural trend.The results underscore the importance of 3D seismic data for a better estimation of reservoir risk and reserves in variably carbonate-cemented sandstones.The carbonate-cemented zones may represent a 'plume' related to migration of petroleum and/or carbon dioxide. Therefore delineation of major carbonate-cemented zones using seismic data may aid in the identification of petroleum migration pathways and pools in the North West Shelf. Alternatively, carbonate cements dissolved south of the major fault zone and possibly in downdip locations in which case dissolution pores may exist in these areas. Further research is required to evaluate these hypotheses.

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Advances in 3D seismic fault interpretation

EAEG/EAPG/EAGO Joint Multidisciplinary Workshop - Developing New Reservoirs in Europe ◽

10.3997/2214-4609.201407004 ◽

1994 ◽

Author(s):

A. Alam ◽

P. Caragounis

Keyword(s):

3D Seismic ◽

Seismic Fault ◽

Fault Interpretation

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3D seismic imaging of the Alpine Fault and the glacial valley at Whataroa, New Zealand

10.5194/egusphere-egu21-9743 ◽

2021 ◽

Author(s):

Vera Lay ◽

Stefan Buske ◽

Franz Kleine ◽

John Townend ◽

Richard Kellett ◽

...

Keyword(s):

New Zealand ◽

Fault Zone ◽

Seismic Data ◽

Seismic Imaging ◽

P Wave ◽

Seismic Survey ◽

Fault System ◽

3D Seismic ◽

Alpine Fault ◽

Drill Site

The Alpine Fault at the West Coast of the South Island (New Zealand) is a major plate boundary that is expected to rupture in the next 50 years, likely as a magnitude 8 earthquake. The Deep Fault Drilling Project (DFDP) aimed to deliver insight into the geological structure of this fault zone and its evolution by drilling and sampling the Alpine Fault at depth. Here we present results from a seismic survey around the DFDP-2 drill site in the Whataroa Valley where the drillhole almost reached the fault plane. This unique 3D seismic survey includes several 2D lines and a 3D array at the surface as well as borehole recordings. Within the borehole, the unique option to compare two measurement systems is used: conventional three-component borehole geophones and a fibre optic cable (heterodyne Distributed Vibration Sensing system (hDVS)). Both systems show coherent signals but only the hDVS system allowed a recording along the complete length of the borehole.Despite the challenging conditions for seismic imaging within a glacial valley filled with sediments and steeply dipping valley flanks, several structures related to the valley itself as well as the tectonic fault system are imaged. The pre-processing of the seismic data also includes wavefield separation for the zero-offset borehole data. Seismic images are obtained by prestack depth migration approaches.Within the glacial valley, particularly steep valley flanks are imaged directly and correlate well with results from the P-wave velocity model obtained by first arrival travel-time tomography. Additionally, a glacially over-deepened trough with nearly horizontally layered sediments is identified about 0.5 km south of the DFDP-2B borehole.With regard to the expected Alpine fault zone, a set of several reflectors dipping 40-56&#176; to the southeast are identified in a ~600 m wide zone between depths of 0.2 and 1.2 km that is interpreted to be the minimum extent of the damage zone. Different approaches image one distinct reflector dipping at 40&#176;, which is interpreted to be the main Alpine Fault reflector. This reflector is only ~100 m ahead from the lower end of the borehole. At shallower depths (z<0.5 km), additional reflectors are identified as fault segments and generally have steeper dips up to 56&#176;. About 1 km south of the drill site, a major fault is identified at a depth of 0.1-0.5 km that might be caused by the regional tectonics interacting with local valley structures. A good correlation is observed among the separate seismic data sets and with geological results such as the borehole stratigraphy and the expected surface trace of the fault.In conclusion, several structural details of the fault zone and its environment are seismically imaged and show the complexity of the Alpine Fault at the Whataroa Valley. Thus, a detailed seismic characterization clarifies the subsurface structures, which is crucial to understand the transpressive fault&#8217;s tectonic processes.

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