Advanced Seismic Data Interpretation for Carbonate Targets Based on Optimised Processing Techniques

1996 ◽  
Author(s):  
Klaus Fischer ◽  
Ulrich Moller ◽  
Roland Marschall
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
Processing Techniques

scholarly journals Advanced Seismic Data Interpretation for Carbonate Targets Based on Optimized Processing Techniques

GeoArabia ◽  
1996 ◽  
Vol 1 (2) ◽  
pp. 285-296
Author(s):  
Klaus C. Fischer ◽  
Ulrich Möller ◽  
Roland Marschall
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Data Interpretation ◽  
Second Phase ◽  
Shelf Area ◽  
Depth Migration ◽  
Amplitude Versus Offset ◽  
And Migration ◽  
Processing Techniques ◽  
Seismic Anomalies

ABSTRACT Seismic data from the shelf area of the Cretaceous Shu’aiba Formation in Abu Dhabi is used to investigate stratigraphic and structural seismic anomalies. The data consists of a 2-D grid of seismic lines, acquired in the late 1980s and 1993. The data was reprocessed in several phases. The first phase consists of standard time domain processing upto final Dip Move Out stack and migration. In the second phase, a macro-velocity model for post-stack depth migration is generated and tested by the interpreters. The third phase is the interpretation of the pre-stack depth migration stack. Due to the structural irregularity of the Shu’aiba Formation, the pre-stack depth migrated data is considered the most reliable for Amplitude Versus Offset analysis. Further steps are L-1 deconvolution followed by Born Inversion. These last steps are required before the lithology can be modeled with high-resolution. The final lithological model is verified by applying forward modeling. The lithological model forms the basis for reservoir and geostatistical evaluations which account for heterogeneities.

Fault Detection by Using Instance Segmentation

2021 ◽  
Author(s):  
Donglin Zhu ◽  
Lei Li ◽  
Rui Guo ◽  
Shifan Zhan
Keyword(s):  
Fault Detection ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Data Interpretation ◽  
Machine Learning Methods ◽  
Segmentation Algorithms ◽  
Direct Recognition ◽  
First Time ◽  
Seismic Images ◽  
Instance Segmentation

Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.

scholarly journals Seismic Data Interpretation and Identification of Hydrocarbon-Bearing Zones of Rajian Area, Pakistan

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 891
Author(s):  
Naveed Ahmad ◽  
Sikandar Khan ◽  
Eisha Fatima Noor ◽  
Zhihui Zou ◽  
Abdullatif Al-Shuhail
Keyword(s):  
Seismic Data ◽  
Foreland Basin ◽  
Data Interpretation ◽  
Indus Basin ◽  
Subsurface Structure ◽  
Vertical Wells ◽  
Salt Range ◽  
Seismic Sections ◽  
The Given ◽  
Triangular Zone

The present study interprets the subsurface structure of the Rajian area using seismic sections and the identification of hydrocarbon-bearing zones using petrophysical analysis. The Rajian area lies within the Upper Indus Basin in the southeast (SE) of the Salt Range Potwar Foreland Basin. The marked horizons are identified using formation tops from two vertical wells. Seismic interpretation of the given 2D seismic data reveals that the study area has undergone severe distortion illustrated by thrusts and back thrusts, forming a triangular zone within the subsurface. The final trend of those structures is northwest–southeast (NW–SE), indicating that the area is part of the compressional regime. The zones interpreted by the study of hydrocarbon potential include Sakessar limestone and Khewra sandstone. Due to the unavailability of a petrophysics log within the desired investigation depths, lithology cross-plots were used for the identification of two potential hydrocarbon-bearing zones in one well at depths of 3740–3835 m (zone 1) and 4015–4100 m (zone 2). The results show that zone 2 is almost devoid of hydrocarbons, while zone 1 has an average hydrocarbon saturation of about 11%.

Improving 4D Seismic Data Interpretation using Geostatistical Filtering

2005 ◽  
Author(s):  
Carlos Eduardo Abreu ◽  
Nathalie Lucet and Philippe Nivlet ◽  
Jean-Jacques Royer
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
4D Seismic
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