scholarly journals Improved definition of faults in the Groningen field using seismic attributes

2017 ◽  
Vol 96 (5) ◽  
pp. s71-s85 ◽  
Author(s):  
Marloes Kortekaas ◽  
Bastiaan Jaarsma
Keyword(s):  
Continuous Production ◽  
Frictional Resistance ◽  
Seismic Attributes ◽  
Fault Model ◽  
3D Seismic ◽  
Reservoir Level ◽  
Gas Field ◽  
Fault Surface ◽  
Definition Of ◽  
Gas Bearing

AbstractThe Groningen field is the largest onshore gas field in Europe. The gas-bearing section comprises aeolian and fluvial Rotliegend sandstones of Permian age and fluvial sandstones of Carboniferous age. Continuous production since 1963 has led to induced seismicity starting in the early 1990s.Faults at reservoir level play a major role in the seismicity in the Groningen field. Fault slip is expected when shear traction is sufficient to overcome frictional resistance on the fault surface. Clear insights into which faults and fault segments are most susceptible to seismicity could be used to optimise production and minimise the seismic risk. To gain these insights, a detailed and realistic fault model is required as input to both statistical analyses on seismicity and deterministic geomechanical modelling of seismogenic behaviour along fault planes. Geometrical seismic attributes and, subsequently, fault planes were extracted from a reprocessed and depth-imaged 3D seismic volume. This resulted in a detailed visualisation of the faults at reservoir level, with extension into the deeper strata below the reservoir in many cases. They represent fault planes with realistic dimensions and shapes. The fault map based on seismic attributes suggests the presence of faults that have not been included in studies on Groningen seismicity before. The improved fault definition correlates with recent earthquake hypocentres. We conclude that a detailed fault model of the Groningen field can be created using 3D seismic attributes and that detailed 3D fault planes can be extracted from these attributes. The results can be used as input to statistical and geomechanical analyses on seismicity.

An assessment of 3D seismic attributes applied in a gas field in Mexico

2004 ◽  
Author(s):  
Raúl del Valle‐García ◽  
Fidel Reyes‐Ramos ◽  
Alfredo Trujillo‐Alcántara
Keyword(s):  
Seismic Attributes ◽  
3D Seismic ◽  
Gas Field

3D seismic attributes for a tight gas sand reservoir characterization of the eastern Sulige gas field, Ordos Basin, China

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. B35-B43 ◽  
Author(s):  
Zhiguo Wang ◽  
Jinghuai Gao ◽  
Daxing Wang ◽  
Qiansheng Wei
Keyword(s):  
Ordos Basin ◽  
Seismic Attributes ◽  
Gas Production ◽  
Seismic Attenuation ◽  
Tight Gas ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Gas Field ◽  
Attribute Analysis ◽  
Phase Tuning

The Lower Permian Xiashihezi Formation of the Ordos Basin is the largest producer of tight gas sand in China. The controls on tight gas production are many and include a variety of geologic, hydrodynamic, and engineering factors from one well to another throughout the basin. In this study, we considered data from a [Formula: see text] 3D seismic volume and logs from 17 wells to investigate the geologic controls on gas production in the [Formula: see text] member of the Xiashihezi Formation, eastern Sulige gas field, Ordos Basin. Our objective was to determine the potential of applying multiple seismic attributes to identify the higher productivity areas of a tight gas sand reservoir. To achieve this, we used amplitude, complex traces, spectral decomposition, and seismic attenuation attributes derived from the 3D seismic volume to detect gas-bearing sand areas. The results of seismic attribute analysis revealed that no single attribute is correlated to higher productivity areas. The qualitative correlations between attributes and production records reflected that higher productivity areas are associated with seismically definable higher amplitude, more stable phase, tuning frequency, and stronger attenuation features in the study area. Meanwhile, three outlier wells in the seismic attribute analysis provided a reminder of the uncertainty in geologic interpretation. The gas-sand reservoir evaluation results suggested that the Pareto principle helps to enhance the interpretation needed to determine the productivity distribution of [Formula: see text] tight-gas reservoir in the study area.

scholarly journals An application of seismic attributes analysis for mapping of gas bearing sand zones in the sawan gas field, Pakistan

2015 ◽  
Vol 51 (4) ◽  
pp. 723-744 ◽  
Author(s):  
Tahir Azeem ◽  
Wang Yanchun ◽  
Perveiz Khalid ◽  
Liu Xueqing ◽  
Fang Yuan ◽  
...  
Keyword(s):  
Seismic Attributes ◽  
Gas Field ◽  
Gas Bearing

scholarly journals Geomorphological Geometries and High-Resolution Seismic Sequence Stratigraphy of Malay Basin’s Fluvial Succession

2021 ◽  
Vol 11 (11) ◽  
pp. 5156
Author(s):  
Abd Al-Salam Al-Masgari ◽  
Mohamed Elsaadany ◽  
Numair A. Siddiqui ◽  
Abdul Halim Abdul Latiff ◽  
Azli Abu Bakar ◽  
...  
Keyword(s):  
High Resolution ◽  
Sequence Stratigraphy ◽  
Three Dimensional ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Seismic Sequence ◽  
3D Seismic ◽  
Seismic Sequences ◽  
Seismic Sequence Stratigraphy ◽  
Point Bars

This study identified the Pleistocene depositional succession of the group (A) (marine, estuarine, and fluvial depositional systems) of the Melor and Inas fields in the central Malay Basin from the seafloor to approximately −507 ms (522 m). During the last few years, hydrocarbon exploration in Malay Basin has moved to focus on stratigraphic traps, specifically those that existed with channel sands. These traps motivate carrying out this research to image and locate these kinds of traps. It can be difficult to determine if closely spaced-out channels and channel belts exist within several seismic sequences in map-view with proper seismic sequence geomorphic elements and stratigraphic surfaces seismic cross lines, or probably reinforce the auto-cyclic aggregational stacking of the avulsing rivers precisely. This analysis overcomes this challenge by combining well-log with three-dimensional (3D) seismic data to resolve the deposition stratigraphic discontinuities’ considerable resolution. Three-dimensional (3D) seismic volume and high-resolution two-dimensional (2D) seismic sections with several wells were utilized. A high-resolution seismic sequence stratigraphy framework of three main seismic sequences (3rd order), four Parasequences sets (4th order), and seven Parasequences (5th order) have been established. The time slice images at consecutive two-way times display single meandering channels ranging in width from 170 to 900 m. Moreover, other geomorphological elements have been perfectly imaged, elements such as interfluves, incised valleys, chute cutoff, point bars, and extinction surfaces, providing proof of rapid growth and transformation of deposits. The high-resolution 2D sections with Cosine of Phase seismic attributes have facilitated identifying the reflection terminations against the stratigraphic amplitude. Several continuous and discontinuous channels, fluvial point bars, and marine sediments through the sequence stratigraphic framework have been addressed. The whole series reveals that almost all fluvial systems lay in the valleys at each depositional sequence’s bottom bars. The degradational stacking patterns are characterized by the fluvial channels with no evidence of fluvial aggradation. Moreover, the aggradation stage is restricted to marine sedimentation incursions. The 3D description of these deposits permits distinguishing seismic facies of the abandoned mud channel and the sand point bar deposits. The continuous meandering channel, which is filled by muddy deposits, may function as horizontal muddy barriers or baffles that might isolate the reservoir body into separate storage containers. The 3rd, 4th, and 5th orders of the seismic sequences were established for the studied succession. The essential geomorphological elements have been imaged utilizing several seismic attributes.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

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