scholarly journals SEISMIC ATTRIBUTES AIDED DETECTION OF NW-SE TRENDING FAULTS DEVELOPED ON AN ISOLATED CARBONATE PLATFORM IN THE NW SIRTE BASIN, NORTH CENTRAL LIBYA

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Muneer Abdalla
Keyword(s):  
Seismic Data ◽  
Carbonate Platform ◽  
Median Filter ◽  
Random Noise ◽  
Seismic Attributes ◽  
Structural Features ◽  
North Central ◽  
Field Development ◽  
Sirte Basin ◽  
Upper Paleocene

The lower and upper Paleocene reservoir formations, the primary producing formations in the northwest Sirte Basin, north-central Libya have complex structures which have an impact on the performance of the reservoirs. It is extremely crucial to understand the complex relationships between the fault networks and stratigraphy of the area for future field development. However, delineating faults particularly subtle faults is not an easy process due to the low signal-to-noise ratio in the post stack seismic data despite the effort and careful process of the pre-stack data. Seismic attributes are critical tools in detecting and enhancing major and minor fault interpretation beyond the seismic resolution of the conventional seismic dataset. This study utilizes variance, root mean square, and curvature attributes computed from the post-stack 3D seismic data acquired in the northwest Sirte Basin to detect major and minor faults along an isolated carbonate platform. A spectral whitening and median filter were applied to improve the quality of the data and remove random noise resulted from data acquisition and processing steps. Those methods were utilized to provide high-resolution seismic data and better show edges and structural features. Numerous faults have been detected in the study area. Most major faults in the lower and upper Paleocene reservoir formations are located along the margins of the isolated carbonate platform and have a NW-SE trend. Data conditioning and seismic attribute analyses applied on the 3-D seismic dataset effectively enhanced our understanding of the reservoir complexity and improve the detection of the major and minor faults and fracture zones in the study area.

Delineation of Carbonate Buildups Using Advanced Seismic Attributes, NW Sirte Basin, Libya

2021 ◽  
pp. 159-168
Author(s):  
Muneer Abdalla
Keyword(s):  
Seismic Data ◽  
Carbonate Platform ◽  
Median Filter ◽  
Random Noise ◽  
Seismic Attributes ◽  
Small Scale ◽  
Seismic Attribute ◽  
Attribute Analysis ◽  
Data Conditioning ◽  
Sirte Basin

The Paleocene reservoir formations of the Northwest Sirte Basin in North-central, Libya contains chaotic and mound-shaped seismic geometries that may have an impact on the performance of the reservoirs. It is crucial to characterize and interpret these complex geometries for future field development. Therefore, this study was utilized numerous seismic attributes to characterize and enhance the interpretation of the chaotic and mounded geometries. Data conditioning represented by spectral whitening and median filter was first applied to enhance the quality of the seismic data and remove random noise resulted from data acquisition and processing. It provided high-resolution seismic data and better-displayed edges and sedimentological features. Variance, root mean square (RMS), curvature, and envelope attributes were computed from the post-stack 3D seismic data to better visualize and interpret the chaotic and mound-like seismic geometries. Based on the seismic attribute analysis, the chaotic facies were interpreted as barrier reefs forming the margins of an isolated carbonate platform, whereas the small-scale mound-shaped facies was interpreted as patch reefs developed on the platform interior. Data conditioning methods and seismic attribute analysis that were applied to the 3-D seismic data have effectively improved the detection and interpretation of the chaotic and mounded facies in the study area. Keywords: Carbonate buildup, data conditioning, seismic attributes, Sirte Basin, Libya

scholarly journals APPLYING FILTERS AND SEISMIC ATTRIBUTES FOR ENHANCING FAULTS IN THE 3D SEISMIC SURVEY OF ALTO DE SIRIRIZINHO (SERGIPE-ALAGOAS BASIN, NORTHEAST BRAZIL)

2013 ◽  
Vol 31 (1) ◽  
pp. 109
Author(s):  
Arthur Victor Medeiros Francelino ◽  
Alex Francisco Antunes
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Random Noise ◽  
Seismic Attributes ◽  
Oil Fields ◽  
Seismic Survey ◽  
Northeast Brazil ◽  
3D Seismic ◽  
3D Seismic Data

The 3D seismic data allow that mature oil fields be reevaluated in order to improve the characterization of faults that affect the flow of hydrocarbons. The use of seismic attributes and filtering allows an improvement in the identification and enhancement of these fractures on seismic data. In this study, we used two different filters: the dip-steered median filter to remove random noise and increase the lateral continuity of reflections, and the fault-enhancement filter used to enhance the discontinuities of the reflections. After filtering, similarity and curvature attributes were applied in order to identify the distribution of fractures along the data. Theuse of these attributes and filters contributed greatly to the identification and enhancement of the continuity of the fractures. RESUMO: Com o advento da sísmica 3D, campos de petróleo maduros podem ser reavaliados melhorando a caracterização das falhas que influenciam o fluxo de hidrocarbonetos. A utilização de filtragens e atributos sísmicos possibilita uma melhora na identificação e no realce dessas fraturas no dado sísmico. No presente trabalho foram utilizados dois tipos de filtros, sendo o dip-steered median filter, com a finalidade de retirar os ruídos aleatórios e aumentar a continuidade lateral das reflexões, e o fault-enhancement filter para realçar as descontinuidades das reflexões. Após a etapa de filtragem foram aplicados os atributos de similaridade e curvatura, para se identificar a distribuição das falhas. O uso dos atributos e filtragens colaborou fortemente para a identificação e o realce da continuidade das fraturas. Palavras-chave: reservatório fraturado; interpretação sísmica e atributos sísmicos

A 1D time-varying median filter for seismic random, spike-like noise elimination

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. V17-V24 ◽  
Author(s):  
Yang Liu ◽  
Cai Liu ◽  
Dian Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Random Noise ◽  
Threshold Value ◽  
Band Pass Filter ◽  
Time Varying ◽  
Noise Elimination ◽  
Pass Filter ◽  
Median Filters

Random noise in seismic data affects the signal-to-noise ratio, obscures details, and complicates identification of useful information. We have developed a new method for reducing random, spike-like noise in seismic data. The method is based on a 1D stationary median filter (MF) — the 1D time-varying median filter (TVMF). We design a threshold value that controls the filter window according to characteristics of signal and random, spike-like noise. In view of the relationship between seismic data and the threshold value, we chose median filters with different time-varying filter windows to eliminate random, spike-like noise. When comparing our method with other common methods, e.g., the band-pass filter and stationary MF, we found that the TVMF strikes a balance between eliminating random noise and protecting useful information. We tested the feasibility of our method in reducing seismic random, spike-like noise, on a synthetic dataset. Results of applying the method to seismic land data from Texas demonstrated that the TVMF method is effective in practice.

scholarly journals Application of Unconventional Seismic Attributes and Unsupervised Machine Learning for the Identification of Fault and Fracture Network

2020 ◽  
Vol 10 (11) ◽  
pp. 3864 ◽  
Author(s):  
Umar Ashraf ◽  
Hucai Zhang ◽  
Aqsa Anees ◽  
Hassan Nasir Mangi ◽  
Muhammad Ali ◽  
...  
Keyword(s):  
Neural Networks ◽  
Median Filter ◽  
Fracture Network ◽  
Structural Features ◽  
Small Scale ◽  
Fracture Density ◽  
Fracture Characterization ◽  
Field Development ◽  
Fracture Length ◽  
Dip Angle

The identification of small scale faults (SSFs) and fractures provides an improved understanding of geologic structural features and can be exploited for future drilling prospects. Conventional SSF and fracture characterization are challenging and time-consuming. Thus, the current study was conducted with the following aims: (a) to provide an effective way of utilizing the seismic data in the absence of image logs and cores for characterizing SSFs and fractures; (b) to present an unconventional way of data conditioning using geostatistical and structural filtering; (c) to provide an advanced workflow through multi-attributes, neural networks, and ant-colony optimization (ACO) for the recognition of fracture networks; and (d) to identify the fault and fracture orientation parameters within the study area. Initially, a steering cube was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter (DSDF), and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Multiple structural attributes were applied and shortlisted, including dip and curvature attributes, filtered and unfiltered similarity attributes, thinned fault likelihood (TFL), fracture density, and fracture proximity. These shortlisted attributes were computed through unsupervised vector quantization (UVQ) neural networks. The results of the UVQ revealed the orientations, locations, and extensions of fractures in the study area. The ACO proved helpful in identifying the fracture parameters such as fracture length, dip angle, azimuth, and surface area. The adopted workflow also revealed a small scale fault which had an NNW–SSE orientation with minor heave and throw. The implemented workflow of structural interpretation is helpful for the field development of the study area and can be applied worldwide in carbonate, sand, coal, and shale gas fields.

Multiattribute Seismic Analysis With Fractal Dimension and 2D and 3D Continuous Wavelet Transform

2014 ◽  
Vol 17 (04) ◽  
pp. 436-443 ◽  
Author(s):  
Puneet Saraswat ◽  
Vijay Raj ◽  
Mrinal K. Sen ◽  
Arun Narayanan
Keyword(s):  
Fractal Dimension ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Seismic Analysis ◽  
Seismic Attributes ◽  
Structural Features ◽  
Primary Concern ◽  
Continuous Wavelet ◽  
Salt Domes

Summary The 3D post-stack seismic attributes provide an intuitive and effective way of using seismic volumes for reservoir characterization and development, and further identification of exploration targets. Some of the seismic attributes can aid in the precise prediction of the geometry and heterogeneity of subsurface geological settings. These also can provide useful information on petrophysical and lithological properties when combined with well-log information. There exist numerous seismic attributes that provide a unique interpretation on some aspects of subsurface geology. Of these, the proper demarcation of structural features— such as location and edges of faults and salt domes, and their throw and extent—always has been of primary concern. In this paper, we propose new multiattribute seismic algorithms by using fractal dimension and 2D/3D continuous wavelet transform (CWT). The use of higher-dimensional wavelets incorporates information from the ensemble of traces and can correlate information between neighboring traces in seismic data. The spectral decomposition that is based on the CWT aids in resolving various features of geological interest at a particular scale or frequency, which, when rendered with fractal attribute, demarcates the boundaries between those. We apply these two algorithms separately to a seismic amplitude volume and co-render output volumes together with some weights to yield a final attribute volume incorporating information from the aforementioned algorithms. We demonstrate the efficacy of these two algorithms in terms of the resolution and proper demarcation of various geological structures on real seismic data. The application of these algorithms results in better illumination and proper demarcation of various geological features such as salt domes, channels, and faults, and it illustrates how these simple tools can help to extract detailed information from seismic data.

Fracture detection using multi seismic attributes ant-tracking in the Rag-e-Sefid oilfield, SW Iran

2021 ◽  
Author(s):  
Zahra Tajmir Riahi ◽  
Khalil Sarkarinejad ◽  
Ali Faghih ◽  
Bahman Soleimany ◽  
Gholam Reza Payrovian
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Structural Features ◽  
Hydrocarbon Exploration ◽  
Small Scale ◽  
Northwestern Part ◽  
3D Seismic ◽  
Petroleum Reservoir ◽  
Well Data

<p><strong>Abstract</strong></p><p>The detailed characterization of faults and fractures can give valuable information about the fluid flow through petroleum reservoir and directly affect the hydrocarbon exploration and production programs. In this study, large- and small-scale fractures in the Asmari horizon of the Rag-e-Sefid oilfield were characterized using seismic attribute and well data analyses. Different spatial filters including finite median hybrid (SO-FMH), dip-steered median, dip-steered diffusion, and fault enhancement filters were used on 3D seismic data to reduce noise, enhance the seismic data quality, and create a 3D seismic steering cube. In the next step, seismic attributes such as coherency, similarity, variance, spectral decomposition, dip, and curvature were applied to identify structural features. In order to check the validity of these structural features, results from seismic attributes calibrated by the interpreted fractures from image logs in the Rag-e-Safid oilfield. Then, the ant-tracking algorithm applied on the selected seismic attributes to highlight faults and fractures. These attributes combined using neural network method to create multi-seismic attributes, view different fault- or fold-sensitive seismic attributes in a single image, and facilitate the large-scale fractures extraction process. Finally, automatic fault and fracture extraction technique used to reduce human intervention, improve accuracy and efficiency for the large-scale fracture interpretation and extraction from edge volumes in the Asmari horizon of the Rag-e-Sefid oilfield. In addition to, small- scale fractures were characterized by the obtained information from the image logs interpretation for sixteen wells. All the detected fractures from seismic and well data have been divided into eight fracture sets based on their orientation and using the statistical analysis. The obtained results show that fractures characteristics and their origin are different in the northwestern and southeastern parts of the Rag-e-Sefid oilfield. The NW Rag-e-Sefid and Nourooz Hendijan Izeh Faults reactivation during Zagros orogeny led to create the dextral shear zone and P, R, R′, T, Y- fracture sets in the northwestern part of the Rag-e-Safid oilfield. Also, activity of the SE-Rag-e-Sefid thrust fault during Zagros orogeny caused to form fault-related fractures sets in the southeastern part of the Rag-e-Sefid field. In addition to, axial, cross axial, oblique fracture sets in the Asmari horizon of the Rag-e-Sefid oilfield were created by folding phase during Zagros orogeny. The obtained results were used to fracture modeling in the Asmari horizon of the Rag-e-Sefid oilfield.</p>

A 2D multistage median filter to reduce random seismic noise

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. V105-V110 ◽  
Author(s):  
Cai Liu ◽  
Yang Liu ◽  
Baojun Yang ◽  
Dian Wang ◽  
Jianguo Sun
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Median Filter ◽  
Random Noise ◽  
Synthetic Data ◽  
Window Size ◽  
Median Filters ◽  
A Value ◽  
Final Data ◽  
Static Corrections

Random noise lowers the S/N of seismic data and decreases the accuracy of dynamic and static corrections, thus degrading final data quality. A 2D multistage median filter (MLM) that effectively reduces the high-frequency random noise can be implemented by applying 1D median filters (MF) in several directions and choosing a value derived from them to output at the center of the 2D window. The choice of window size depends on the intensity of the random noise and the percentage of the input data samples within the window that contain noise. Synthetic data can be used to demonstrate how to choose the window size. The tendency of the method to damage the signal while reducing the noise can be minimized by optimizing window size and by applying two passes with modest-sized windows as opposed to a single pass with a larger window. Results of using the method on prestack and poststack data from the Songliao basin in China demonstrate that the method is effective at both stages.

scholarly journals Pitfalls and limitations in seismic attribute interpretation of tectonic features

2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Seismic Attribute ◽  
3D Data ◽  
Amplitude Data ◽  
Seismic Sections ◽  
Push Down ◽  
Data Coherence ◽  
Level Of Experience ◽  
Tectonic Features

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.

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