• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Geophysics and Geophysical Exploration
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• v.16 no.3
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• pp.163-170
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• 2013

The amplitude variation with offset of seismic data can detect fluids in the sediment and resolve the petrophysical properties of hydrocarbons in the subsurface. We analyzed and described the amplitude variation in deep sea seismic data obtained from the Ulleung Basin, East Sea. By inspecting seismic CDP-offset and CDP-angle gathers which show a bright reflection event, we decided a target zone for amplitude variation analysis. From the seismic angle gather at the middle of Ulleung Basin, we recognized amplitude increase or decrease versus offset on the intercept-gradient curve. Using the product attribute and Poisson's ratio change attribute computed in terms of intercept with gradient, the top and the base of gas saturated sediments were described. The area of amplitude variation suggestive of the presence of gas saturated sediments is shown at the depth of 3 s traveltime. Anomalous features of seismic amplitude in the Ulleung Basin were classified by the crossplot of intercept and gradient. The background trend of crossplot between intercept and gradient shows an inverse proportional relation that is common for wet sediments. Anomalous amplitudes of Class III fall into the first and the third quadrants on crossplots. We inferred regional gas/water saturated area with the horizontal dimension of 150 m in the Ulleung Basin by cross-section with respect to cross-plot anomaly.

• Geophysics and Geophysical Exploration
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• v.5 no.2
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• pp.71-77
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• 2002

We developed a modeling and visualization software that can analyze 3-dimensional seismic data. The software divides 3 dimensional space into a series of vertical and horizontal polygons, and allows the various seismic attributes and other spatial information to be stored on these polygons. The program can pick a particular pattern in semi-automatic mode, and store the pattern in the spatial DB. The pattern can be modeled and visualized in 3 dimensional space.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.665-673
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• 2011

In order to estimate the hydrocarbon reserves, the porosity of the reservoir must be determined. The porosity of the area without a well is generally calculated by extrapolating the porosity logs measured at wells. However, if not only well logs but also seismic data exist on the same site, the more accurate pseudo porosity log can be obtained through artificial neural network technique by extracting the relations between the seismic data and well logs at the site. In this study, we have developed a module which creates pseudo porosity logs by using the polynomial neural network method. In order to obtain more accurate pseudo porosity logs, we selected the seismic attributes which have high correlation values in the correlation analysis between the seismic attributes and the porosity logs. Through the training procedure between selected seismic attributes and well logs, our module produces the correlation weights which can be used to generate the pseudo porosity log in the well free area. To verify the reliability and the applicability of the developed module, we have applied the module to the field data acquired from F3 Block in the North Sea and compared the results to those from the probabilistic neural network method in a commercial program. We could confirm the reliability of our module because both results showed similar trend. Moreover, since the pseudo porosity logs from polynomial neural network method are closer to the true porosity logs at the wells than those from probabilistic method, we concluded that the polynomial neural network method is effective for the data sets with insufficient wells such as F3 Block in the North Sea.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.97-114
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• 2020

Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.

• Geophysics and Geophysical Exploration
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• v.17 no.4
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• pp.216-230
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• 2014

Based on the interpretation of 3D seismic profiles acquired in the northwestern area of the Ulleung Basin, East Sea, the shallow sediments consist of five seismic units separated by regional reflectors. An anticline is present in the study area that documents activity of many faults. Bottom simulating reflectors are characterized by high RMS amplitude. Acoustic blanking with low RMS amplitude is distinctively recognized in the gas hydrate stability zone. Seismic attribute analysis shows that if gas hydrates are underlain by free gas, the high reflection strength and the low instantaneous frequency are displayed below the boundary between them. Whereas, if not, the reflection strength is low and instantaneous frequency is high continuously below the gas hydrate zone. Based on the spectral decomposition of the bottom simulating reflector, the high envelope at the specific high frequency range indicates the generation of the tuning effect due to the lower free gas content. Four models for the occurrence of the gas hydrate are suggested considering the slope of sedimentary layers as well as the presence of gas hydrate or free gas.

• Geophysics and Geophysical Exploration
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• v.4 no.3
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• pp.80-83
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• 2001

Four seismic reflection horizons in 3-D seismic data, coherence derived from the seismic data, and 38 well logs from the Boonsville Gas Filed in Texas were tried to be integrated and visualized in 3 dimensions. Time surface was constructed from pick times of the reflection horizons. Average velocities to each horizon at 38 well locations were calculated based on depth markers from the well logs and time picks from the 3-D seismic data. The time surface was transformed to depth surface through velocity interpolation. Coherence was calculated on the 3-D seismic data by semblance method. Spatial distribution of the coherence is captured easily in 3-D visualization. Comparing to a time-slice of seismic data, distinctive stratigraphic features could be correctly recognized on the 3-D visualization.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.

• Economic and Environmental Geology
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• v.47 no.6
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• pp.673-681
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• 2014

Recently, many countries in the world try to develop alternative energy sources, however, traditional hydrocarbon resources are still occupying most of the energy resources. Exploration demands for high technologies are increasing in the development of limited oil & gas resources field owing to the exhaustion of hydrocarbon resources for access area. Therefore, an effort for the development and the application of new technologies such as azimuth seismic survey, ocean-bottom seismic survey and marine controlled-source electromagnetic survey is necessary as well as an understanding of the existing technologies such as 2D/3D seismic survey. This dissertation is designed with the purpose of introducing marine hydrocarbon exploration technologies and analyzing their internalexternal researches, development and science information. In this study, we analised total 616 dissertations for the marine petroleum exploration released in the Sci-expanded DB of 'web of science' during the 2001~2014 periods.

• Economic and Environmental Geology
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• v.46 no.6
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• pp.485-494
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• 2013

$CO_2$ Monitoring, Mitigation and Verification (MMV) is the essential part in the Carbon Capture and Storage (CCS) project in order to assure the storage permanence economically and environmentally. In large-scale CCS projects in the world, the seismic time-lapse survey is a key technology for monitoring the behavior of injected $CO_2$. In this study, we developed a basic process procedure for 3-D seismic baseline data from the Aquistore project, Estevan, Canada. Major target formations of Aquistore CCS project are the Winnipeg and the Deadwood sandstone formations located between 1,800 and 1,900 ms in traveltime. The analysis of trace energy and similarity attributes of seismic data followed by spectral decomposition are carried out for the characterization of $CO_2$ injection zone. High trace energies are concentrated in the northern part of the survey area at 1,800 ms and in the southern part at 1,850 ms in traveltime. The sandstone dominant regions are well recognized with high reflectivity by the trace energy analysis. Similarity attributes show two structural discontinuities trending the NW-SE direction at the target depth. Spectral decomposition of 5, 20 and 40 Hz frequency contents discriminated the successive E-W depositional events at the center of the research area. Additional noise rejection and stratigraphic interpretation on the baseline data followed by applying appropriate imaging technique will be helpful to investigate the differences between baseline data and multi-vintage monitor data.