Enhanced ant tracking: Using a multispectral seismic attribute workflow to improve 3D fault detection

2021 ◽  
Vol 40 (7) ◽  
pp. 502-512
Author(s):  
Mateo Acuña-Uribe ◽  
María Camila Pico-Forero ◽  
Paul Goyes-Peñafiel ◽  
Darwin Mateus
Keyword(s):  
Random Noise ◽  
Seismic Attributes ◽  
Hydrocarbon Exploration ◽  
Fault Identification ◽  
Complex Task ◽  
Frequency Filtering ◽  
Seismic Attribute ◽  
Interpretation Process ◽  
Seismic Volumes ◽  
Fault Interpretation

Fault interpretation is a complex task that requires time and effort on behalf of the interpreter. Moreover, it plays a key role during subsurface structural characterization either for hydrocarbon exploration and development or well planning and placement. Seismic attributes are tools that help interpreters identify subsurface characteristics that cannot be observed clearly. Unfortunately, indiscriminate and random seismic attribute use affects the fault interpretation process. We have developed a multispectral seismic attribute workflow composed of dip-azimuth extraction, structural filtering, frequency filtering, detection of amplitude discontinuities, enhancement of amplitude discontinuities, and automatic fault extraction. The result is an enhanced ant-tracking volume in which faults are improved compared to common fault-enhanced workflows that incorporate the ant-tracking algorithm. To prove the effectiveness of the enhanced ant-tracking volume, we have applied this methodology in three seismic volumes with different random noise content and seismic characteristics. The detected and extracted faults are continuous, clean, and accurate. The proposed fault identification workflow reduces the effort and time spent in fault interpretation as a result of the integration and appropriate use of various types of seismic attributes, spectral decomposition, and swarm intelligence.

Applying principal component analysis to seismic attributes for interpretation of evaporite facies: Lower Triassic Jialingjiang Formation, Sichuan Basin, China

2017 ◽  
Vol 5 (4) ◽  
pp. T461-T475 ◽  
Author(s):  
Suyun Hu ◽  
Wenzhi Zhao ◽  
Zhaohui Xu ◽  
Hongliu Zeng ◽  
Qilong Fu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Gamma Ray ◽  
Principal Component ◽  
Lower Triassic ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Hydrocarbon Exploration ◽  
Seismic Survey ◽  
Seismic Attribute ◽  
Siliciclastic Rocks

In China and elsewhere, it is important to predict different lithologies and lithofacies for hydrocarbon exploration in a mixed evaporite-carbonate-siliciclastic system. The lower section of the second member of the Jialingjiang Formation (T1j2L) is mainly composed of anhydrite, dolostone, limestone, and siliciclastic rocks, providing a rare opportunity to reconstruct detailed facies in a [Formula: see text] 3D seismic survey with 31 wells. Wireline logs (sonic, density, and gamma ray) calibrated by core analysis are essential in distinguishing anhydrite, siliciclastics, and carbonates. Although different lithologies are characterized by different acoustic impedance (AI), with certain overlapping, it is still difficult to predict lithology by any single seismic attribute because of the limited seismic resolution in a thinly interbedded formation of multiple lithologies. In our study, principal component analysis (PCA) was applied to extract lithologic information from selected seismic attributes; the first two principal components were used to predict the content of anhydrite, siliciclastics, and carbonates. Content maps of anhydrite, siliciclastics, and carbonates — created by mixing the represented color — were used to reconstruct lithofacies of the T1j2L submember. It is quite difficult, even with the PCA approach, to uniquely resolve the three lithologies due to the overlapped AI and the limited resolution of the seismic data. However, the workflow that we evaluated dramatically improved the prediction accuracy of lithology and lithofacies. Facies transition during the deposition of the T1j2L submember in the study area was inferred from a paleo-uplift in the southwest to a restricted lagoon and then to an open marine setting in the northeast.

The Importance of Seismic Attributes in Complex Area, Case Study from Vodianivske Field, Dnieper-Donets Basin, Ukraine

2021 ◽  
Author(s):  
Ivan Khabanets ◽  
Benjamin Medvedev ◽  
Carlo D'Aguanno ◽  
Diego Scapin ◽  
Marco Mantova
Keyword(s):  
High Resolution ◽  
Frequency Tuning ◽  
Seismic Attributes ◽  
Hydrocarbon Exploration ◽  
Donets Basin ◽  
Small Scale ◽  
Lower Permian ◽  
Seismic Attribute ◽  
Block Boundary ◽  
Time Migration

Abstract The Dnieper-Donets Basin (DDB) is the principal producer of hydrocarbons in Ukraine and reserves are found in lower Permian and in Visean-Serpukhovian from Lower Carboniferous. The Vodianivske field is located halfway between Poltava and Kharkiv in east Ukraine with proven reserves at depth of 5-6km. Previous studies based on legacy seismic data show thickness changes of the upper Visean towards the main structure and dim small-scale structures on the block boundary. A recent 3D data reprocessing using 5D interpolation and advanced prestack time migration provides a broad frequency content image and imparts detailed high-resolution geological events. While traditional exploration is focused on gas traps in the Visean and below, current study aims to scan for potential traps in the Serpukhovian and above. In order to reveal thin section features, multiple seismic attributes were tested, and spectral decomposition was found to be a powerful tool that delineated thin sand bodies in river valleys and allowed interpretation of high-resolution small-scale faults and pinch-outs not seen before. Frequency tuning analysis on mapped horizons associated with upper Serpukhovian supported the presence of a large deltaic structure revealing SE-NW thin ∼1km wide sand body and developed set of crossing meanders. Similar approach was applied on legacy data expanding to the east and while seismic quality was limited, it was possible to identify a narrow ∼25km length meander and highlight a fault set. Upon seismic attribute study we were able to identify and map thin units associated with sands that can be considered as future targets in hydrocarbon exploration in the area.

scholarly journals Intelligent Detection of Small Faults Using a Support Vector Machine

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6242
Author(s):  
Aiping Zeng ◽  
Lei Yan ◽  
Yaping Huang ◽  
Enming Ren ◽  
Tao Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Production Efficiency ◽  
Random Noise ◽  
Principal Component ◽  
Seismic Attributes ◽  
Forward Model ◽  
Fault Prediction ◽  
Fault Identification ◽  
Support Vector ◽  
Small Fault

The small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an intelligent small fault identification method combining variable mode decomposition (VMD) and a support vector machine (SVM). A fault forward model is established to analyze the response characteristics of different seismic attributes under the condition of random noise. The results show that VMD can effectively realize the attenuation of random noise and the seismic attributes extracted on this basis have a good correlation with the small fault. Through the analysis of the SVM algorithm and the fault forward model, it is proved that it is feasible to realize intelligent predictions of small faults by using seismic attributes as the input of a SVM. The fault prediction method using a SVM that is proposed in this paper has higher accuracy than the principal component analysis method, as the prediction results have important guiding significance and reference value for later coal mining. Therefore, the method presented in this paper can be used as a new intelligent method for small fault identification in coal fields.

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

Categorizing and correlating diffractivity attributes with seismic-reflection attributes using autoencoder networks

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. O59-O70
Author(s):  
Sergius Dell ◽  
Jan Walda ◽  
Andreas Hoelker ◽  
Dirk Gajewski
Keyword(s):  
Spectral Decomposition ◽  
Seismic Reflection ◽  
Seismic Attributes ◽  
Learning Approach ◽  
Fracture Density ◽  
Seismic Attribute ◽  
Edge Diffraction ◽  
Time Migration ◽  
Fault Interpretation ◽  
Decomposition Class

Seismic attributes play a crucial role in fault interpretation and mapping fracture density. Conventionally, seismic attributes derived from migrated reflections are used for this purpose. The attributes derived from the other counterparts of the recorded wavefield are often ignored and excluded from the categorization. We have performed categorization of the attributes derived from the diffracted part of the wavefield and combine them into a new seismic attribute class, which we call diffractivity attributes. The extraction of diffractivity attributes is based on the 3D Kirchhoff time migration operator that includes a dynamic muting. We distinguish three major classes in the diffractivity attributes, which describe geometric and amplitude properties of the seismic diffractions. We assign point and edge diffraction focusing as well as the azimuth to the geometric class. The amplitudes of the isolated seismic diffractions are used to extract the instantaneous attributes based on the complex-trace approach. The instantaneous amplitudes, phase, frequency, and sweetness build up the instantaneous attribute class. We perform a spectral decomposition of the isolated diffractions into the isofrequencies using the wavelet approach. The isofrequencies compose the spectral-decomposition class. We also link the new diffractivity class to the conventional seismic reflection attributes. We use a deep learning approach based on convolutional neural networks for classifying and correlating the diffractivity attributes.

Quantifying the sensitivity of seismic facies classification to seismic attribute selection: An explainable machine learning study

2022 ◽  
pp. 1-90
Author(s):  
David Lubo-Robles ◽  
Deepak Devegowda ◽  
Vikram Jayaram ◽  
Heather Bedle ◽  
Kurt J. Marfurt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Global Analysis ◽  
Random Noise ◽  
Seismic Attributes ◽  
Attribute Selection ◽  
Seismic Facies ◽  
Attribute Importance ◽  
Seismic Attribute ◽  
Facies Classification

During the past two decades, geoscientists have used machine learning to produce a more quantitative reservoir characterization and to discover hidden patterns in their data. However, as the complexity of these models increase, the sensitivity of their results to the choice of the input data becomes more challenging. Measuring how the model uses the input data to perform either a classification or regression task provides an understanding of the data-to-geology relationships which indicates how confident we are in the prediction. To provide such insight, the ML community has developed Local Interpretable Model-agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP) tools. In this study, we train a random forest architecture using a suite of seismic attributes as input to differentiate between mass transport deposits (MTDs), salt, and conformal siliciclastic sediments in a Gulf of Mexico dataset. We apply SHAP to understand how the model uses the input seismic attributes to identify target seismic facies and examine in what manner variations in the input such as adding band-limited random noise or applying a Kuwahara filter impact the models’ predictions. During our global analysis, we find that the attribute importance is dynamic, and changes based on the quality of the seismic attributes and the seismic facies analyzed. For our data volume and target facies, attributes measuring changes in dip and energy show the largest importance for all cases in our sensitivity analysis. We note that to discriminate between the seismic facies, the ML architecture learns a “set of rules” in multi-attribute space and that overlap between MTDs, salt, and conformal sediments might exist based on the seismic attribute analyzed. Finally, using SHAP at a voxel-scale, we understand why certain areas of interest were misclassified by the algorithm and perform an in-context interpretation to analyze how changes in the geology impact the model’s predictions.

Geometric seismic attribute estimation using data-adaptive windows

2019 ◽  
Vol 7 (2) ◽  
pp. SC33-SC43 ◽  
Author(s):  
Tengfei Lin ◽  
Bo Zhang ◽  
Kurt Marfurt
Keyword(s):  
Random Noise ◽  
Window Size ◽  
Seismic Attributes ◽  
Seismic Survey ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Simple Modification ◽  
Analysis Window ◽  
Data Adaptive ◽  
Using Data

Geometric seismic attributes such as coherence are routinely used for highlighting geologic features such as faults and channels. Traditionally, we use a single user-defined analysis window of fixed size to calculate attributes for the entire seismic volume. In general, smaller windows produce sharper geologic edges, but they are more sensitive to noise. In contrast, larger windows reduce the effect of random noise, but they might laterally smear faults and channel edges and vertically mix the stratigraphy. The vertical and lateral resolutions of a 3D seismic survey change with depth due to attenuation losses and velocity increase, such that a window size that provides optimal images in the shallower section is often too small for the deeper section. A common workaround to address this problem is to compute the seismic attributes using a suite of fixed windows and then splice the results at the risk of reducing the vertical continuity of the final volume. Our proposed solution is to define laterally and vertical smoothly varying analysis windows. The construction of such tapered windows requires a simple modification of the covariance matrix for eigenstructure-based coherence and a less obvious, but also simple, modification of semblance-based coherence. We determine the values of our algorithm by applying it to a vintage 3D seismic survey acquired offshore Louisiana, USA.

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.

scholarly journals Characterization of reservoir sands using 3D seismic attributes in the coastal swamp area of Niger Delta Basin

2021 ◽  
Author(s):  
Oluwatoyin Khadijat Olaleye ◽  
Pius Adekunle Enikanselu ◽  
Michael Ayuk Ayuk
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Niger Delta Basin

AbstractHydrocarbon accumulation and production within the Niger Delta Basin are controlled by varieties of geologic features guided by the depositional environment and tectonic history across the basin. In this study, multiple seismic attribute transforms were applied to three-dimensional (3D) seismic data obtained from “Reigh” Field, Onshore Niger Delta to delineate and characterize geologic features capable of harboring hydrocarbon and identifying hydrocarbon productivity areas within the field. Two (2) sand units were delineated from borehole log data and their corresponding horizons were mapped on seismic data, using appropriate check-shot data of the boreholes. Petrophysical summary of the sand units revealed that the area is characterized by high sand/shale ratio, effective porosity ranged from 16 to 36% and hydrocarbon saturation between 72 and 92%. By extracting attribute maps of coherence, instantaneous frequency, instantaneous amplitude and RMS amplitude, characterization of the sand units in terms of reservoir geomorphological features, facies distribution and hydrocarbon potential was achieved. Seismic attribute results revealed (1) characteristic patterns of varying frequency and amplitude areas, (2) major control of hydrocarbon accumulation being structural, in terms of fault, (3) prospective stratigraphic pinch-out, lenticular thick hydrocarbon sand, mounded sand deposit and barrier bar deposit. Seismic Attributes analysis together with seismic structural interpretation revealed prospective structurally high zones with high sand percentage, moderate thickness and high porosity anomaly at the center of the field. The integration of different seismic attribute transforms and results from the study has improved our understanding of mapped sand units and enhanced the delineation of drillable locations which are not recognized on conventional seismic interpretations.

scholarly journals APPLICATION OF DIRECT HYDROCARBON INDICATORS FOR OIL AND GAS PROSPECTING IN THE DNIPRO-DONETS DEPRESSION

2021 ◽  
pp. 99-108
Author(s):  
Sergiy VYZHVA ◽  
Ihor SOLOVYOV ◽  
Ihor МYKHALEVYCH ◽  
Viktoriia KRUHLYK ◽  
Georgiy LISNY
Keyword(s):  
Acoustic Impedance ◽  
Oil And Gas ◽  
Practical Importance ◽  
Seismic Attributes ◽  
Limited Range ◽  
Seismic Attribute ◽  
Tectonic Model ◽  
Hydrocarbon Traps ◽  
Attribute Analysis ◽  
Volumetric Images

Based on the results of numerous seismic studies carried out in the areas and fields of the Dnipro-Donets depression, the strategy to identify hydrocarbon traps in this region has been developed taking into account modern requirements for prospecting and exploration of gas and oil fields. The studies are designed to determine the favorable zones of hydrocarbon accumulations based on the analysis of the structural-tectonic model. A necessary element for solving such a problem is to aaply direct indicators of hydrocarbons to predict traps of the structural, lithological or combined type. It was determined that an effective approach to identify hydrocarbon traps in the region is attribute analysis employing seismic attributes such as seismic envelope, acoustic impedance or relative acoustic impedance. In most cases of practical importance, the analysis of the distribution of the values of these attributes turned out to be sufficient for performing the geological tasks. It is given an example of extracting additional useful information on the spatial distribution of hydrocarbon traps from volumetric images obtained from seismograms of common sources with a limited range of ray angles inclinations. To analyze the distributions of seismic attribute values, it is recommended to use the Geobody technology for detecting geological bodies as the most effective when using volumetric seismic data. The distributions of various properties of rocks, including zones of increased porosity or zones of presence of hydrocarbons are determined depending on the types of seismic attributes used in the analysis,. The use of several seismic attributes makes it possible to identify geological bodies saturated with hydrocarbons with increased porosity and the like. The paper provides examples of hydrocarbon traps recognition in the areas and fields of the Dnipro-Donets depression practically proved by wells. A generalization on the distribution of promising hydrocarbon areas on the Northern flank of the Dnipro-Donets depression and the relationship of this distribution with the identified structural elements of the geological subsoil is made. 

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