A knowledge-integration framework for interpreting seismic facies

2014 ◽  
Vol 2 (1) ◽  
pp. SA1-SA9 ◽  
Author(s):  
Iván D. Marroquín
Keyword(s):  
Spatial Distribution ◽  
Knowledge Integration ◽  
Optimal Number ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Integration Framework ◽  
Facies Classification ◽  
Combining Data ◽  
Seismic Anomalies ◽  
Selection Of

In recent years, the size of seismic data volumes and the number of seismic attributes available have increased. As a result, the task of recognizing seismic anomalies for the prediction of stratigraphic features or reservoir properties can be overwhelming. One way to evaluate a large amount of data and understand potential geologic trends is to automate seismic facies classification. However, the interpretation of seismic facies remains an elusive issue. Interpreters are confronted with the selection of the clustering technique and the optimal number of seismic facies that best uncover the spatial distribution of seismic facies. An interpretation framework combining data visualization with the results from various clustering techniques was evaluated. The framework allows interpreters to be directly involved in the seismic facies classification process. Because of the active participation, interpreters (1) gain insight into the detected seismic facies, (2) verify hypotheses with respect to the spatial distribution of seismic facies, (3) compare different seismic facies classification, and (4) gain more confidence with the seismic facies interpretation.

Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. W1-W13 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Preliminary Test ◽  
Seismic Facies ◽  
Least Squares Regression ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Texture Model ◽  
Facies Classification ◽  
Analysis Window

In exploration geology and geophysics, seismic texture is still a developing concept that has not been sufficiently known, although quite a number of different algorithms have been published in the literature. This paper provides a review of the seismic texture concepts and methodologies, focusing on latest developments in seismic amplitude texture analysis, with particular reference to the gray level co-occurrence matrix (GLCM) and the texture model regression (TMR) methods. The GLCM method evaluates spatial arrangements of amplitude samples within an analysis window using a matrix (a two-dimensional histogram) of amplitude co-occurrence. The matrix is then transformed into a suite of texture attributes, such as homogeneity, contrast, and randomness, which provide the basis for seismic facies classification. The TMR method uses a texture model as reference to discriminate among seismic features based on a linear, least-squares regression analysis between the model and the data within an analysis window. By implementing customized texture model schemes, the TMR algorithm has the flexibility to characterize subsurface geology for different purposes. A texture model with a constant phase is effective at enhancing the visibility of seismic structural fabrics, a texture model with a variable phase is helpful for visualizing seismic facies, and a texture model with variable amplitude, frequency, and size is instrumental in calibrating seismic to reservoir properties. Preliminary test case studies in the very recent past have indicated that the latest developments in seismic texture analysis have added to the existing amplitude interpretation theories and methodologies. These and future developments in seismic texture theory and methodologies will hopefully lead to a better understanding of the geologic implications of the seismic texture concept and to an improved geologic interpretation of reflection seismic amplitude.

Predicting and 3D modeling of karst zones using seismic facies analysis in Ordovician carbonates of the Tahe oilfield, China

2020 ◽  
Vol 8 (2) ◽  
pp. T293-T307
Author(s):  
José N. Méndez ◽  
Qiang Jin ◽  
María González ◽  
Wei Hehua ◽  
Cyril D. Boateng
Keyword(s):  
Cross Sections ◽  
Input Data ◽  
Optimal Number ◽  
Seismic Facies ◽  
Rock Properties ◽  
Seismic Attribute ◽  
Clustering Technique ◽  
Yingshan Formation ◽  
Facies Classification ◽  
Seismic Facies Analysis

Karsted carbonates of the Ordovician Yingshan Formation represent significant hydrocarbon reservoirs in the Tarim Basin, China. Due to the geologic complexity of the formation, realistically predicting and modeling karst zones and rock properties is challenging. This drives the need to apply diverse techniques for building a suitable geologic model. We have developed a static model approach that uses fully automated seismic facies classification processes for predicting and modeling patterns associated with karst elements. Our method uses a seismic attribute and well logs as input data. We initially processed a seismic facies volume using the hierarchical clustering technique. This is based on seismic attribute values that take into account an optimal number of classes. The outcome reveals various patterns illustrated with low amplitudes highlighting the geomorphology of paleokarst elements. Simultaneously, a seismic traces map of the karsted interval was processed using the hybrid clustering technique conducted on seismic trace shape. In this case, the karst facies was extracted from the output and used as secondary input data in trend analysis of the model. Both outputs obtained from clustering techniques are processed in a volume of the most probable facies, which delineate the karst patterns. The results of the modeling process are visualized in various time slices and cross sections, appropriately recognizing the relationship of estimated patterns with karst zones. We have evaluated the karstification thickness and porosity map obtained from the 3D model that detail a reasonable connectivity between karst elements. This is based on the paleogeographic location and type of filling, as well as the dissolution development along the main striking faults. Finally, our method outputs a logical model of karst zones located within the host rock, which reduces the uncertainty and identify nonperforated segments.

scholarly journals Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1839
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
José Lemus-Romani ◽  
Marcelo Becerra-Rozas ◽  
José M. Lanza-Gutiérrez ◽  
...  
Keyword(s):  
Combinatorial Problems ◽  
Set Covering ◽  
Covering Problem ◽  
Integration Framework ◽  
Q Learning ◽  
Whale Optimization ◽  
Sine Cosine Algorithm ◽  
Exploration Exploitation ◽  
Selection Of

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

scholarly journals An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3311
Author(s):  
Riccardo Ballarini ◽  
Marco Ghislieri ◽  
Marco Knaflitz ◽  
Valentina Agostini
Keyword(s):  
Motor Task ◽  
Optimal Number ◽  
The Other ◽  
Simulated Dataset ◽  
Muscle Synergies ◽  
Mean Square ◽  
Real Dataset ◽  
Traditional Approaches ◽  
Selection Of ◽  
Semg Signals

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

scholarly journals Open Plot Project: an open-source toolkit for 3-D structural data analysis

Solid Earth ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. Tavani ◽  
P. Arbues ◽  
M. Snidero ◽  
N. Carrera ◽  
J. A. Muñoz
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Structural Data ◽  
Geological Modelling ◽  
Analysis Tools ◽  
Transect Analysis ◽  
Selection Of

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

Semi‐supervised deep autoencoder for seismic facies classification

2021 ◽  
Author(s):  
Xingye Liu ◽  
Bin Li ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Qingchun Li ◽  
...  
Keyword(s):  
Seismic Facies ◽  
Facies Classification

scholarly journals Spatial analysis of deaths from pulmonary tuberculosis in the city of São Luís, Brazil

2014 ◽  
Vol 40 (5) ◽  
pp. 543-551 ◽  
Author(s):  
Marcelino Santos-Neto ◽  
Mellina Yamamura ◽  
Maria Concebida da Cunha Garcia ◽  
Marcela Paschoal Popolin ◽  
Tatiane Ramos dos Santos Silveira ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Pulmonary Tuberculosis ◽  
Secondary Data ◽  
Positive Association ◽  
Significant Positive Association ◽  
Public Health Policies ◽  
Operational Characteristics ◽  
Operational Variables ◽  
The City ◽  
Selection Of

OBJECTIVE: To characterize deaths from pulmonary tuberculosis, according to sociodemographic and operational variables, in the city of São Luís, Brazil, and to describe their spatial distribution. METHODS: This was an exploratory ecological study based on secondary data from death certificates, obtained from the Brazilian Mortality Database, related to deaths from pulmonary tuberculosis. We included all deaths attributed to pulmonary tuberculosis that occurred in the urban area of São Luís between 2008 and 2012. We performed univariate and bivariate analyses of the sociodemographic and operational variables of the deaths investigated, as well as evaluating the spatial distribution of the events by kernel density estimation. RESULTS: During the study period, there were 193 deaths from pulmonary tuberculosis in São Luís. The median age of the affected individuals was 52 years. Of the 193 individuals who died, 142 (73.60%) were male, 133 (68.91%) were Mulatto, 102 (53.13%) were single, and 64 (33.16%) had completed middle school. There was a significant positive association between not having received medical care prior to death and an autopsy having been performed (p = 0.001). A thematic map by density of points showed that the spatial distribution of those deaths was heterogeneous and that the density was as high as 8.12 deaths/km2. CONCLUSIONS: The sociodemographic and operational characteristics of the deaths from pulmonary tuberculosis evaluated in this study, as well as the identification of priority areas for control and surveillance of the disease, could promote public health policies aimed at reducing health inequities, allowing the optimization of resources, as well as informing decisions regarding the selection of strategies and specific interventions targeting the most vulnerable populations.

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