Volume texture extraction for 3D seismic visualization and interpretation

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1294-1302 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Extraction Method ◽  
Large Scale ◽  
Quantitative Information ◽  
Hydrocarbon Exploration ◽  
3D Seismic ◽  
Image Domain ◽  
Seismic Image ◽  
Occurrence Matrix ◽  
Seismic Visualization ◽  
Texture Extraction

Visual inspection of poststack seismic image patterns is effective in recognizing large‐scale seismic features; however, it is not effective in extracting quantitative information to visualize, detect, and map seismic features in an automatic and objective manner. Although conventional seismic attributes have significantly enhanced interpreters' ability to quantify seismic visualization and interpretation, very few attributes are published to characterize both intratrace and intertrace relationships of amplitudes from a three‐dimensional (3D) perspective. These relationships are fundamental to the characterization and identification of certain geological features. Here, I present a volume texture extraction method to overcome these limitations. In a two‐dimensional (2D) image domain where data samples are visualized by pixels (picture elements), a texture has been typically characterized based on a planar texel (textural element) using a gray level co‐occurrence matrix. I extend the concepts to a 3D seismic domain, where reflection amplitudes are visualized by voxels (volume picture elements). By evaluating a voxel co‐occurrence matrix (VCM) based on a cubic texel at each of the voxel locations, the algorithm extracts a plurality of volume textural attributes that are difficult to obtain using conventional seismic attribute extraction algorithms. Case studies indicate that the VCM texture extraction method helps visualize and detect major structural and stratigraphic features that are fundamental to robust seismic interpretation and successful hydrocarbon exploration.

Planning a drilling campaign in a petroleum province using high resolution 3D seismic data – IODP proposal 909

2020 ◽  
Author(s):  
David Cox ◽  
Andrew M. W. Newton ◽  
Paul C. Knutz ◽  
Mads Huuse
Keyword(s):  
High Resolution ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Large Scale ◽  
Greenland Ice Sheet ◽  
Hydrocarbon Exploration ◽  
3D Seismic ◽  
Free Gas ◽  
Shallow Subsurface ◽  
3D Seismic Data

<p>A drilling hazard assessment has been completed for a large area of the NW Greenland-Baffin Bay continental shelf. This assessment was in relation to International Ocean Discovery Program (IODP) proposal 909 that aims to drill several sites across the shelf in an attempt to better understand the evolution and variability of the northern Greenland Ice Sheet. The assessment utilised high quality and extensive 3D seismic data that were acquired during recent hydrocarbon exploration interest in the area – a fact that highlights the risk of drilling in a petroleum province and therefore, the importance of this assessment with regards to safety.</p><p>Scattered seismic anomalies are observed within the Cenozoic sedimentary succession covering the rift basins of the Melville Bay region. These features, potentially representing the presence of free gas or gas-rich fluids, vary in nature from isolated anomalies, fault flags, stacked fluid flow features and canyons; all of which pose a significant drilling risk and were actively avoided during site selection. In areas above the Melville Bay Ridge – a feature that dominates the structure of this area – free gas is also observed trapped beneath extensive gas hydrate deposits, identified via a spectacularly imaged bottom simulating reflector marking the base of the gas hydrate stability zone. The location of the hydrate deposits, and the free gas beneath, are likely controlled by a complicated migration history, due to large scale rift-related faulting and migration along sandy aquifer horizons. In other areas, gas is interpreted to have reached the shallow subsurface due to secondary leakage from a deeper gas reservoir on the ridge crest.</p><p>It is clear that hydrocarbon related hazards within this area are varied and abundant, making it a more challenging location to select sites for an IODP drilling campaign. However, due to the extensive coverage and high resolution (up to 11 m vertical resolution (45 Hz at 2.0 km/s velocity) of the 3D seismic data available, as well as the use of recently acquired ultra-high resolution site survey lines, these features can be accurately imaged and confidently mapped. This allowed for the development of a detailed understanding of the character and distribution of fluids within the shallow subsurface, and the use of this knowledge to select site localities that maximise the potential for drilling to be completed safely and successfully if proposal 909 were to be executed.</p>

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>

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Mineral exploration with 3-D controlled-source electromagnetic method: a synthetic study of Sukhoi Log gold deposit

2019 ◽  
Vol 219 (3) ◽  
pp. 1698-1716 ◽  
Author(s):  
M Malovichko ◽  
A V Tarasov ◽  
N Yavich ◽  
M S Zhdanov
Keyword(s):  
Finite Difference ◽  
Gold Deposit ◽  
Large Scale ◽  
Mineral Exploration ◽  
Mineral Deposit ◽  
Hydrocarbon Exploration ◽  
Contraction Operator ◽  
Inversion Algorithm ◽  
Controlled Source ◽  
Regularized Inversion

SUMMARY This paper presents a feasibility study of using the controlled-source frequency-domain electromagnetic (CSEM) method in mineral exploration. The method has been widely applied for offshore hydrocarbon exploration; however, nowadays this method is rarely used on land. In order to conduct this study, we have developed a fully parallelized forward modelling finite-difference (FD) code based on the iterative solver with contraction-operator preconditioner. The regularized inversion algorithm uses the Gauss–Newton method to minimize the Tikhonov parametric functional with the Laplacian-type stabilizer. A 3-D parallel inversion code, based on the iterative finite-difference solver with the contraction-operator preconditioner, has been evaluated for the solution of the large-scale inverse problems. Using the computer simulation for a synthetic model of Sukhoi Log gold deposit, we have compared the CSEM method with the conventional direct current sounding and the CSEM survey with a single remote transmitter. Our results suggest that, a properly designed electromagnetic survey together with modern 3-D inversion could provide detailed information about the geoelectrical structure of the mineral deposit.

Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

2011 ◽  
Vol 7 (3) ◽  
pp. 88-101 ◽  
Author(s):  
DongHong Sun ◽  
Li Liu ◽  
Peng Zhang ◽  
Xingquan Zhu ◽  
Yong Shi
Keyword(s):  
Linear Programming ◽  
Portfolio Management ◽  
Real World ◽  
Credit Card ◽  
Extraction Method ◽  
Large Scale ◽  
Programming Model ◽  
Rule Extraction ◽  
Multiple Criteria ◽  
Data Sets

Due to the flexibility of multi-criteria optimization, Regularized Multiple Criteria Linear Programming (RMCLP) has received attention in decision support systems. Numerous theoretical and empirical studies have demonstrated that RMCLP is effective and efficient in classifying large scale data sets. However, a possible limitation of RMCLP is poor interpretability and low comprehensibility for end users and experts. This deficiency has limited RMCLP’s use in many real-world applications where both accuracy and transparency of decision making are required, such as in Customer Relationship Management (CRM) and Credit Card Portfolio Management. In this paper, the authors present a clustering based rule extraction method to extract explainable and understandable rules from the RMCLP model. Experiments on both synthetic and real world data sets demonstrate that this rule extraction method can effectively extract explicit decision rules from RMCLP with only a small compromise in performance.

A Blind Extraction Method of FH Signals Based on Improved Symmetric Co-Occurrence Matrix in HF Channel

2014 ◽  
Vol 543-547 ◽  
pp. 2570-2574
Author(s):  
Pei Gu ◽  
Bin Wang
Keyword(s):  
Extraction Method ◽  
Blind Detection ◽  
Threshold Method ◽  
Time Frequency ◽  
Low Snr ◽  
Interference Signals ◽  
Noise Threshold ◽  
Blind Extraction ◽  
Occurrence Matrix ◽  
Hf Channel

To remove the noise and interference signals in the blind detection of FH signals from HF channel, this paper gives a FH signals extraction method based on the improved symmetric GLCM, by combining the time-frequency analysis with the threshold method of GLCM. Firstly, the paper defines the calculation of improved symmetric GLCM on the direction of frequency and time. Then, the noise threshold is estimated based on the noise probability in the improved GLCM of frequency and the FH signals from the improved GLCM of time can be extracted by using the noise threshold. Simulation results show that the method can extract integrated FH signals under low SNR without any prior information, and that the estimation of noise threshold is more accurate and stable. Furthermore the method is simple with a small amount of computation, which is easy to be applied in the engineering.

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