Artificial immune based self organizing maps for seismic facies analysis

Saraswat, Puneet; Sen, Mrinal K.

doi:10.1190/1.3627541

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…The neuron in the competing layer can automatically compete for the opportunity to respond to the input pattern and adjust the weights W ij by competitive learning. 50 The neuron with the strongest response is known as the winning neuron and is also known as the best matching unit (BMU). After repeated training and weight adjustment, the derived topological mapping can be used for assigning the best fitting category for each input pattern.…”

Section: Sampling and Methodologymentioning

confidence: 99%

Pore Structure Characterization of Eocene Low-Permeability Sandstones via Fractal Analysis and Machine Learning: An Example from the Dongying Depression, Bohai Bay Basin, China

Lü

Liu

2021

ACS Omega

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Poroperm analysis, mercury injection capillary pressure (MICP), and nuclear magnetic resonance (NMR) measurements were performed to delineate the pore structures and fractal behaviors of the Eocene low-permeability sandstones in the Dongying Depression, Bohai Bay Basin, China. Three types of pore structures (I, II, and III) have been classified by applying the self-organizing map (SOM) clustering model. Comparative analysis of three different fractal models indicates that the MICP tubular model and NMR model are quite effective for pore structure characterization. The results show that the reservoirs generally exhibit high fractal dimensions, indicative of complex pore structures. The presence of small pore throats is primarily responsible for the heterogeneities and complexities in the Eocene low-permeability sandstones. A modified Winland model was established for the permeability estimation using MICP data. Different from high-permeability reservoirs or unconventional (e.g., shale and tight formation) reservoirs, r 10 is the best parameter for permeability estimation, indicating that the permeability of the Eocene low-permeability sandstones is largely controlled by the large pore systems. Additionally, a porosity model derived from movable fluids using NMR data has been established and provided better prediction effect compared with the classic Coates and Schlumberger Doll Research (SDR) models. Fractal analysis and permeability estimation are shown to be quite effective for investigating microscopic behaviors and in predicting the reservoir quality of low-permeability sandstone reservoirs.

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Section: Sampling and Methodologymentioning

confidence: 99%

Pore Structure Characterization of Eocene Low-Permeability Sandstones via Fractal Analysis and Machine Learning: An Example from the Dongying Depression, Bohai Bay Basin, China

Lü

Liu

2021

ACS Omega

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“…Several seismic facies can be distinguished: transparent, chaotic, linear and shingles (dGB Earth Sciences B.V., 2013). We use our proposed algorithm on the filtered data provided by Opendtect to delineate faults, fractures and channels (Saraswat and Sen, 2012) along the MFS4 horizon of interest (the blue line in Fig. 8).…”

Section: A P P L I Cat I O N O N R E a L S E I S M I C Datamentioning

confidence: 99%

Dynamic subwindow matching: A new similarity measure for seismic facies analysis

Song

Qiu

Chen

et al. 2022

Geophysical Prospecting

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Seismic facies analysis is usually implemented using pattern recognition methods to classify seismic waveforms extracted along a horizon with a fixed time window. The commonly used similarity measures in those technologies are sensitive to the accuracy of horizon interpretation and the window selection and may thus lead to an unsatisfactory facies map. In this paper, we propose a novel similarity measure called dynamic subwindow matching for seismic facies analysis. The proposed measure is the average similarity of a series of short waveforms containing optimal matching subwindows instead of using the whole signal as done in traditional measures. Because of the introduction of nonlinear alignments for seismic reflection events, the proposed measure has the ability to overcome the horizon inaccuracy problem in seismic facies analysis. Furthermore, it can be directly applied to measure the similarity of the waveforms with different time lengths from the targeted interval. Finally, a new method combining the proposed measure and the k-nearest neighbours method is developed for supervised seismic facies analysis. The synthetic and field data application demonstrate that the proposed measure is robust to amplitude and horizon interpretation noise and provide an effective approach for seismic facies analysis of varying-thickness reservoirs.

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“…Other techniques are support vector machines (Li and Castagna 2004), unsupervised techniques such as self-organising maps (SOMs) (Kohonen 2001;Taner et al 2001;Zhang, Quieren and Schuelke 2001;Coléou, Poupon and Azbel 2003;, 2004a,b, 2007Roy and Marfurt 2011), and, recently, generative topographic mapping (Roy et al 2014;Chopra and Marfurt 2014). In fact, one of the most attractive applications of SOMs in typical problems regarding seismic facies analysis has been performed by Saraswat and Sen (2012). In that case, an algorithm emulating the typical interaction mechanism of vertebrates' immune system is used to reduce the dimensionality of seismic data before SOM clustering.…”

Section: Introductionmentioning

confidence: 99%

“…; Chopra and Marfurt ). In fact, one of the most attractive applications of SOMs in typical problems regarding seismic facies analysis has been performed by Saraswat and Sen (). In that case, an algorithm emulating the typical interaction mechanism of vertebrates’ immune system is used to reduce the dimensionality of seismic data before SOM clustering.…”

Section: Introductionmentioning

confidence: 99%

Seismic facies analysis through musical attributes

Amendola

Gabbriellini

Dell’Aversana

et al. 2017

Geophysical Prospecting

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Seismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called sonification, mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.

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Artificial immune based self organizing maps for seismic facies analysis

Cited by 6 publications

References 8 publications

Pore Structure Characterization of Eocene Low-Permeability Sandstones via Fractal Analysis and Machine Learning: An Example from the Dongying Depression, Bohai Bay Basin, China

Pore Structure Characterization of Eocene Low-Permeability Sandstones via Fractal Analysis and Machine Learning: An Example from the Dongying Depression, Bohai Bay Basin, China

Dynamic subwindow matching: A new similarity measure for seismic facies analysis

Seismic facies analysis through musical attributes

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