Fault and non-fault areas detection based on seismic data through min/max autocorrelation factors and fuzzy classification

Shakiba, Sima; Asghari, Omid; Khah, Nasser Keshavarz Faraj; Zabihi, Sajjad Sarallah; Tokhmechi, Behzad

doi:10.1016/j.jngse.2015.05.024

“…Clustering seeks to divide the data into clusters that maximize the similarity between the data within each cluster and the similarity between the data within the different clusters. A fuzzy c-means clustering method has been proposed to solve the problem of assigning each data to a particular cluster in each iteration (Bezdek, 1981;Ghadami et al, 2015;Kadkhodaie-Ilkhchi et al, 2010;Kadkhodaie and Amini, 2008;Olayiwola and Sanuade, 2021;Pourreza et al, 2023;Salavati et al, 2023;Shakiba et al, 2015;Tian et al, 2016). In FCM, the objective is to minimize the Eq.…”

Section: Fuzzy C-means Methodsmentioning

confidence: 99%

“…According to the principles of hydraulic flow units, the distribution of the logarithm of the flow zone index in each hydraulic flow unit is the normal distribution. This method applies this principle and determines the number of hydraulic flow units (Al-Rbeawi and Kadhim, 2017;Alhashmi et al, 2016;Bhattacharya et al, 2016;El-Sayed et al, 2021;El Sharawy and Gaafar, 2016;Ismail et al, 2021;Kazemzadeh et al, 2013;Kianoush et al, 2022a;Kianoush et al, 2023a;Mirkamali et al, 2016;Roslin and Esterle, 2016;Salehi et al, 2015;Shakiba et al, 2015). The result is four normal distributions representing four units of hydraulic current (Fig.…”

Section: Histogram Analysismentioning

confidence: 99%

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Eftekhari,

Memariani,

Maleki

et al. 2023

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Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.

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“…According to the principles of hydraulic flow units, the distribution of the logarithm of the flow zone index in each hydraulic flow unit is the normal distribution. This method applies this principle and determines the number of hydraulic flow units (Al-Rbeawi and Kadhim, 2017;Alhashmi et al, 2016;Bhattacharya et al, 2016;El-Sayed et al, 2021;El Sharawy and Gaafar, 2016;Ismail et al, 2021;Kazemzadeh et al, 2013;Kianoush et al, 2022a;Kianoush et al, 2023a;Mirkamali et al, 2016;Roslin and Esterle, 2016;Salehi et al, 2015;Shakiba et al, 2015).…”

Section: Histogram Analysismentioning

confidence: 99%

“…However, unfortunately, in most cases, this convergence does not lead to the general minimum and stops at the first local minimum. For this reason, the FCM algorithm is sensitive to the selection of initial values, and therefore a random selection of initial values leads to unfavorable performance of the algorithm (Bezdek, 1981;Hosseini et al, 2023a;Jafarzadeh et al, 2019;Kadkhodaie and Amini, 2008;Kadkhodaie et al, 2006;Majdi and Beiki, 2019;Shakiba et al, 2015). The general purpose of conducting this study is to determine the final model of reservoir modeling and sedimentary environment for the Asmari reservoir in the Mansouri field, utilizing techniques to identify rock typing, flow units, and electrofacies.…”

Section: Introductionmentioning

confidence: 99%

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units in the Asmari Formation, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.

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“…Estes processos utilizam das mais diversas técnicas relacionadas à classificação e predição de informações geológicas, a saber: regressão linear multiatributos, análise por principais componentes (PCA), k-means, lógica fuzzy, RNA's supervisionadas e não supervisionadas dentre as principais (RODEN; SMITH; SACREY, 2015,ZHAO et al, 2015,SHAKIBA et al, 2015.…”

Section: Justificativaunclassified

Classificação de sismofáceis carbonáticas a partir da técnica Self-Organizing Maps (SOM).

Bronizeski¹

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RESUMOBRONIZESKI, Edgar Davanço. Classificação de sismofácies carbonáticas a partir da técnica Self-Organizing Maps (SOM), 2018, 111 f. Dissertação (Mestrado) -Escola Politécnica, Universidade de São Paulo, Santos, 2018. O desenvolvimento de soluções para a classificação de ambientes de sedimentação ainda é um desafio, sobretudo em ambientes de deposição carbonáticos. Em reservatórios de óleo e gás offshore, onde a geometria dos ambientes deposicionais é fundamental para a estimativa de viabilidade técnica e econômica de exploração e produção, a discriminação destes ambientes depende de dados indiretos. Nesse sentido, a sísmica de reflexão e seus respectivos atributos funcionam como robusta ferramenta interpretativa. Grandes reservas de petróleo offshore se encontram em rochas carbonáticas constituídas, sobretudo, por calcita e dolomita. Estes minerais, no entanto, não apresentam resposta sísmica contrastante, já que a diferença de velocidade entre tais minerais é inexpressiva. A utilização da amplitude sísmica, unicamente, não possibilita diferenciar propriedades das rochas carbonáticas, tal como ocorre com rochas siliciclásticas. Compactação e hidrotermalismo são fatores pós-deposicionais que influenciam na alteração das características de rochas carbonáticas, e apresentam difícil detecção nas seções sísmicas. Tais fatores necessitam de uma abordagem que envolva as propriedades dos múltiplos atributos sísmicos para a respectiva caracterização. A técnica Self-Organizing Maps (SOM) consiste em ferramenta de análise e visualização de dados vetoriais dentro do espaço dimensional definido pelas variáveis. Trata-se de abordagem multi-atributos em que a identificação e classificação das amostras ocorre de maneira não supervisionada. Neste trabalho são utilizados dados sísmicos dos quais foi extraída a amplitude sobre um horizonte interpretado. Foram utilizados também os atributos do traço sísmico complexo, tais como: envelope, fase instantânea e frequência instantânea, além da impedância acústica e derivada espacial. Estes atributos foram escolhidos em função das características geológicas que representam. Além disso, tais atributos têm sido utilizados em outros trabalhos de classificação de fácies sísmicas. Após uma primeira análise SOM, tendo em vista o realce dos atributos de entrada, foi aplicada a Análise por Principais Componentes (APC). Com base nos componentes obtidos, foi realizada uma segunda análise SOM, obtendo-se assim os resultados que mais corresponderam ao sistema deposicional analisado. Foram identificados também clinoformas nos dados sísmicos, que apresentam correlação com a classificação dos resultados assim como um mapa de distribuição de rochas carbonáticas foi elaborado pela interpretação das classes identificadas correlacionando-as ao poço 1-SPS-0029 associando-o ao modelo de deposição de uma plataforma carbonática. Palavras-chave: SOM. Mapas auto organizáveis. Classificação de fácies. Sismofácies. Atributos sísmicos. Plataforma carbonática ABSTRACT BRONIZESKI, Edgar Davanço. Classification o...

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Fault and non-fault areas detection based on seismic data through min/max autocorrelation factors and fuzzy classification

Cited by 22 publications

References 28 publications

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units in the Asmari Formation, SW Iran

Classificação de sismofáceis carbonáticas a partir da técnica Self-Organizing Maps (SOM).

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