The partitioning of petrophysical data: a review

Moss, Brian

doi:10.1144/gsl.sp.1997.122.01.14

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…For determining electrofacies, authors like Doveton (1994) and Moss (1997) suggest to use clustering methods. In this paper, we use a few mathematical methods for automatization of the task of facies and electrofacies identification.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Puskarczyk

2019

Acta Geophys.

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Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network-Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method-support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

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Section: Introductionmentioning

confidence: 99%

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Puskarczyk

2019

Acta Geophys.

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“…For determining electrofacies, authors like Serra [3], Doveton [4], Moss [5] suggest to use cluster analysis in n dimensions with each wireline log as a dimension. In this paper there are shown the results of using Kohonen algorithms for identification beds with different properties recorded on the well logs.…”

Section: Introductionmentioning

confidence: 99%

Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations

Puskarczyk

2018

E3S Web Conf.

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Abstract.The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly, classification and thirdly, the accurate characterization of divisional intervals. Data set comprised of standard well logs from the selected well. Shale formations are represented mainly by claystones, siltstones, and mudstones. The formations are also partially rich in organic matter. During the calculations, information about lithology of stratigraphy weren't taken into account. In the analysis, selforganizing neural network -Kohonen Algorithm (ANN) was used for sweet spots identification. Different networks and different software were tested and the best network was used for application and interpretation. As a results of Kohonen networks, groups corresponding to the gas-bearing intervals were found. The analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in sweet spots is present. Kohonen algorithm was also used for geological interpretation of well log data and electrofacies prediction. Reliable characteristic into groups shows that Ja Mb and Sa Fm which are usually treated as potential sweet spots only partially have good reservoir conditions. It is concluded that ANN appears to be useful and quick tool for preliminary classification of members and sweet spots identification.

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Determination of electrofacies using wireline logs based on multivariate statistical analysis for the Kareem Formation, Gulf of Suez, Egypt

Sharawy

Nabawy

2016

Environ Earth Sci

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The partitioning of petrophysical data: a review

Cited by 8 publications

References 19 publications

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations

Determination of electrofacies using wireline logs based on multivariate statistical analysis for the Kareem Formation, Gulf of Suez, Egypt

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