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Unsupervised identification of electrofacies employing machine learning
Proceedings, 2017Machine learning techniques are widely used in petrophysicics and geophysics to solve complex and non-linear problems of practical importance. In particular, numerous applications for identifying electrofacies from well logs have been conducted. However, there is no unique approach for reliable automatic classification of electrofacies as the accuracy ...
I. Emelyanova +3 more
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Modeling Hydrocarbon Bearing Reservoirs Using Fuzzy SVR and Electrofacies Analysis
NSG2021 27th European Meeting of Environmental and Engineering Geophysics, 2021Summary Permeability is one of the key petrophysical parameters of hydrocarbon bearing formations. One of the crucial roles of this parameter is to estimate production rate in oil bearing reservoirs. Permeability is usually measured by core plugs in laboratory.
N. Moosavi, M. Bagheri
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Maximum Autocorrelation Factors applied to electrofacies classification
SEG Technical Program Expanded Abstracts 2010, 2010A vast amount of data is obtained during the development of a petroleum field. Seismic data, well logs, core and production data, all contribute to a better reservoir characterization and modeling. Several methods of multivariate data analysis can be used to support its interpretation, helping in important tasks as the identification of lithological ...
Rodrigo Duarte Drummond +3 more
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Improved Reservoir Characterization using Petrophysical Classifiers within Electrofacies
SPE Improved Oil Recovery Symposium, 2012Abstract Estimation of permeability in a reservoir is necessary for simulation of production history. In mature fields, cores are limited so estimation of permeability is usually done from permeability-porosity correlations developed from cored wells. In this paper, a methodology is presented to predict permeability from well logs.
Woan Jing Teh +2 more
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Electrofacies classification using Supervised learning algorithms
First EAGE Conference on Machine Learning in Americas, 2020Summary The aim of this paper is to present a simple but effective workflow to classify depositional facies using conventional well logging data and supervised learning algorithms. Facies recognition is a time-consuming task and economically expensive.
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Electrofacies Characterization and Permeability Predictions in Complex Reservoirs
SPE Reservoir Evaluation & Engineering, 2002Summary We propose a two-step approach to permeability prediction from well logs that uses nonparametric regression in conjunction with multivariate statistical analysis. First, we classify the well-log data into electrofacies types.
Sang Heon Lee +2 more
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Adaptive multi-resolution graph-based clustering algorithm for electrofacies analysis
Applied Geophysics, 2020Logging facies analysis is a significant aspect of reservoir description. In particular, as a commonly used method for logging facies identification, Multi-Resolution Graph-based Clustering (MRGC) can perform depth analysis on multidimensional logging curves to predict logging facies.
Hongliang Wu +5 more
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EVALUACIÓN DE ELECTROFACIES EN UN YACIMIENTO DE LA FRANJA PETROLERA NORTE CUBANA
Geosciences = Geociências, 2023Este artículo muestra los resultados obtenidos después de aplicar técnicas de clasificación estadística multivariada sobre registros geofísicos de radiactividad gamma natural, coeficiente de absorción fotoeléctrica, porosidad total, densidad, resistividad eléctrica y caliper en pozos ubicados en el yacimiento Habana del Este, al oeste de la Franja ...
Rosa María VALCARCE +1 more
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Using Spatial Constraints in Clustering for Electrofacies Calculation
2017Petroleum reservoir geological models are usually built in two steps. First, a 3-D model of geological bodies is computed, within which rock properties are expected to be stationary and to have low variability. Such geological domains are referred to as “facies” and are often “electrofacies” obtained by clustering petrophysical log curves and ...
Jean-Marc Chautru +4 more
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Electrofacies analysis of the Asmari reservoir, Marun oil field, SW Iran
Geosciences Journal, 2020This article integrates core and well log data to determine reservoir electrofacies of the Oligo-Miocene Asmari Formation in the western Dezfol Embayment, SW Iran. At the start, an unsupervised neural network was employed based on the selforganizing map (SOM) technique to identify and extract electrofacies groups of Asmari Formation in the Marun ...
Bahman Soleimani +2 more
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