Electrical Borehole Imaging

Lüthi, Stefan M.

doi:10.1007/978-3-662-04627-2_5

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Numerous techniques, such as increased fluid loss rates into fissures and fractures or complete circulation loss during drilling operations, have been introduced to identify fracture reservoirs based on accurate observations during drilling (Feng et al 2016;Xu et al 2019). In the mid-1980s, advances in dip meter technology and image logs enable the detection of features such as FVDC (Luthi 2001;Serra 1989;Toyobo et al 2020). Many authors argue that they can predict fracture density (FVDC) using micro-resistivity images (FMI), and the output of this log is image loge (Zellou and Ouenes 2003).…”

Section: Gap Of Knowledge and Proposed Novel Modelmentioning

confidence: 99%

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Rajabi

Beheshtian

Davoodi

et al. 2021

J Petrol Explor Prod Technol

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One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

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Section: Gap Of Knowledge and Proposed Novel Modelmentioning

confidence: 99%

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Rajabi

Beheshtian

Davoodi

et al. 2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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Porous medium permeability estimation for well imagery and characterization using complex resistivity spectra

Valentin

Albuquerque

et al. 2018

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Formation permeability plays a crucial role in oil/gas industry as it helps geologists understand how the soil was formed and the most likely location of a certain reservoir. It also aids engineers and analysts to know whether that reservoir is exploitable or not. However, these measurements are very costly and difficult to obtain. In the past decades, several methods have appeared for estimating permeability from different sources like Nuclear Magnetic Resonance data or well-logs. In this work we tested the relationship between complex impedance spectra and permeability measurements, showing that -for the tested porous medium-it does exist a correlation of almost 98% between the obtained complex electrical impedance spectra and the permeability of those mediums. This link could be used in the oil/gas industry adapting the current electrical imaging tools to perform sweep measurements instead of single-frequency measurements, which could be used, in theory, to obtain permeability measurements with spacial resolution. On the other hand, we also designed and implemented an imaging tool that relies on this principle to obtain complex impedance volume images.Keywords: Complex Resistivity Spectra, Permeability Estimation, Porosity, Borehole Image, Oil, Reservoir.Resumo: As medidas de permeabilidade de formação são chave no relacionadoà caracterização de solos. Este parámetro tem um papel fundamental na indústria de gás eóleo, pois ajuda os geólogos a entender como um certo solo foi formado e a possível e mais provável localização de um certo reservatório; e ajuda os engenheiros e analistas a saber se um certo reservatórioé explorável ou não. No entanto, medidas de permeabilidade são muito custosas e difíceis de se obter. Nasúltimas décadas apareceram novos métodos de estimativa da permeabilidade a partir de fontes de dados como Resonância Magnética Nuclear ou logs de poços. Neste trabalho testamos a relação que medidas de impedância complexa têm com medidas de permeabilidade, provando que -para os médios porosos testados-existe uma correlação de quase 98% entre os espectros de impedância complexa obtidos e a permeabilidade desses médios. Esta relação poderia ser utilizada na indústria deóleo e gás adaptando os sistemas de imageamento elétricos atuais para realizar varreduras de frequência, ao invés de medidas monofrequenciais, as quais poderiam ser posteriormente usadas para criar imagens de permeabilidade estimada. Por outro lado, também realizamos o design e implementação de uma ferramenta de imageamento baseada no princípio de obtenção de imagens de espectros complexos impedância elétrica.

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Electrical Borehole Imaging

Cited by 2 publications

References 22 publications

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Porous medium permeability estimation for well imagery and characterization using complex resistivity spectra

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