A machine learning approach to facies classification using well logs

Bestagini, Paolo; Lipari, Vincenzo; Tubaro, Stefano

doi:10.1190/segam2017-17729805.1

Cited by 71 publications

(31 citation statements)

References 12 publications

(21 reference statements)

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“…Hall (2016) introduced a Geophysical Tutorial where he showed a simple application of machine learning techniques for facies classification with a small dataset of seven wireline logs and associated interpreted facies extracted from ten wells of the H ugoton gas field in southwest Kansas. Several studies, i.e., Zhao et al (2015), Bestagini et al (2017), Sidahmed et al (2017), Bhattacharya and Mishra (2018), explored the usage of machines learning for classifying facies. Rock image classification using a deep convolution neural network has been done by Cheng and Guo (2017).…”

Section: Introductionmentioning

confidence: 99%

The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Saroji

Winata²,

Hidayat³

et al. 2021

Aceh Int. J. Sci. Technol

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Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.

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Section: Introductionmentioning

confidence: 99%

The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Saroji

Winata²,

Hidayat³

et al. 2021

Aceh Int. J. Sci. Technol

View full text Add to dashboard Cite

show abstract

“…In geophysics, specifically for well log measurements, unsupervised learning algorithms such as self-organizing map, cross-entropy clustering, and Gaussian mixture model have been applied to logs for automatic zoning and lithofacies recognition (Fung et al, 1995;Wu et al, 2018). Supervised learning methods, mainly classification or regression neural networks, have been employed for lithological classification, and permeability, porosity, or shear wave velocity estimation from well log data (Ahmadi & Chen, 2019;An et al, 2018;Anemangely et al, 2019;Bestagini et al, 2017). However, few works demonstrate the ability of their ML algorithms in generalizing to new data outside the training data set.…”

Section: Introductionmentioning

confidence: 99%

Shale Anisotropy Model Building Based on Deep Neural Networks

You

Cheng

2020

JGR Solid Earth

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Seismic anisotropy parameters are essential in the processing and interpretation of modern array data with multicomponent, long offsets and wide azimuth acquisitions. Traditional well logs do not measure anisotropy in a vertical well and thus cannot provide the needed information. Conventional calibration‐based as well as recent inversion‐based rock physics modeling methods involve tuning parameters and subjective choices that are largely empirical and difficult to generalize. Here we present a machine learning approach to alleviate these problems. Since it is impossible to collect massive labeled field well log data, we generate paired synthetic data of features (porosity, density, vertical P and S wave velocities, P wave and shear moduli) and labels (bulk and shear moduli of rock matrices and aspect ratio of ellipsoidal cracks). By tuning hyperparameters we obtain an optimal fully connected neural network with four hidden layers that fits well with the synthetic data. The neural network is applied to published laboratory measurements and field well log data from a Chinese well and a U.S. well without any modification. We show that anisotropy models estimated by the deep neural network agree well with the inversion results and with the laboratory measurements. The neural network optimized by extensive training based on massive synthetic data removes the subjectivity in parameter selection, generalizes to different geological environments, and has the potential to provide real‐time anisotropy estimation while logging.

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“…Although machine learning has been significantly used in geoscience fields, the application of this technique in core-based lithofacies identification, a key component to better understanding oil and gas reservoirs, is still limited. Machine-learning techniques have been intensely used to aid seismic-facies classification (de Matos et al, 2007(de Matos et al, , 2011Roy et al, 2014;Qi et al, 2016;Zhao et al, 2016Zhao et al, , 2017Qian et al, 2018), electrofacies classification (Allen and Pranter, 2016), lithofacies classification from well logs (Baldwin et al, 1990;Zhang et al, 1999;Bestagini et al, 2017), to predict permeability in tight sands (Zhang et al, 2018), and even for seismicity studies (Kortström et al, 2016;Perol et al, 2018;Sinha et al, 2018;Wu et al, 2018). Cored wells are important because they are the only data that provide the ground truth of subsurface reservoirs including the lithofacies variations.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks as aid in core lithofacies classification

et al. 2019

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Artificial intelligence methods have a very wide range of applications. From speech recognition to self-driving cars, the development of modern deep-learning architectures is helping researchers to achieve new levels of accuracy in different fields. Although deep convolutional neural networks (CNNs) (a kind of deep-learning technique) have reached or surpassed human-level performance in image recognition tasks, little has been done to transport this new image classification technology to geoscientific problems. We have developed what we believe to be the first use of CNNs to identify lithofacies in cores. We use highly accurate models (trained with millions of images) and transfer learning to classify images of cored carbonate rocks. We found that different modern CNN architectures can achieve high levels of lithologic image classification accuracy (approximately 90%) and can aid in the core description task. This core image classification technique has the potential to greatly standardize and accelerate the description process. We also provide the community with a new set of labeled data that can be used for further geologic/data science studies.

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A machine learning approach to facies classification using well logs

Cited by 71 publications

References 12 publications

The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Shale Anisotropy Model Building Based on Deep Neural Networks

Convolutional neural networks as aid in core lithofacies classification

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