Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA

Bhattacharya, Shuvajit; Mishra, Srikanta

doi:10.1016/j.petrol.2018.06.075

Cited by 122 publications

(27 citation statements)

References 21 publications

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“…The hits in group 1 are represented in blue, for group 2 in gray, for group 3 in yellow and for group 4 in green. Bhattacharya & Carr, 2016;Mishra & Datta-Gupta, 2017;Bhattacharya & Mishra, 2018). Notwithstanding, except for Bayesian Network, other methodologies, although robust, fail to overcome the conception of the correlation coefficient as a measure of accuracy, which makes it hard to classify sets of complex and non-linear data in 2-D space.…”

Section: Automatic Lithofacies Classification With T-sne and K-nearest Neighbors Algorithmmentioning

confidence: 99%

Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

et al. 2021

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One of the critical processes in the exploration of hydrocarbons is the identification and prediction of lithofacies that constitute the reservoir. One of the cheapest and most efficient ways to carry out that process is from the interpretation of well log data, which are often obtained continuously and in the majority of drilled wells. The main methodologies used to correlate log data to data obtained in well cores are based on statistical analyses, machine learning models and artificial neural networks. This study aims to test an algorithm of dimension reduction of data together with an unsupervised classification method of predicting lithofacies automatically. The performance of the methodology presented was compared to predictions made with artificial neural networks. We used the t-Distributed Stochastic Neighbor Embedding (t-SNE) as an algorithm for mapping the wells logging data in a smaller feature space. Then, the predictions of facies are performed using a KNN algorithm. The method is assessed in the public dataset of the Hugoton and Panoma fields. Prediction of facies through traditional artificial neural networks obtained an accuracy of 69%, where facies predicted through the t-SNE + K-NN algorithm obtained an accuracy of 79%. Considering the nature of the data, which have high dimensionality and are not linearly correlated, the efficiency of t SNE+KNN can be explained by the ability of the algorithm to identify hidden patterns in a fuzzy boundary in data set. It is important to stress that the application of machine learning algorithms offers relevant benefits to the hydrocarbon exploration sector, such as identifying hidden patterns in high-dimensional datasets, searching for complex and non-linear relationships, and avoiding the need for a preliminary definition of mathematic relations among the model’s input data.

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Section: Automatic Lithofacies Classification With T-sne and K-nearest Neighbors Algorithmmentioning

confidence: 99%

Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

et al. 2021

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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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“…With the rapid development of computer technology, the main applications of machine learning in sedimentary microfacies include KNN [3] , Bayesian network [4] , support vector machine [5] and artificial neural network [6] . It depends on specific geological environment and sedimentary background when these methods mainly construct morphological parameters and physical parameters of GR curves.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

Luo¹,

Zhou²,

Yx³

et al. 2021

Preprint

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Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

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Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA

Cited by 122 publications

References 21 publications

Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

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

Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

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