Automatic lithology prediction from well logging using kernel density estimation

Corina, Anisa Noor; Hovda, Sigve

doi:10.1016/j.petrol.2018.06.012

Cited by 35 publications

(8 citation statements)

References 21 publications

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“…[12], [13]. In geophysical tasks, the application of unsupervised learning methods is rather limited; some examples of use are self-organising maps [14], Vector quantization (VQ) [15], clusterization [16], Fuzzy classification [17], and Kernel Density Estimation (KDE) [18]. In such cases, the resulting clusters were compared with the rocks encountered in the deposit during the exploration phase.…”

Section: Overview Of Contemporary Methodsmentioning

confidence: 99%

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Kuchin

Grundspeņķis

2017

Applied Computer Systems

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-The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan's uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.

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Section: Overview Of Contemporary Methodsmentioning

confidence: 99%

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Kuchin

Grundspeņķis

2017

Applied Computer Systems

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“…An integrated technique was proposed by (Corina and Hovda, 2018) to get accurate lithofacies classification, which was then combined with well log interpretations for exact core permeability modeling. To forecast discrete lithofacies distribution at missing intervals, probabilistic neural networks (PNNs) were used to model lithofacies sequences as a function of well logging data.…”

Section: Recent Work On Drill-coresmentioning

confidence: 99%

Application of machine learning methods to detect and classify Core images using GAN and texture recognition

Nurseitov¹,

Bostanbekov²,

Abdimanap³

et al. 2022

Preprint

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During exploration campaigns, oil companies rely heavily on drill core samples as they provide valuable geological information that helps them find important oil deposits. Traditional core logging techniques are laborious and subjective. Core imaging, a new technique in the oil industry, is used to supplement analysis by rapidly characterising large quantities of drill cores in a nondestructive and noninvasive manner. In this paper, we will present the problem of core detection and classification. The first problem is detecting the cores and segmenting the holes in images by using Faster RCNN and Mask RCNN models respectively. The second problem is filling the hole in the core image by applying the Generative adversarial network(GAN) technique and using Contextual Residual Aggregation(CRA) which creates high frequency residual for missing contents in images. And finally applying Texture recognition models for the classification of core images.

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“…Many professional scholars have established many empirical equations, but these empirical equations cannot be widely used due to the different geological conditions of each region and the complex nonlinear relationship between the logging parameters and porosity . As a powerful tool to solve nonlinear problems, machine learning algorithms are also widely used in the field of reservoir prediction. − The following are some expert application examples.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters

Sun

Zhang

et al. 2023

ACS Omega

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Porosity is an integral part of reservoir evaluation, but in the field of reservoir prediction, due to the complex nonlinear relationship between logging parameters and porosity, linear models cannot accurately predict porosity. Therefore, this paper uses machine learning methods that can better handle the relationship between nonlinear logging parameters and porosity to predict porosity. In this paper, logging data from Tarim Oilfield are selected for model testing, and there is a nonlinear relationship between these parameters and porosity. First, the data features of the logging parameters are extracted by the residual network, which uses the "hop connections" method to transform the original data closer to the target variable. In addition, the residual blocks inside the residual network use jump connections, which alleviates the gradient vanishing problem caused by increasing depth in deep neural networks. The dynamic nature of data would necessitate LSTM in the first place. Then, a bidirectional long short-term network (BiLSTM) is used to predict the porosity of the extracted logging data features. Among them, the BiLSTM is composed of two independent reverse LSTMs, which can better solve the nonlinear prediction problem. In order to further improve the accuracy of the model, this paper introduces an attention mechanism to learn by weighting each of the inputs in proportion to their impact on the porosity. The experimental results also show that the data features extracted by the residual neural network can be better used as the input of the BiLSTM model.

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Automatic lithology prediction from well logging using kernel density estimation

Cited by 35 publications

References 21 publications

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Application of machine learning methods to detect and classify Core images using GAN and texture recognition

Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters

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