Well-Logging Prediction Based on Hybrid Neural Network Model

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.

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A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽

10.3390/a11110170 ◽

2018 ◽

Vol 11 (11) ◽

pp. 170 ◽

Cited By ~ 2

Author(s):

Zhixi Li ◽

Vincent Tam

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Short Term Memory ◽

Predictive Ability ◽

Trading Strategies ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

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Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Electronics ◽

10.3390/electronics10151834 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1834

Author(s):

Abdullah Aljumah

Keyword(s):

Neural Network ◽

Real World ◽

Short Term Memory ◽

Herd Immunity ◽

Machine Learning Techniques ◽

Support Vector ◽

Sensitive Information ◽

K Nearest Neighbor ◽

Learning Techniques ◽

The World

From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

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Predicting Academic Performance of Deaf Students Using Feed Forward Neural Network and An Improved PSO Algorithm

Webology ◽

10.14704/web/v18si01/web18048 ◽

2021 ◽

Vol 18 (Special Issue 01) ◽

pp. 112-126

Author(s):

V. Sathya Durga ◽

Thangakumar Jeyaprakash

Keyword(s):

Neural Network ◽

Prediction Models ◽

Pso Algorithm ◽

Machine Learning Techniques ◽

Support Vector ◽

Feed Forward Neural Network ◽

Deaf Students ◽

Literacy Rate ◽

The Neural Network ◽

Learning Techniques

Literacy rate of deaf students is very less in India. So there is a need to build an effective academic prediction model for identifying weak deaf students. Many machine learning techniques like Decision tree, Support Vector Machine, Neural Network are used to build prediction models. But the most preferred technique is neural network. It is found out that regression model build with neural networks takes more time to converge and the error rate is quite high. To solve the problems of neural network, we use Particle Swarm Optimization (PSO) for weight adjustment in the neural network. But, one of the main drawback of PSO lies in setting the initial parameters. So, a new PSO algorithm which determines the initial weight of the neural network using regression equation is proposed. The results show that neural network build with the proposed PSO algorithm performs well than neural network build with basic PSO algorithm. The Mean Square Error (MSE) achieved in this work is 0.0998, which is comparatively less than many existing models.

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Application of Artificial Intelligence and Machine Learning Techniques in Classifying Extent of Dementia Across Alzheimer's Image Data

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2021040103 ◽

2021 ◽

Vol 6 (2) ◽

pp. 29-46

Author(s):

Robin Ghosh ◽

Anirudh Reddy Cingreddy ◽

Venkata Melapu ◽

Sravanthi Joginipelli ◽

Supratik Kar

Keyword(s):

Neural Network ◽

Machine Learning ◽

Nearest Neighbor ◽

Image Data ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Mild Dementia ◽

Extreme Gradient Boosting

Alzheimer's disease (AD) is one of the most common forms of dementia and the sixth-leading cause of death in older adults. The presented study has illustrated the applications of deep learning (DL) and associated methods, which could have a broader impact on identifying dementia stages and may guide therapy in the future for multiclass image detection. The studied datasets contain around 6,400 magnetic resonance imaging (MRI) images, each segregated into the severity of Alzheimer's classes: mild dementia, very mild dementia, non-dementia, moderate dementia. These four image specifications were used to classify the dementia stages in each patient applying the convolutional neural network (CNN) algorithm. Employing the CNN-based in silico model, the authors successfully classified and predicted the different AD stages and got around 97.19% accuracy. Again, machine learning (ML) techniques like extreme gradient boosting (XGB), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) offered accuracy of 96.62%, 96.56%, 94.62, and 89.88%, respectively.

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Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods

Complexity ◽

10.1155/2020/6309272 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Zhao Yang ◽

Yifan Wang ◽

Jie Li ◽

Liming Liu ◽

Jiyang Ma ◽

...

Keyword(s):

Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Gradient Boosting ◽

Support Vector ◽

Model Parameters ◽

Neural Network Models ◽

Flow Prediction ◽

Extreme Gradient Boosting

This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.

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Stock Prediction Using Machine Learning

WSEAS TRANSACTIONS ON COMPUTER RESEARCH ◽

10.37394/232018.2021.9.17 ◽

2021 ◽

Vol 9 ◽

pp. 152-158

Author(s):

Shubha Singh ◽

Sreedevi Gutta ◽

Ahmad Hadaegh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Stock Price Prediction ◽

Price Prediction ◽

Learning Techniques ◽

Artificial Neural Network Ann

The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.

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A Thorough Review of Machine Learning Applications in Oil and Gas Industry

10.2118/205720-ms ◽

2021 ◽

Author(s):

Cenk Temizel ◽

Celal Hakan Canbaz ◽

Yildiray Palabiyik ◽

Hakki Aydin ◽

Minh Tran ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Oil And Gas ◽

Data Classification ◽

Machine Learning Techniques ◽

Reservoir Engineering ◽

Support Vector ◽

Back Propagation Algorithm ◽

Well Completion ◽

Learning Techniques

Abstract Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.

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Research on Kick Detection Model Based on Machine Learning

10.1115/omae2021-62785 ◽

2021 ◽

Author(s):

Xianzhi Song ◽

Shiming Duan ◽

Zhijun Pei ◽

Zhaopeng Zhu

Keyword(s):

Neural Network ◽

Linear Model ◽

Early Warning ◽

Oil And Gas ◽

Short Term Memory ◽

Research Progress ◽

Support Vector ◽

Oil And Gas Exploration ◽

Detection Model ◽

Equivalent Time

Abstract With the expansion of oil and gas exploration and the development of complex oil and gas resource areas, the probability of risk is increasing. Kick is one of the high-risk risks of drilling, and timely and accurate early warning is increasingly important. Based on the kick generation mechanism, kick characterization parameters are preliminarily selected. According to the characteristics of the data and previous research progress, Random Forest (RF), Support Vector Machine (SVM), Fully Connected Neural Network (FNN), and Long Short-term Memory Neural Network (LSTM) are established using experimental data from Memorial University of Newfoundland. The test results show that the accuracy of the SVM-linear model is 0.952, and the missing alarm, the false alarm rate is only 0.064, 0.035. Also, through the analysis of the kick response time, the lag time of the SVM-linear model is 3.6s, and the comprehensive equivalent time is 23.13, which shows the best performance. This paper lays a good foundation for the establishment of an intelligent kick early warning model.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-01925-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Moojung Kim ◽

Young Jae Kim ◽

Sung Jin Park ◽

Kwang Gi Kim ◽

Pyung Chun Oh ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Influenza Vaccination ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Age Group ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Abstract Background Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination Methods Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups. Results The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). Conclusions The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

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