Machine Learning Predictive Models to Estimate the UCS and Tensile Strength of Rocks in Bakken Field

Petroleum Engineering ◽

Average Error ◽

Support Vector ◽

Short Period ◽

Abstract Uniaxial Compressive Strength (UCS) and Tensile Strength (TS) are among the essential rock parameters required and determined for rock mechanical studies in Petroleum Engineering. However, the determination of such parameters requires some laboratory experiments, which may be time-consuming and costly at the same time. In order to estimate these parameters efficiently and in a short period, some mathematical tools have been used by different researchers. When regression tools proved to give good results only in the limited range of data used, machine learning methods proved to be very accurate in generating models that can cover a wide range of data. In this study, two machine learning models were used to predict the UCS and TS, Support Vector Regression optimized by Genetic Algorithm (GA-SVR) and Artificial Neural Networks (ANNs). The results were discussed for both uniaxial compressive strength and tensile strength in terms of coefficient of determination R2, root mean squared error (RMSE) and mean average error (MAE). First, for the case of UCS, values of 0.99 and 0.99, values of 3.41 and 2.9 and values of 2.43 and 1.9 were obtained for R2, RMSE and MAE for the ANN and GA-SVR, respectively. Second, for the TS, the same analogy was followed, a coefficient R2 of 0.99 and 0.99, RMSE values of 0.41 and 0.45 and MAE values of 0.30 and 0.39 were obtained for ANNs and GA-SVR, respectively. The next step was to assess these models on a different dataset consisting of data obtained from Bakken Field in Williston Basin, North Dakota, United States. The models showed excellent results comparing to the correlations they were compared with, outperforming them in terms of R2, RMSE and MAE, giving the following results for ANN and SVR respectively, R2 of 0.93, 0.92, RMSE of 9.54, 11.22 and MAE of 7.28, 9.24. The resultant conclusion of this work is that the use of machine learning algorithms can generate universal models which reduce the time and effort to estimate some complex parameters such as UCS and Tensile Strength.

Evaluation of Sea fog Detection Accuracy Based on Geostationary Satellite Image Using Machine Learning

10.5194/egusphere-egu2020-22319 ◽

2020 ◽

Author(s):

NaKyeong Kim ◽

Suho Bak ◽

Minji Jeong ◽

Hongjoo Yoon

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Satellite Remote Sensing ◽

Geostationary Satellite ◽

Detection Accuracy ◽

Sea Fog ◽

Short Period ◽

Wide Range ◽

Fog Detection

<p><span>A sea fog is a fog caused by the cooling of the air near the ocean-atmosphere boundary layer when the warm sea surface air moves to a cold sea level. Sea fog affects a variety of aspects, including maritime and coastal transportation, military activities and fishing activities. In particular, it is important to detect sea fog as they can lead to ship accidents due to reduced visibility. Due to the wide range of sea fog events and the lack of constant occurrence, it is generally detected through satellite remote sensing. Because sea fog travels in a short period of time, it uses geostationary satellites with higher time resolution than polar satellites to detect fog. A method for detecting fog by using the difference between 11 &#956;m channel and 3.7 &#956;m channel was widely used when detecting fog by satellite remote sensing, but this is difficult to distinguish between lower clouds and fog. Traditional algorithms are difficult to find accurate thresholds for fog and cloud. However, machine learning algorithms can be used as a useful tool to determine this. In this study, based on geostationary satellite imaging data, a comparative analysis of sea fog detection accuracy was conducted through various methods of machine learning, such as Random Forest, Multi-Layer Perceptron, and Convolutional Neural Networks.</span></p>

Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs

Journal of Energy Resources Technology ◽

10.1115/1.4050579 ◽

2021 ◽

Vol 143 (11) ◽

Author(s):

Zeeshan Tariq ◽

Mohamed Mahmoud ◽

Abdulazeez Abdulraheem

Keyword(s):

Machine Learning ◽

Mathematical Models ◽

Petroleum Engineering ◽

Support Vector ◽

Pvt Properties ◽

Empirical Correlations ◽

Oil Viscosity ◽

Engineering Applications ◽

Wide Range ◽

Black Oil

Abstract Pressure–volume–temperature (PVT) properties of crude oil are considered the most important properties in petroleum engineering applications as they are virtually used in every reservoir and production engineering calculation. Determination of these properties in the laboratory is the most accurate way to obtain a representative value, at the same time, it is very expensive. However, in the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the PVT properties. This study demonstrates the combined use of two machine learning (ML) technique, viz., functional network (FN) coupled with particle swarm optimization (PSO) in predicting the black oil PVT properties such as bubble point pressure (Pb), oil formation volume factor at Pb, and oil viscosity at Pb. This study also proposes new mathematical models derived from the coupled FN-PSO model to estimate these properties. The use of proposed mathematical models does not need any ML engine for the execution. A total of 760 data points collected from the different sources were preprocessed and utilized to build and train the machine learning models. The data utilized covered a wide range of values that are quite reasonable in petroleum engineering applications. The performances of the developed models were tested against the most used empirical correlations. The results showed that the proposed PVT models outperformed previous models by demonstrating an error of up to 2%. The proposed FN-PSO models were also compared with other ML techniques such as an artificial neural network, support vector regression, and adaptive neuro-fuzzy inference system, and the results showed that proposed FN-PSO models outperformed other ML techniques.

An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽

10.1186/s42400-021-00074-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Md. Shafiur Rahman ◽

Sajal Halder ◽

Md. Ashraf Uddin ◽

Uzzal Kumar Acharjee

Keyword(s):

Machine Learning ◽

Social Networks ◽

Social Network ◽

Anomaly Detection ◽

Research Area ◽

Security And Privacy ◽

Support Vector ◽

The Social ◽

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes

Materials ◽

10.3390/ma12091475 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1475 ◽

Cited By ~ 3

Author(s):

Safwan Altarazi ◽

Rula Allaf ◽

Firas Alhindawi

Keyword(s):

Machine Learning ◽

Tensile Strength ◽

Predictive Ability ◽

Classification Performance ◽

Polymeric Films ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Extrusion Blow Molding

In this study, machine learning algorithms (MLA) were employed to predict and classify the tensile strength of polymeric films of different compositions as a function of processing conditions. Two film production techniques were investigated, namely compression molding and extrusion-blow molding. Multi-factor experiments were designed with corresponding parameters. A tensile test was conducted on samples and the tensile strength was recorded. Predictive and classification models from nine MLA were developed. Performance analysis demonstrated the superior predictive ability of the support vector machine (SVM) algorithm, in which a coefficient of determination and mean absolute percentage error of 96% and 4%, respectively were obtained for the extrusion-blow molded films. The classification performance of the MLA was also evaluated, with several algorithms exhibiting excellent performance.

Child Behavioral Analysis: Machine Learning based Investigation for Autism Screening and Early Diagnosis

International Journal of Early Childhood Special Education ◽

10.9756/int-jecse/v13i2.211167 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1199-1208

Author(s):

N. Ajaypradeep ◽

Dr.R. Sasikala

Keyword(s):

Machine Learning ◽

Early Diagnosis ◽

Cross Validation ◽

Parameter Tuning ◽

Autism Spectrum ◽

Support Vector ◽

Therapeutic Practice ◽

Nearest Neighbours ◽

Autism is a developmental disorder which affects cognition, social and behavioural functionalities of a person. When a person is affected by autism spectrum disorder, he/she will exhibit peculiar behaviours and those symptoms initiate from that patient’s childhood. Early diagnosis of autism is an important and challenging task. Behavioural analysis a well known therapeutic practice can be adopted for earlier diagnosis of autism. Machine learning is a computational methodology, which can be applied to a wide range of applications in-order to obtain efficient outputs. At present machine learning is especially applied in medical applications such as disease prediction. In our study we evaluated various machine learning algorithms [(Naive bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbours (KNN)] with “k-fold” based cross validation for 3 datasets retrieved from the UCI repository. Additionally we validated the effective accuracy of the estimated results using a clustered cross validation strategy. The process of employing the clustered cross validation scrutinises the parameters which contributes more importance in the dataset. The strategy induces hyper parameter tuning which yields trusted results as it involves double validation. On application of the clustered cross validation for a SVM based model, we obtained an accuracy of 99.6% accuracy for autism child dataset.

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v8i430209 ◽

2021 ◽

pp. 45-59

Author(s):

Shler Farhad Khorshid ◽

Adnan Mohsin Abdulazeez ◽

Amira Bibo Sallow

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Data Mining ◽

Nearest Neighbors ◽

Performance Comparison ◽

Support Vector ◽

K Nearest Neighbors ◽

Data Set ◽

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

Scientific Reports ◽

10.1038/s41598-021-03699-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Manizhe Zarei ◽

Omid Bozorg-Haddad ◽

Sahar Baghban ◽

Mohammad Delpasand ◽

Erfan Goharian ◽

...

Keyword(s):

Machine Learning ◽

Flood Control ◽

Time Lag ◽

Regression Tree ◽

Average Error ◽

Support Vector ◽

Streamflow Forecasts ◽

Flood Damages ◽

Artificial Neural Network Ann

AbstractWater is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

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 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.

International Journal of Advanced Research in Computer Science and Software Engineering ◽

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.