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hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2021年 95卷 104210-104210页

作者： Abad, Abouzar Rajabi Behesht Mousavi, Seyedmohammadvahid Mohamadian, Nima Wood, David A. Ghorbani, Hamzeh Davoodi, Shadfar Alvar, Mehdi Ahmadi Shahbazi, Khalil Islamic Azad Univ Dept Petr Engn Omidiyeh Branch Omidiyeh Iran Islamic Azad Univ Dept Petr Engn Nour Branch Nour Iran Islamic Azad Univ Young Researchers & Elite Club Omidiyeh Branch Omidiyeh Iran DWA Energy Ltd Lincoln LN5 9JP England Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Chamran Univ Dept Comp Engn Fac Engn Ahvaz Iran Petr Univ Technol PUT Dept Petr Engn Ahvaz Iran

Gas condensate reservoirs display unique phase behavior and are highly sensitive to reservoir pressure changes. This makes it difficult to determine their PVT characteristics, including their condensate viscosity, which is a key variable in determining their flow behavior. In this study, a novel machine learning approach is developed to predict condensate viscosity in the near wellbore regions (mu c) from five input variables: pressure (P), temperature (T), initial gas to condensate ratio (RS), gas specific gravity (gamma g), and condensate gravity (API). Due to the absence of accurate recombination methods for determining (mu)c machine learning methods offer a useful alternative approach. Nine machine learning and hybrid machine learning algorithms are evaluated including novel multiple extreme learning machine (MELM), least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) and each hybridized with a particle swarm optimizer (PSO) and genetic algorithm (GA). The new MELM algorithm has some advantages including 1) rapid execution, 2) high accuracy, 3) simple training, 4) avoidance of overfitting, 5) non-linear conversion during training, 6) no trapping at local optima, 6) fewer optimization steps than SVM and LSSVM. Combining MELM with PSO, to find the best control parameters, further improves its performance. Analysis indicates that the MELM-PSO model provides the highest (mu)c prediction accuracy achieving a root mean squared error (RMSE) of 0.0035 cP and a coefficient of determination (R-2) of 0.9931 for a dataset of 2269 data records compiled from gas-condensate fields around the world. The MELM-PSO algorithm generates no outlying data predictions. Spearman correlation coefficient analysis identifies that P, gamma g and Rs are the most influential variables in terms of condensate viscosity based on the large dataset studied.

关键词： hybrid machine learning algorithms Gas condensate viscosity Multi-layer extreme learning machine Multilayer perceptron Least squares support vector machine

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Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

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FUEL 2022年 308卷 121872-121872页

作者： Abad, Abouzar Rajabi Behesht Ghorbani, Hamzeh Mohamadian, Nima Davoodi, Shadfar Mehrad, Mohammad Aghdam, Saeed Khezerloo-ye Nasriani, Hamid Reza Islamic Azad Univ Omidiyeh Branch Dept Petr Engn Omidiyeh Iran Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahrood Iran Amirkabir Univ Technol Dept Petr Engn Tehran Iran Univ Cent Lancashire Sch Engn Fac Sci & Technol Preston Lancs England

Condensate reservoirs are the most challenging hydrocarbon reservoirs in the world. The behavior of condensate gas reservoirs regarding pressure and temperature variation is unique. Adjusting fluid flow rate through wellhead chokes of condensate gas wells is critical and challenging for reservoir management. Predicting this vital parameter is a big step for the development of condensate gas fields. In this study, a novel machine learning approach is developed to predict gas flow rate (Qg) from six input variables: temperature (T);upstream pressure (Pu);downstream pressure (Pd);gas gravity (gamma g);choke diameter (D64) and gas-liquid ratio (GLR). Due to the absence of accurate recombination methods for determining Qg, machine learning methods offer a functional alternative approach. Four hybrid machine learning (HML) algorithms are developed by integrating multiple extreme learning machine (MELM) and least squares support vector machine (LSSVM) with two optimization algorithms, the genetic algorithm (GA) and the particle swarm optimizer (PSO). The evaluation conducted on prediction performance and accuracy of the four HML models developed indicates that the MELM-PSO model has the highest Qg prediction accuracy achieving a root mean squared error (RMSE) of 2.8639 Mscf/d and a coefficient of determination (R2) of 0.9778 for a dataset of 1009 data records compiled from gas-condensate fields around Iran. Comparison of the prediction performance of the HML models developed with those of the previous empirical equations and artificial intelligence models reveals that the novel MELM-PSO model presents superior prediction efficiency and higher computational accuracy. Moreover, the Spearman correlation coefficient analysis performed demonstrates that D64 and GLR are the most influential variables in the gas flow rate for the large dataset evaluated in this study.

关键词： Gas flow rate Multi-hidden layer extreme learning machine hybrid machine learning algorithms Least squares support vector machine Wellhead choke

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Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2021年第12期11卷 4375-4397页

作者： Rajabi, Meysam Beheshtian, Saeed Davoodi, Shadfar Ghorbani, Hamzeh Mohamadian, Nima Radwan, Ahmed E. Alvar, Mehdi Ahmadi Birjand Univ Technol Dept Min Engn Birjand Iran Islamic Azad Univ Dept Petr Engn Omidiyeh Branch Omidiyeh Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran Islamic Azad Univ Omidiyeh Branch Young Researchers & Elite Club Omidiyeh Iran Jagiellonian Univ Fac Geog & Geol Inst Geol Sci Gronostajowa 3a PL-30387 Krakow Poland Shahid Chamran Univ Fac Engn Dept Comp Engn Ahvaz Iran

One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m;R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

关键词： Fracture density Multi-hidden layer extreme learning machine hybrid machine learning algorithms Multi-layer perceptron

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Predicting the daily return direction of the stock market using hybrid machine learning algorithms

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Financial Innovation 2019年第1期5卷 435-454页

作者： Xiao Zhong David Enke Graduate School of Management Clark University313B Carlson Hall950 Main StreetWorcesterMA 01610USA Laboratory for Investment and Financial Engineering Department of Engineering Management and Systems

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market ***,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the *** employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data *** paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic *** and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index *** controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to ***,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning *** addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

关键词： Daily stock return forecasting Return direction classification Data representation hybrid machine learning algorithms Deep neural networks(DNNs) Trading strategies

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A hybrid learning frame work for recognition abnormal events intended from surveillance videos

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 1089-1102页

作者： Babiyola, A. Aruna, S. Sumithra, S. Buvaneswari, B. Meenakshi Sundararajan Engn Coll Dept ECE Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur Tamilnadu India JJ Coll Engn & Technol Dept ECE Trichy Tamilnadu India Panimalar Engn Coll Dept Informat Technol Chennai Tamil Nadu India

The need for a monitoring system has grown as a result of rising crime and anomalous activity. To avoid unusual incidents, the common man initiated video surveillance of important areas, which was then passed on to the government. In typical surveillance operations, surveillance devices create a vast volume of data that must be manually analysed. Manually handling huge data sets in real time results in information loss. To prevent abnormal incidents, the actions in sensitive areas can be properly monitored, evaluated, and alerted to the appropriate authorities. Previous deep learning-based activity identification methods have appeared, but the findings are inaccurate, and the proposed hybrid machine learning algorithms (HMLA) incorporate two detection methods for surveillance videos like as Transfer learning (TL) and Continual learning (CL). As a result, the suspicious activity in the video may be missed. Consequently, numerous image processing and computer vision technologies were used in activity detection to decrease human effort and mistakes in surveillance operations. Activities in sensitive areas can be properly monitored and evaluated to avoid unusual incidents, and the appropriate authorities may be alerted. Hence, in order to decrease human error and effort in surveillance operations, activity recognition embraced a variety of image processing and computer vision technologies. In this present work, the capacity has constraints that impact recognition accuracy. Consequently, this research paper presents a HMLA based technique that uses feature extraction using multilayer (Long Short Term Memory) LSTM, Convolutional Neural Networks (CNN), and Temporal feature extraction using multilayer LSTM to improve identification accuracy by 96% while requiring minimal execution time. To show the superior performance of the proposed hybrid machine learning technique, a standard UCF crime dataset was utilised for experimental analysis and compared to existing deep learning

关键词： hybrid machine learning algorithms surveillance videos transfer learning continual learning recognition abnormal events

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Estimation of Energy Produced in Hydroelectric Power Plant Industrial Automation Using Deep learning and hybrid machine learning Techniques

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2021年第3期49卷 213-232页

作者： Aksoy, Bekir Isparta Univ Appl Sci Dept Mechatron Engn Fac Technol West CampusE-14 Block TR-32100 Isparta Turkey

Today, due to the rapidly increasing environmental pollution, the importance of energy obtained from renewable energy sources is increasing. One of these important renewable energy sources is Hydroelectric Power Plants. Using hybrid-machine learning algorithms, non-hybrid machine learning algorithms and deep learning models, the electrical energy produced by Hydroelectric Power Plant was estimated based on seventeen different input parameters such as flow rate and generator current. In this study, hyperparameters were tuned and feature selection was made with a Genetic Algorithm in order to increase the accuracy rate in artificial intelligence models. In addition, all artificial intelligence models were evaluated according to the processing time, Coefficient of determination, Mean Squared Error, Mean Absolute Error and Root Mean Square Error performance evaluation criteria. It was seen that the electrical energy production value was determined with the Random Forest Algorithm, which is one of the non-hybrid machine learning algorithms, with an accuracy rate of 99.641%, with Genetic Algorithm + Random Forest Algorithm, which is a hybrid machine learning algorithm, with an accuracy rate of 99.672% and with Deep Neural Network, which is one of the deep learning models, with a 99.99% accuracy rate. According to the results, it was determined that the Deep Neural Networks model determined the electrical energy production value with a higher accuracy rate compared to hybrid and non-hybrid machine learning algorithms.

关键词： hybrid machine learning algorithms regression hydroelectric power plant

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Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 202卷 108425-108425页

作者： Rashidi, Sina Mehrad, Mohammad Ghorbani, Hamzeh Wood, David A. Mohamadian, Nima Moghadasi, Jamshid Davoodi, Shadfar Islamic Azad Univ Omidiyeh Branch Dept Petr Engn Omidiyeh Iran Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahrood Iran Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran DWA Energy Ltd Lincoln England Islamic Azad Univ Omidiyeh Branch Young Researchers & Elite Club Omidiyeh Iran Petr Ind Univ Petr Engn Dept Ahvaz Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia

An important requirement of reservoir management is to understand the properties of reservoir fluids and dependent phase behaviors. This makes it possible to determine the properties of reservoir fluids in laboratory pressure-volume-temperature (PVT) tests. Such laboratory tests are costly and time consuming, and reservoir data are sometimes not available. When estimating the in-place fluid volumes and/or designing enhanced recovery processes information on bubble point pressure (BPP) and oil formation volume factor (OFVF) is required. Predicting these two parameters is therefore one of the priorities of reservoir engineers. It is becoming increasingly beneficial to be able to predict BPP and OFVF with efficient machine-learning algorithms based on underlying variables that are more easily measured directly in the field. In this study, a dataset of 638 data records of published crude oil fluid samples from around the world is evaluated. After filtering the dataset for outliers, 591 data records for BPP and 599 datasets for OFVF are evaluated with efficient hybrid machinelearning algorithms (multi-layer extreme learning machine ?MELM? and least squares support vector machine ? LSSVM) optimized using a genetic algorithm ?GA? and a particle swarm optimizer ?PSO? to improve their prediction performance. Four underlying variables are considered for each data record: temperature (T), solution gas oil ratio (Rs), gas specific gravity (?g) and oil gravity (API). The PSO- MELM-PSO hybrid algorithm achieved the highest prediction accuracy measured in terms of root mean square errors (RMSE) of 33.5 psi for BPP and 0.0199 for OFVF. The four-hybrid machine-learning-optimization algorithms evaluated all outperform the empirical relationships used for many decades in the oil industry to predict BPP and OFVF.

关键词： Oil formation volume factor Bubble point pressure Multi-layer extreme learning machine hybrid machine learning algorithms Least squares support vector machine

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Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2021年第2期19卷 2050074-2050074页

作者： Prakaash, A. S. Sivakumar, K. Sathyabama Inst Sci & Technol Dept Math Chennai 600119 Tamil Nadu India

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.

关键词： Data prediction application-oriented hybrid machine learning algorithms Neural Network Fuzzy Regression model Whale Updated Seek Mode-based Cat Swarm Optimization data acquisition feature extraction data normalization Recurrent Neural Network Whale Optimization Algorithm

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