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Enhancing fault diagnosis of undesirable events in oil & gas systems: A machine learning approach with new criteria for stability analysis and classification accuracy

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2023年

作者： Sahraoui, Mohammed Amine Rahmoune, Chemseddine Zair, Mohamed Gougam, Fawzi Damou, Ali Univ Mhamed BOUGARA Syst Engn & Telecommun Lab LIST Boumerdes Algeria Univ MhamedBougara Solid Mech & Syst Lab LMSS Boumerdes Algeria

Petroleum serves as a cornerstone of global energy supply, underpinning economic development. Consequently, the effective detection of faults in oil and gas (O&G) wells is of paramount importance. In response to the limitations observed in prior research, this study presents an innovative fault diagnosis system, rooted in machine learning techniques. Our approach encompasses a comprehensive analysis, incorporating stability assessment via standard deviation (STD), and a meticulous evaluation of accuracy and stability for distinct fault scenarios. By integrating data preprocessing, feature selection methods, and deploying a robust random forest classifier, our model achieves a substantial enhancement in fault classification accuracy and stability. Extensive experimentation substantiates the superiority of our approach, surpassing the performance of previous studies that predominantly emphasized overall accuracy while disregarding stability analysis. Notably, our model attains remarkable accuracies, notably achieving a flawless 100% accuracy for scenario 3 faults. Detailed examination of mean accuracies and STDs further reinforces the precision and consistency of our model's predictive capabilities. Additionally, a qualitative assessment underscores the practical utility and reliability of our model in accurately identifying critical fault types. This research significantly advances fault detection methodologies within the O&G industry, providing valuable insights for decision-making systems in oil well operations.

关键词： Fault detection stability analysis accuracy oil & gas undesirable events random forest marine predator algorithm

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Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

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FUEL 2023年第1期337卷

作者： Liang, Bin Liu, Jiang You, Junyu Jia, Jin Pan, Yi Jeong, Hoonyoung Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China Chongqing Univ Sci & Technol Chongqing 401331 Peoples R China Res Inst Petr Explorat & Dev Beijing 100086 Peoples R China Seoul Natl Univ Res Inst Energy & Resources Inst Engn Res Dept Energy Syst Engn Seoul 08826 South Korea

Accurate prediction of hydrocarbon production is crucial for the oil and gas industry. However, the strong heterogeneity of underground formation, the inconsistency in oil-gas-water distribution, and the complex flow mechanisms make hydrocarbon production forecasting (HPF) difficult, which leads to a high level of uncertainty in the prediction results. The explosion of machine learning (ML) methodologies that are capable of analyzing big data shed new light on HPF using production data. In this article, an in-depth review is provided regarding HPF using ML methodologies. Firstly, the merits and drawbacks of traditional HPF methods are analyzed and summarized. Then, the applications of ML algorithms in HPF are reviewed in detail, especially concentrating on artificial neural network, support vector machine, and ensemble learning. For each algorithm, the basic theory and its variants are first introduced, and its applications in HPF are comprehensively demonstrated subsequently. Finally, this article presents the challenge and prospects of machine-learning-based HPF. Sophisticated ML proxy models can be con-structed and employed to deal with an extended type of input data such that improving the efficacy of data utilization. On the other hand, deep learning models designed to handle time-series data can gain more attention. Modeling approaches for multivariate time-series hydrocarbon production data using deep neural networks with similar functionality to LSTM may lead to more accurate and computationally efficient production forecasting.

关键词： AI Artificial intelligence ANN Artificial neural network SVM Support vector machine DCA Decline curve analysis MFNN Multilayer feed-forward neural networks MLP Multilayer perceptron BP Back Propagation algorithm DNN Deep neural network LSTM Long short-term memory GAN Generative adversarial network CNN Convolutional neural network FCNN Fully connected neural network RNN Recurrent neural network BPANN Back propagation artificial neural network NN Neural network cDC-GAN Conditional deep convolutional generative neural network CONV-1D 1D convolutional neural network GA Genetic algorithm DE Differential evolution SA Simulated annealing GSA Gravitational search algorithm PSO Particle swarm optimization MPA marine predator algorithm ICA Imperialist competitive algorithm LM Levenberg Marquardt BRA Bayesian regularization algorithm MNN Multiple-neural-network HONN Higher-order neural network HOSO Higher-order synaptic operations MLMVN Multilayer neural network with multi-valued neurons DLSTM Deep long-short term memory EEMD Ensemble empirical mode decomposition FNN Feed-forward neural network BFGS Quasi-newton algorithm ORF Oil recovery factor GOR Gas oil ratio ADAM Adaptive moment estimation method WOPR well oil production rate SGD stochastic gradient descent SVR Support vector regression LSSVM Least squares support vector machine RSM Response surface method LR Linear regression RF Random forest AdaBoost Adaptive boosting GBDT Gradient boosting decision tree GBM Gradient-boosting machine MORF Multi-objective random forest GBT Gradient boosting tree PCA Principal component analysis RREE Reverse recursive feature elimination

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A Novel Application of Improved marine predators algorithm and Particle Swarm Optimization for Solving the ORPD Problem

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ENERGIES 2020年第21期13卷 5679页

作者： Shaheen, Mohamed A. M. Yousri, Dalia Fathy, Ahmed Hasanien, Hany M. Alkuhayli, Abdulaziz Muyeen, S. M. Future Univ Egypt Fac Engn & Technol Elect Engn Dept Cairo 11835 Egypt Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum 63514 Egypt Jouf Univ Fac Engn Elect Engn Dept Sakakah 74331 Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig 44519 Egypt King Saud Univ Coll Engn Dept Elect Engn POB 800 Riyadh 11421 Saudi Arabia Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA 6102 Australia

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved marine predators algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

关键词： marine predator algorithm optimal reactive power dispatch power losses power system operation

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A Novel Ultra Local Based-Fuzzy PIDF Controller for Frequency Regulation of a Hybrid Microgrid System with High Renewable Energy Penetration and Storage Devices

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PROCESSES 2023年第4期11卷 1093页

作者： Yakout, Ahmed H. AboRas, Kareem M. Kotb, Hossam Alharbi, Mohammed Shouran, Mokhtar Samad, Bdereddin Abdul Ain Shams Univ Fac Engn Dept Elect Engn Cairo 11517 Egypt Alexandria Univ Fac Engn Dept Elect Power & Machines Alexandria 21544 Egypt King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Cardiff Univ Sch Engn Magnet & Mat Res Grp Cardiff CF24 3AA Wales

A new ultra-local control (ULC) model and two marine predator algorithm (MPA)-based controllers;MPA-based proportional-integral-derivative with filter (PIDF) and MPA-based Fuzzy PIDF (FPIDF) controllers;are combined to enhance the frequency response of a hybrid microgrid system. The input scaling factors, boundaries of membership functions, and gains of the FPIDF con-troller are all optimized using the MPA. In order to further enhance the frequency response, the alpha parameter of the proposed ULC model is optimized using MPA. The performance of the pro-posed controller is evaluated in the microgrid system with different renewable energy sources and energy storage devices. Furthermore, a comparison of the proposed MPA-based ULC-PIDF and ULC-FPIDF controllers against the previously designed controllers is presented. Moreover, a vari-ety of scenarios are studied to determine the proposed controller's sensitivity and robustness to changes in wind speed, step loads, solar irradiance, and system parameter changes. The results of time-domain simulations performed in MATLAB/SIMULINK are shown. Finally, the results demonstrate that under all examined conditions, the new ULC-based controllers tend to further enhance the hybrid microgrid system's frequency time response.

关键词： ultra-local control fuzzy control PID load frequency control marine predator algorithm renewable energy

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An extended state observer-based sliding mode control method for hydraulic servo system of marine stabilized platforms

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OCEAN ENGINEERING 2023年第1期279卷

作者： Du, Zunfeng Chen, Xiangyu Zhang, Qingwei Yang, Yuan Tianjin Univ Sch Civil Engn State Key Lab Hydraul Engn Simulat & Safety Tianjin Peoples R China

The marine stabilized platform is a critical apparatus to ensure the stability of offshore equipment and operations with respect to the inertial coordinate system. However, the hydraulic actuating system of the platform is challenging to control due to the severe offshore environment and hydraulic parameter perturbations. Therefore, to improve the stability and anti-disturbance performance of the hydraulic actuators on the marine stabilized platform, an extended state observer-based sliding mode control (ESO-SMC) method is proposed in the paper. We first analyze the kinematic model of the platform and the dynamic model of the hydraulic servo system. Then, ESO-SMC is designed, and its stability is verified. The novel marine predator algorithm (MPA) is used to optimize the parameters in ESO-SMC design. Simulation is performed in MATLAB/Simulink using an engineering ship model and a stabilized platform model on the ship. Compared with the traditional Sliding Mode Control (SMC), Velocity Feedforward PID (VFPID), and PID methods, ESO-SMC performs best in resisting external wave load disturbances and flow gain parameter perturbations. In conclusion, ESO-SMC is a reliable control approach for enhancing the anti-interference performance of the hydraulic servo system on marine stabilized platforms in the simulation.

关键词： marine stabilized platform Hydraulic servo system Sliding mode control Extended state observer Wave load disturbances Parameter perturbations marine predator algorithm

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A novel hybrid particle swarm optimization with marine predators

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Han, Baole Li, Baosheng Qin, Chuandong Xidian Univ Sch Math & Stat Xian 710071 Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Peoples R China Ningxia Key Lab Intelligent Informat & Big Data Pr Yinchuan 750021 Peoples R China

Particle swarm optimization algorithms are often applied to solve optimization problems. However, the traditional particle swarm optimization algorithm has a single search method and is less capable of exploration and exploitation when solving high-dimensional and complex problems. In this paper, a hybrid particle swarm optimization algorithm for marine predators (HMPPSO) is proposed by combining the multi-stage search strategy of marine predators algorithm (MPA). We divide the search process of HMPPSO into three stages: the first stage uses Brownian motion for exploration;the middle stage divides the population into two parts, the first half remains responsible for exploration through Brownian motion. Besides, in the second half of the population, a random wandering strategy is proposed to randomly select five particles so that it can control the update of particles to prevent falling into the local optimal solution;the learning strategy is improved in the later stage. The control parameters are used to adjust the strategy, while making the single case learning method and the example averaging method alternate. This strategy accelerates the convergence of the algorithm while enhancing the diversity of the algorithm in the later stage and improving the development ability of the algorithm. In addition, the population diversity is enhanced using chaotic initialization and opposition-based learning strategies. The algorithm in this paper is applied to different types of CEC2017 benchmark test functions and four multi-dimensional non-linear structural design optimization problems. Compared with other recent algorithms, the results show that the performance of HMPPSO is significantly better than other algorithms.

关键词： Particle swarm optimization marine predator algorithm Multi-stage search strategy Structural design optimization

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A survey of recently developed metaheuristics and their comparative analysis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期117卷

作者： Alorf, Abdulaziz Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia

The aim of this study was to gather, discuss, and compare recently developed metaheuristics to understand the pace of development in the field of metaheuristics and make some recommendations for the research community and practitioners. By thoroughly and comprehensively searching the literature and narrowing the search results, we created with a list of 57 novel metaheuristic algorithms. Based on the availability of the source code, we reviewed and analysed the optimization capability of 26 of these algorithms through a series of experiments. We also evaluated the exploitation and exploration capabilities of these metaheuristics by using 50 unimodal functions and 50 multimodal functions, respectively. In addition, we assessed the capability of these algorithms to balance exploration and exploitation by using 29 shifted, rotated, composite, and hybrid CEC-BC-2017 benchmark functions. Moreover, we evaluated the applicability of these metaheuristics on four real-world constrained engineering optimization problems. To rank the algorithms, we performed a nonparametric statistical test, the Friedman mean rank test. Based on the statistical results for the unimodal and multimodal functions, we declared that the GBO, PO, and MRFO algorithms have better exploration and exploitation capabilities. Based on the results for the CEC-BC-2017 benchmark functions, we found the MPA, FBI, and HBO algorithms to be the most balanced. Finally, based on the results for the constrained engineering optimization problems, we declared that the HBO, GBO, and MA algorithms are the most suitable. Collectively, we confidently recommend the GBO, MPA, PO, and HBO algorithms for real-world optimization problems.

关键词： marine predator algorithm Metaheuristics survey Optimization algorithms Political optimizer Optimization survey Optimization algorithm comparison Engineering optimization

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Enhanced marine predators algorithm for identifying static and dynamic Photovoltaic models parameters

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ENERGY CONVERSION AND MANAGEMENT 2021年 236卷 113971-113971页

作者： Elaziz, Mohamed Abd Thanikanti, Sudhakar Babu Ibrahim, Ibrahim Anwar Lu, Songfeng Nastasi, Benedetto Alotaibi, Majed A. Hossain, Md Alamgir Yousri, Dalia Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Macquarie Univ Sch Engn Sydney NSW 2109 Australia CSIRO Energy 10 Murray Dwyer Cct Mayfield West NSW 2300 Australia Sapienza Univ Rome Dept Planning Design & Technol Architecture Via Flaminia 72 I-00196 Rome Italy King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Griffith Univ Queensland Micro & Nanotechnol Ctr Nathan Qld 4111 Australia Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt

Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ? STD. The obtained value of RMSE ? STD by the proposed algorithm is 0.0084505 ? 1.0971e -17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505 ? 9.6235e -14 and 0.0084505 ? 2.5235e -9). The results show the proposed model?s superiority over

关键词： Solar energy technology marine predator algorithm Parameters estimation Single diode Two diode model

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Big Data Multi-Strategy predator algorithm for Passenger Flow Prediction

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 800-810页

作者： Guo, Peng Zhengzhou Univ Ind Technol Sch Management Zhengzhou 450064 Peoples R China

Faced with the rapidly recovering tourism market, accurate prediction of passenger flow can help local authorities achieve more effective resource regulation. Therefore, based on big data technology, a multi-strategy predator algorithm is proposed, which uses the marine predator algorithm, combined with regularized extreme learning machines and Collaborative Filtering algorithms, to achieve accurate passenger flow prediction. The experiment findings denote that the performance parameters of the algorithm are excellent, with extremely strong convergence performance, and only 30 iterations are needed to reach the optimal solution. The fitting degree of this algorithm is 97.8%, which is 6.27% -19.31% higher than that of long and short-term memory networks, random forest algorithms, and support vector machine regression. In actual passenger flow prediction, the error rate of this algorithm is only 2.29%, which is 3.47% - 6.50% higher than the three comparison algorithms. This study provides a new and efficient prediction method for passenger flow prediction. Its excellent predictive performance can not only help relevant departments predict and manage passenger traffic more accurately, but also provide reference for traffic prediction in other fields. Overall, this study has important reference value and practical significance for the research and practice of passenger flow prediction.

关键词： Passemger Flow Prediction regularized extreme learning machine Collaborative Filtering algorithm marine predator algorithm

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A two-stage SCUC model for distribution networks considering uncertainty and demand response

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HELIYON 2023年第10期9卷 e20189页

作者： Wang, Fei Gan, Lei Zhang, Pengchao State Grid Hubei Jingmen Power Supply Co Jingmen 448000 Peoples R China State Grid Hubei Shiyan Power Supply Co Shiyan 442000 Peoples R China State Grid Hubei Xiangyang Power Supply Co Xiangyang 441000 Peoples R China

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

关键词： Demand response (DR) Uncertainty Component failure of power system marine predator algorithm

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