uncertainty parameters
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2021 ◽  
Author(s):  
Samat Ramatullayev ◽  
Shi Su ◽  
Coriolan Rat ◽  
Alaa Maarouf ◽  
Monica Mihai ◽  
...  

Abstract Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Naeem ◽  
Ahmed A. Khammash ◽  
Ibrahim Mahariq ◽  
Ghaylen Laouini ◽  
Jeevan Kafle

In this paper, we designed an algorithm by applying the Laplace transform to calculate some approximate solutions for fuzzy fractional-order nonlinear equal width equations in the sense of Atangana-Baleanu-Caputo derivatives. By analyzing the fuzzy theory, the suggested technique helps the solution of the fuzzy nonlinear equal width equations be investigated as a series of expressions in which the components can be effectively recognised and produce a pair of numerical results with the uncertainty parameters. Several numerical examples are analyzed to validate convergence outcomes for the given problem to show the proposed method’s utility and capability. The simulation outcomes reveal that the fuzzy iterative transform method is an effective method for accurately and precisely studying the behavior of suggested problems. We test the developed algorithm by three different problems. The analytical analysis provided that the results of the models converge to their actual solutions at the integer-order. Furthermore, we find that the fractional derivative produces a wide range of fuzzy results.


2021 ◽  
Vol 11 (21) ◽  
pp. 9972
Author(s):  
Jian Chen ◽  
Mohamed A. Mohamed ◽  
Udaya Dampage ◽  
Mostafa Rezaei ◽  
Saleh H. Salmen ◽  
...  

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts.


2021 ◽  
Author(s):  
Chengshuai Liu ◽  
Bingyan Ma ◽  
Caihong Hu ◽  
Qiang Wu ◽  
Yue Sun ◽  
...  

Abstract Storm Water Management Model (SWMM) is one of the most commonly used models in urban flood simulation. However, because the calibration and verification of the model's uncertainty parameters are extremely dependent on the measured flood data, it is difficult to apply the model in areas lacking data. This study proposes a parameter sample clustering method based on peer research results to determine the uncertainty parameters of SWMM, and compares the simulation results with the simulation results of the manual adjustment method based on measured data. The research shows that the Absolute error (AE), Relative error (RE), Nash efficiency coefficient (NSE), and Coefficient of determination (R2) of the water depth simulated by the parameter sample clustering method are 0.040m, 9.08%, 0.949, 0.967 compared with the measured value, respectively. The value of AE, RE, NSE, and R2 of the manual tuning method during the calibration simulation period are 0.066m, 15.95%, 0.881 and 0.924, respectively. Therefore, the parameter sample clustering method has a better simulation effect than manual tuning method, and it can be further promoted in areas without flood data.


2021 ◽  
Vol 9 ◽  
Author(s):  
Wazif Sallehhudin ◽  
Aya Diab

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.


2021 ◽  
Author(s):  
Phu-Cuong Pham ◽  
Yong-Lin Kuo

Abstract This paper proposes a novel robust proportional derivative adaptive non-singular synergetic control (PDATS) for the delta robot system. A proposal radial basis function approximation neural networks (RBF) compensates for external disturbances and uncertainty parameters. To counteract the chattering noise of the low-resolution encoder, a second-order sliding mode (SOSM) observer in the feedback loop showed the ability to obtain the angular velocity estimations. The stability of the PDATS approach is proven using the Lyapunov stability theory. Both the simulation and experiment result effectiveness and performances of the PDATS controller in trajectory; pick and place operations of a parallel delta robot. The characteristics of the controller demonstrate that the proposed method can effectively reduce external disturbance and uncertainty parameters of the robot by a convergent finite-time, and provide higher accuracy in comparison with finite-time synergetic control and PD control.


2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Komeyl Baghizadeh ◽  
Julia Pahl ◽  
Guiping Hu

In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factory’s fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.


2021 ◽  
Author(s):  
Xupeng He ◽  
Weiwei Zhu ◽  
Ryan Santoso ◽  
Marwa Alsinan ◽  
Hyung Kwak ◽  
...  

Abstract Geologic CO2 Sequestration (GCS) is a promising engineering technology to reduce global greenhouse emissions. Real-time forecasting of CO2 leakage rates is an essential aspect of large-scale GCS deployment. This work introduces a data-driven, physics-featuring surrogate model based on deep-learning technique for CO2 leakage rate forecasting. The workflow for the development of data-driven, physics-featuring surrogate model includes three steps: 1) Datasets Generation: We first identify uncertainty parameters that affect the objective of interests (i.e., CO2 leakage rates). For the identified uncertainty parameters, various realizations are then generated based on Latin Hypercube Sampling (LHS). High-fidelity simulations based on a two-phase black-oil solver within MRST are performed to generate the objective functions. Datasets including inputs (i.e., the uncertainty parameters) and outputs (CO2 leakage rates) are collected. 2) Surrogate Development: In this step, a time-series surrogate model using long short-term memory (LSTM) is constructed to map the nonlinear relationship between these uncertainty parameters as inputs and CO2 leakage rates as outputs. We perform Bayesian optimization to automate the tuning of hyperparameters and network architecture instead of the traditional trial-error tuning process. 3) Uncertainty Analysis: This step aims to perform Monte Carlo (MC) simulations using the successfully trained surrogate model to explore uncertainty propagation. The sampled realizations are collected in the form of distributions from which the probabilistic forecast of percentiles, P10, P50, and P50, are evaluated. We propose a data-driven, physics-featuring surrogate model based on LSTM for CO2 leakage rate forecasting. We demonstrate its performance in terms of accuracy and efficiency by comparing it with ground-truth solutions. The proposed deep-learning workflow shows promising potential and could be readily implemented in commercial-scale GCS for real-time monitoring applications.


Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4971
Author(s):  
Panagiotis Korkidis ◽  
Anastasios Dounis ◽  
Panagiotis Kofinas

This paper focuses on the development of a multi agent control system (MACS), combined with a stochastic based approach for occupancy estimation. The control framework aims to maintain the comfort levels of a building in high levels and reduce the overall energy consumption. Three independent agents, each dedicated to the thermal comfort, the visual comfort, and the indoor air quality, are deployed. A stochastic model describing the CO2 concentration has been studied, focused on the occupancy estimation problem. A probabilistic approach, as well as an evolutionary algorithm, are used to provide insights on the stochastic model. Moreover, in order to induce uncertainty, parameters are treated in a fuzzy modelling framework and the results on the occupancy estimation are investigated. In the control framework, to cope with the continuous state-action space, the three agents utilise Fuzzy Q-learning. Simulation results highlight the precision of parameter and occupancy estimation, as well as the high capabilities of the control framework, when taking into account the occupancy state, as energy consumption is reduced by 55.9%, while the overall comfort index is kept in high levels, with values close to one.


2021 ◽  
Vol 9 (06) ◽  
pp. 42-50
Author(s):  
Emel Hulya Yukseloglu ◽  
◽  
Hakan Arpacik ◽  
FatmaCavus Yonar ◽  
Dilek Salkim Islek ◽  
...  

Calibration is an expectation for quality management systems as well as a need for laboratories. If there is a need to measure the size in the laboratory, there is also a need to determine whether the measuring instrument used there is measuring with the desired accuracy.In our study, the suitability of the digital thermometer, which is one of the devices used in the forensic science laboratory during the examination of the evidence, was examined within the scope of ISO / IEC 17025: 2017 standard.The temperature of the environment is very important during the analysis of the evidence.Therefore, it is important to determine the measurement uncertainty and confidence interval in the thermometer that measures this temperature.For this reason, in our study, the calibration procedures were carried out by selecting the digital thermometer device and using the calibration method compared to the reference.Uncertainty budgets were prepared by calculating the measurement uncertainty parameters one by one. Conformity assessment was made according to the measurement results.


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