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Evolving new strategies to estimate reservoir oil formation volume factor: Smart modeling and correlation development

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 181卷 106180-000页

作者： Saghafi, Hamid Reza Rostami, Alireza Arabloo, Milad IOR Res Inst Vanak Sq Tehran Iran Petr Univ Technol Dept Petr Engn Ahvaz Iran Islamic Azad Univ North Tehran Branch Young Researchers & Elites Club Tehran Iran

The precise estimation of oilfield PVT properties is of primary importance for improving field evaluation and development strategies. In the present work, adaptive network-based fuzzy inference system (ANFIS) and genetic programming (GP) models were established for the accurate assessment of reservoir oil formation volume factor (OFVF). A large database including nearly 1200 datapoints from all over the globe was collected for modeling. The performance of the new models was compared to correlations in the existing literature via several graphical and statistical paradigms. Variance approach analysis was implemented to determine the sensitivity of the target parameter as the output with respect to each input variable. It is shown that the highest forecasting accuracy is attained using the developed ANFIS model and GP-based empirical correlation with an average absolute relative deviation (AARD%) of 1.8% and 2.1%, respectively, in comparison to the results obtained from other approaches described in the literature. Lastly, the mathematical techniques designed for estimating OFVF can be of high importance for experts working with reservoir simulation and PVT analysis.

关键词： Oil formation volume factor adaptive network-based fuzzy inference system Genetic programming PVT data

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Atmospheric Turbidity Forecasting using Side-by-side ANFIS

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Energy Procedia 2014年 49卷 2387-2397页

作者： J. Nou R. Chauvin A. Traoré S. Thil S. Grieu PROMES-CNRS Rambla de la thermodynamique Tecnosud 66100 Perpignan France University of Perpignan Via Domitia 52 Avenue Paul Alduy 66860 Perpignan France

In a context of sustainable development, enthusiasm for CSP technologies is increasing. In addition, the CSPIMP (Concentrated Solar Power efficiency IMProvement) European project has been recently initiated to achieve a better competitiveness of the CSP plants. Its main objective is to develop a new procedure to improve the steam turbine start up cycles, maintenance activities and advanced plant control schemes. A challenge in the project is to forecast the solar resource with the aim of improving the management of CSP plants. A key parameter when trying to estimate or forecast solar radiation is atmospheric turbidity. Indeed, the Direct Normal Irradiance (DNI) under clear sky conditions can be expressed as a function of extraterrestrial irradiation, altitude and atmospheric turbidity. So, this paper focuses on forecasting atmospheric turbidity at different time horizons (up to 3 hours) using side-by-side adaptive network-based fuzzy inference systems (ANFIS). First, a Multi-Resolution Analysis (MRA) based on the discrete wavelet transform allowed clear sky DNI values to be extracted from the NREL database. In addition, a Principal Component Analysis (PCA) has been considered in order to develop the forecasting model using uncorrelated input variables and reduce its complexity (and, as a consequence, computation time). Finally, the results we obtained about atmospheric turbidity forecasting are satisfactory and validate the proposed approach.

关键词： Atmospheric turbidity forecasting adaptive network-based fuzzy inference system principal component analysis wavelet-based multi-resolution analysis.

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Intra-Day DNI Forecasting Under Clear Sky Conditions Using ANFIS

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IFAC Proceedings Volumes 2014年第3期47卷 10361-10366页

作者： Rémi Chauvin Julien Nou Stéphane Thil Stéphane Grieu PROMES-CNRS (UPR 8521) Tecnosud Rambla de la thermodynamique 66100 Perpignan France University of Perpignan Via Domitia 52 Avenue Paul Alduy 66860 Perpignan France Tel.: +334-6868-2222

The present paper deals with the developement of a new intra-day Direct Normal Irradiance (DNI) forecasting methodology, under clear sky conditions. Indeed, one challenge of the CSPIMP (Concentrated Solar Power plant efficiency IMProvement) research project is to forecast the sun's resource accurately and design efficient plant control approaches. First, a quick review of the different formulations available for the atmospheric turbidity coefficient (from which DNI can be calculated) is performed. The data selection and filtering process is then described. Finally, the new forecasting approaches are compared to persistent and autoregressive models. The most efficient model presented here is based on side-by-side adaptive network-based fuzzy inference systems (ANFIS). The selected configuration achieves very good results and validates the proposed forecasting methodology.

关键词： direct normal irradiance atmospheric turbidity clear sky conditions forecasting techniques adaptive network-based fuzzy inference system

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Path optimization for multiple humanoid robot using TLBO based ANFIS controller in obscure environment

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Materials Today: Proceedings 2021年 47卷 2677-2684页

作者： Abhishek Kumar Kashyap Anish Pandey Dayal R. Parhi Abhishek Sharma Mechanical Engineering Department National Institute of Technology Rourkela 769008 Odisha India School of Mechanical Engineering Kalinga Institute of Industrial Technology Bhubaneswar 751024 Odisha India Department of Mechanical Engineering Manipal University Jaipur Jaipur 303007 Rajasthan India

Humanoid robot NAO is an intelligent reprogrammable agent designed to execute desired work using wireless control system by sensing the working environment at the same time. This article concentrates on the analysis of hybrid intelligent navigation approaches for single and multiple humanoid NAOs. It describes the optimization of a collision-free path in a static and dynamic terrain. The hybridization introduced is designed in two steps. ANFIS (adaptive network-based fuzzy inference system) controller produces a transitional driving angle (TDA) using a robot location with respect to obstacle distance in all directions. It is fed to Teacher Learner based Optimization (TLBO) approach that produces optimum driving angle (ODA) for the humanoid robot to guide along with its predefined target. It optimizes the path selection by taking Euclidean distance and ODA as the key factor. The hybridized controller is examined in an environment that consists of single and multiple NAOs. Case of inter-collision may occur in the path planning of multiple humanoid NAOs. It is eliminated with the integration of the dining philosopher controller in the base algorithm to prioritized one robot towards the assigned target. Simulation results demonstrate the proposed controller's efficacy and explain that it gives optimized path length and travel time. In addition, a comparative study with previously applied effective algorithms is done.

关键词： Teacher learner based optimization adaptive network-based fuzzy inference system Multiple humanoid robots Inter-collision avoidance Dining philosopher controller

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Optimized Dissolved Oxygen fuzzy Control for Recombinant Escherichia coli Cultivations

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ALGORITHMS 2021年第11期14卷 326-326页

作者： Akisue, Rafael Akira Harth, Matheus Lopes Horta, Antonio Carlos Luperni de Sousa Junior, Ruy Univ Fed Sao Carlos Grad Program Chem Engn Campus Sao CarlosRod Washington Luis Km 235 BR-13565905 Sao Carlos Brazil Univ Fed Sao Carlos Dept Chem Engn Campus Sao CarlosWashington Luis Km 235 BR-13565905 Sao Carlos Brazil

Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant Escherichia coli cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an adaptive network-based fuzzy inference system tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and E. coli cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by E. coli during the cultivation.

关键词： adaptive network-based fuzzy inference system dissolved oxygen fuzzy control recombinant Escherichia coli SUPERSYS_HCDC

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A new aspect of the ApEn application to improve the PSO-ANFIS model to forecast Caspian Sea levels

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REGIONAL STUDIES IN MARINE SCIENCE 2024年 69卷

作者： Zanganeh, Morteza Chaji, Alireza Golestan Univ Fac Engn Dept Civil Engn Golestan Iran Shahid Chamran Univ Ahvaz Dept Elect Engn Shohadaye Hoveizeh Campus Technol Ahvaz Iran

Short-term forecasting of sea levels at the Caspian Sea plays an important issue for coastal planning. Therefore, a new aspect of the Approximate Entropy (ApEn) method application is studied to improve the Combined Particle Swarm Optimization and the adaptive network-based fuzzy inference system (PSO-ANFIS), Auto-Regressive Integrated Moving Average (ARIMA) models and Back Propagation Neural networks (BP-NN). To evaluate the idea, seawater levels from 1961 to 2021 are categorized as training, validation (from 1961 to 2007 at Anzali station), and testing data (from 2008 to 2021 at Amirabad station). Results show the PSO-ANFIS model performance for forecasting the seawater level changes for four randomly selected time intervals and three critical periods. In addition, it is concluded that there are no considerable changes in the PSO-ANFIS model accuracy for embedding dimensions greater than 13 in forecasting of sea water level in the Caspian Sea. At the same time, the employed ApEn shows that there are no significant changes in approximate entropy values for block numbers more than 13 for the Caspian Sea levels. This shows the ApEn method performance to determine embedding dimensions for the forecaster models of time series. In addition, it resulted that the Caspian Sea water levels are on a decreasing trend on average by 5.495 cm every year until 2024 and the PSO-ANFIS model is reliable for 36 months as a short-term forecaster model. Comparing the developed PSO-ANFIS model with Long Short-Term Memory (LSTM) networks shows the PSO-ANFIS model significant superiority to the LSTM.

关键词： Sea Water Level fluctuations Approximate Entropy Particle Swarm Optimization adaptive network-based fuzzy inference system Caspian Sea

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Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

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HYDROLOGICAL PROCESSES 2005年第7期19卷 1431-1444页

作者： Chang, YT Chang, LC Chang, FJ Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 10764 Taiwan

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network-based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M-5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input-output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M-5 curves in real-time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright (c) 2004 John Wiley & Sons, Ltd.

关键词： genetic algorithm artificial neural network fuzzy rule base adaptive network-based fuzzy inference system reservoir operation

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Machine learning models for generating the drilled porosity log for composite formations

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第23期14卷 2700-2700页

作者： Gamal, Hany Elkatatny, Salaheldin Mahmoud, Ahmed Abdulhamid King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Dept Petr Engn Dhahran 31261 Saudi Arabia

Determining the porosity of the drilled formation is a significant task for formation evaluation purposes for further implementation in petroleum reservoir simulation and estimating the hydrocarbon reserves. The cost and time are common limitations for the rock porosity determination by the conventional logging way. To overcome these restrictions, this research presents a new approach for porosity estimation from the drilling data by employing the vision of the fourth industrial revolution. The study utilized three machine learning methods (support vector machine (SVM), adaptive network-based fuzzy inference system (ANFIS), and functional network (FN)) for developing rock porosity prediction models from the drilling parameters for composite types of formation lithology that include sandstone and carbonate formations in addition to shale rock. The drilling data covered the penetration rate, rotating speed, torque, weight on bit, mud pumping rate, and pressure of standpipe with 3817 measurements for developing the models. The three models were optimized for enhancing better accuracy level, and the models were evaluated by calculating the coefficient of correlation (R), variance account for (VAF), average absolute percentage error (AAPE), and root mean square error (RMSE) between the real porosity measurements and the predicted ones. The obtained prediction for the developed models presents a strong prediction for the rock porosity for the three developed models with R greater than 0.92, VAF higher than 0.85, AAPE less than 8.34%, and RMSE below 0.02. For further validation, the models were tested for an unseen dataset of 1000 points which covered the same drilled formation types, and the results confirmed the high level of accuracy. In addition, the study proposed FN-based equation for porosity prediction from the drilling data while drilling composite lithology formations.

关键词： Rock porosity Sensor data adaptive network-based fuzzy inference system Functional network Support vector machine

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Evaluation of Inputs for Mach Number Prediction Model based on ANFIS

Evaluation of Inputs for Mach Number Prediction Model Based ...

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第28届中国控制与决策会议

作者： Biao Wang Zhizhong mao College of Information Science & Engineering Northeastern University

ISBN: (纸本)9781467397155

This study investigates the effect of inputs for Mach number prediction model based on an adaptive network-based fuzzy inference system(ANFIS).Firstly,through mechanism and experimental analysis the displacement of the main exhaust hydraulic servo value and the displacement of mesh screen hydraulic servo valve are selected as the input variables for Mach number prediction *** based on these two variables,twelve ANFIS models were constructed and one real-life data set is employed to evaluate performances of these *** show that the feedback of previous output to input node can improve the performances of prediction models whatever inputs *** consuming time of modeling and forecasting is also compared to provide a tradeoff between accuracy and computational complexity,to which online applications may refer.

关键词： Mach number Prediction model adaptive network-based fuzzy inference system

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An Improved Hydrological Forecast Method based on Chaos and Trend Term

An Improved Hydrological Forecast Method Based on Chaos and ...

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2009中国控制与决策会议

作者： DING Guang-bin,DING Jia-feng School of Conservancy and Hydropower,Hebei University of Engineering,Handan 056021

In this paper,an improved hydrological medium and long-term forecast method based on chaos and trend term is ***,the periodic term and trend term of the hydrological time series in the nearest years are analyzed and then using chaotic characteristic analysis for a difference of the residual time ***,the adaptive network-based fuzzy inference system is applied to predict the residual series for obtaining the prediction *** computation on an actual case shows that the new method is reliable to not only natural flat flow year but also special low flow year.

关键词： Hydrological forecast Time series Chaos adaptive network-based fuzzy inference system

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