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

ARABIAN JOURNAL OF GEOSCIENCES 2021年第23期14卷 1-13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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Designing a system for a process parameter determined through modified PSO and fuzzy neural network

Designing a system for a process parameter determined throug...

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12th Pacific-Asia Conference on Knowledge Discovery and Data Mining

作者： Wong, Jui-Tsung Chen, Kuei-Hsien Su, Chwen-Tzeng Natl Yunlin Univ Sci & Technol Dept Ind Engn & Management 123 Univ Rd Sec 3 Douliou Yunlin 640 Taiwan Shih Chien Univ Kaohsiung Campus Dept Int Business Kaohsiung 845 Taiwan

ISBN: (纸本)9783540681243

In the manufacturing industry, the key to retaining a competitive advantage lies in increased yield and reduced a number of reworks. Determining the optimal parameters for the process so that the quality characteristics can meet the target is an important strategy. Traditional statistical techniques such as response surface methodology and analysis of variance, whose basic assumptions must be met, are generally used in this regard. In recent years, artificial intelligence has reached a sufficient level of maturity and is extensively being used in various domains. This paper proposes a system based on the modified particle swarm optimizer (PSO) and the adaptive network-based fuzzy inference system (ANFIS) to determine the process parameters. A perturbed strategy is incorporated into the modified PSO. The application of this system is then demonstrated with the determining of parameters in the wire bonding process in the IC packaging industry. Moreover, the performance of the modified PSO is evaluated with testing functions. The results show that the modified PSO yielded a superior performance to traditional PSO. In the optimization of the process parameter, the modified PSO is able to find the optimal solution in the ANFIS model.

关键词： wire bonding process determination of process parameters modified particle swarm optimizer adaptive network-based fuzzy inference system

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Applying a computational intelligence method to predict the rehabilitation treatment for females with lateral patellar displacement

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Performance Enhancement and Health 2018年第1期6卷 36-42页

作者： Karimzadehfini, Atiye Zolaktaf, Vahid Mahdavinejad, Reza Department of Sports Injuries and Corrective Exercises University of Isfahan Isfahan Iran

A lateral positioned patella has long been regarded as a major contributing factor in the development of patella femoral pain (PFP). Despite extensive research, there is still little consensus as to the most effective treatment strategy for the management of patients with lateral patellar displacement (LPD). Computational intelligence methods are proving useful aids to physicians and other medical staff, improving objectivity when making diagnostic and treatment decisions and reducing the time to make decisions. This study describes an adaptive network-based fuzzy inference system (ANFIS) used to build a model for the indirect prediction of rehabilitation treatment outcomes for females with LPD from only demographic and clinical characteristics. The prediction abilities offered using two ANFIS models are presented using data from 48 female patients referred to rehabilitation clinics of Isfahan Ayatollah Kashani and Al Zahra hospitals, Iran. The results indicate that the ANFIS model has strong potential to improve indirect prediction of rehabilitation treatment for females with LPD with a high degree of accuracy and robustness. © 2017

关键词： adaptive network-based fuzzy inference system fuzzy c-means clustering method Lateral patellar displacement Rehabilitation treatment Subtractive clustering method

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An ANFIS based Model for Predicting Frost Heaving in Seasonal Frozen Regions

An ANFIS Based Model for Predicting Frost Heaving in Seasona...

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3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Xiang Yiming Liu Xueyan Ling Guixiang Du Bin Guizhou Commun Planning Survey & Design Inst Co L Guiyang 550022 Peoples R China

ISBN: (纸本)9783038350057

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

关键词： Frost heaving hybrid learning algorithm subtractive clustering algorithm adaptive network-based fuzzy inference system

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An Expert system based on Discrete Wavelet Transform - ANFIS For Acquisition and Recognition of Invariant Features from Texture Images 23

An Expert System Based on Discrete Wavelet Transform - ANFIS...

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23nd Signal Processing and Communications Applications Conference (SIU)

作者： Avci, Derya Poyraz, Mustafa Leblebicioglu, Mehmet Kemal Firat Univ Elekt Elekt Muhendisligi Bolumu Elazig Turkey Ortadogu Tekn Univ Elekt Elekt Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781467373869

Texture is low-level image features. Today, texture analysis is applied to different fields such as ultrasound images recognition, document classification, classification of radar imagery, texture-based image retrieval. Texture analysis and classification is an important issue. Comparison of texture analysis and classification was characterized by the lack of adequate methods. Wavelet decomposition method is superior to other conventional methods. In this study, Discrete Wavelet Transform (DWT) adaptive network based fuzzy inference system (ANFIS) were used for acqusition and recognition invariant properties of tissue-type image. 22 texture images were taken from Brodatz database for the analysis of tissue-type image. 50 randomly 64 x 64 regions are selected from each of 22 images. Testing Succes with the average rate of 91.27% was obtained by using DWT-ANFIS method.

关键词： Expert systems texture images discrete wavelet transform entropy adaptive network-based fuzzy inference system

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Towards the short-term forecasting of direct normal irradiance using a sky imager

Towards the short-term forecasting of direct normal irradian...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Nou, Julien Chauvin, Remi Eynard, Julien Thil, Stephane Grieu, Stephane Tecnosud PROMES CNRS UPR 8521 Rambla Thermodynam F-66100 Perpignan France Univ Perpignan Via Domitia52 Ave Paul Alduy F-66860 Perpignan France

In a context of sustainable development, interest for Concentrating Solar Power (CSP) is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Indeed, in CSP plants, electricity generation is directly impacted by both availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). Moreover, in the framework of the CSPIMP research project, PROMES-CNRS has developped a sky imager able to provide High Dymanic Range (HDR) images. As a result, the present paper deals with the short-term forecasting of DNI using sky-imaging data. Preliminary results highlight that models (in particular based on artificial intelligence tools) that make use of the fractional cloud cover have the potential to outperform persistence models in terms of forecasting accuracy. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： direct normal irradiance fractional cloud cover clear-sky index concentrating solar power adaptive network-based fuzzy inference system

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Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

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INTERNATIONAL JOURNAL OF FORECASTING 2011年第3期27卷 804-816页

作者： Ebrahimpour, Reza Nikoo, Hossein Masoudnia, Saeed Yousefi, Mohammad Reza Ghaemi, Mohammad Sajjad Inst Res Fundamental Sci Sch Cognit Sci Niavaran Iran Shahid Rajaee Univ Dept Elect & Comp Engn Tehran Iran Islamic Azad Univ Qazvin Management & Accounting Fac Qazvin Iran Univ Tehran Dept Math Stat & Comp Sci Tehran 14174 Iran Int Max Planck Res Sch Comp Sci Saarbrucken Germany Sharif Univ Technol Dept Math Sci Tehran Iran RCISP Tehran Iran

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an adaptive network-based fuzzy inference system (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company's daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

关键词： Modified stacked generalization Mixture of MLP experts (ME) adaptive network-based fuzzy inference system Combining neural networks Time series forecasting

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Evaluation of Inputs for Mach Number Prediction Model based on ANFIS 28

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

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28th Chinese Control and Decision Conference

作者： Wang, Biao Mao, Zhizhong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781467397148

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 model. Then based on these two variables, twelve ANFIS models were constructed and one real-life data set is employed to evaluate performances of these models. Results show that the feedback of previous output to input node can improve the performances of prediction models whatever inputs are. Finally 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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Faulty Sensor Detection, Identification and Reconstruction of Indoor Air Quality Measurements in a Subway Station

Faulty Sensor Detection, Identification and Reconstruction o...

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11th International Conference on Control, Automation and systems (ICCAS)/Robot World Conference

作者： Liu, Hongbin Huang, Mingzhi Janghorban, Iman Ghorbannezhad, Payam Yoo, ChangKyoo Kyung Hee Univ Dept Environm Sci & Engn Yongin 446701 South Korea

ISBN: (纸本)9788993215038

Indoor air quality (IAQ) is important in subway stations because it can influence the health and comfort of passengers significantly. To effectively monitor and control the IAQ in subway stations, several key air pollutants data were collected by the air sampler and tele-monitoring system. In this study, an air pollutant prediction model based an adaptive network-based fuzzy inference system (ANFIS) was used to detect sensor fault, and a structured residual approach with maximum sensitivity (SRAMS) method was used to identify and reconstruct sensor faults existing in subway system. When a sensor failure was detected, the faulty sensor was identified using the exponential weighted moving average filtered squared residual (FSR). Four identification indices, including the identification index based on FSR (I-FSR), the identification index based on generalized likelihood ratio (I-GLR), the identification index based on cumulative sum of residuals (I-Qsum), and the identification index based on cumulative variances index (I-Vsum) were used to assist in identifying sensor faults. The best reconstructed sensor value can be estimated based on a given sensor fault direction. The drifting sensor failure was tested and the effectiveness of the proposed sensor validation procedure was verified.

关键词： Sensor validation Fault detection and identification Fault reconstruction adaptive network-based fuzzy inference system Indoor air quality Subway 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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21st Chinese Control and Decision Conference

作者： Ding Guang-bin Ding Jia-feng Hebei Univ Engn Sch Conservancy & Hydropower Handan 056021 Peoples R China

ISBN: (纸本)9781424427239

In this paper, an improved hydrological medium and long-term forecast method based on chaos and trend term is presented. First, 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 series. Finally, the adaptive network-based fuzzy inference system is applied to predict the residual series for obtaining the prediction result. The 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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