Case-based System: Indexing And Retrieval With Fuzzy Hypercube

In some applications with case-based system, the attributes available for indexing are better described as linguistic variables instead of receiving numerical treatment. In these applications, the concept of fuzzy hypercube can be applied to give a geometrical interpretation of similarities among cases. This paper presents an approach that uses geometrical properties of fuzzy hypercube space to make indexing and retrieval processes of cases.2818823Kolodner, J., (1993) Case-Based Reasoning, , Morgan Kaufmann, San Mateo, CAZadeh, L.A., The concept of linguistic variable and its application to approximate reasoning (1975) Information Science, 8 (PART 1), pp. 199-249Pedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, p. 165. , The MIT PressZadeh, L.A., Toward a theory of fuzzy system (1971) Aspect of Network and System Theory, pp. 469-490. , ed. R.E. Kalman and N. De Claris. New York: Holt, Rinehart and WinstonKosko, B., (1992) Neural Networks and Fuzzy Systemsa Dynamical Systems Approach to Machine Intelligence, , Englewwood Cliffs, NJ: Prentice HallSadegh-Zadeh, K., Fundamentals of clinical methodology: 3. Nosology (1999) Artificial Intelligence in Medicine, 17, pp. 87-108Sadegh-Zadeh, K., Fuzzy genomes (2000) Artificial Intelligence in Medicine, 18, pp. 1-28Helgason, C.M., Jobe, T.H., The fuzzy cube and causal efficacy: Representation of concomitant mechanisms in stroke (1998) Neural Networks, 11, pp. 549-555Sadegh-Zadeh, K., Advances in fuzzy theory (1999) Artificial Intelligence in Medicine, 15, pp. 309-32

Classification Of Petroleum Well Drilling Operations Using Support Vector Machine (svm)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.Burges, C.J.C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 1-47Cristianini, N., Shawe-Taylor, J., (2000) An Introduction to support vector machines and other kernel-based learning methods, , Cambridge University Press, CambridgeHsu, C.-W., Lin, C.-J., A comparison of methods for multi-class support vector machines (2002) IEEE Transactions on Neural Networks, 13 (2), pp. 415-425Kyllingstad, A., Horpestad, J.L., Klakegg, S., Kristiansen, A., Aadnoy, B.S., Factors Limiting the Quantitative Use of Mud-Logging Data (1993) Proceedings of the SPE Asia Pacific Oil and Gas Conference, , SingaporeYue, Z.Q., Lee, C.F., Law, K., Tham, L.G., Automatic monitoring of rotary-percussive drilling for ground characterization - illustrated by a case example in Hong Kong (2003) International Journal of Rock Mechanics & Mining Sciences, 41, pp. 573-612Tavares, R.M., Mendes, J.R.P., Morooka, C.K., Plácido, J.C.R., Automated Classification System for Petroleum Well Drilling using Mud-Logging Data (2005) 18th International Congress of Mechanical Engineer, , Offshore & Petroleum and Engineering, Ouro PretoVapnik, V., (1998) Statistical Learning Theory, , John Wiley & Son

A Genetic Neuro-model Reference Adaptive Controller For Petroleum Wells Drilling Operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.Siqueira, C., Antes tarde do que sem contratos (2005) Brasil Energia, 298, pp. 26-28. , In PortugueseUnneland, T., Häuser, M., Real-Time Asset Management: From Vision to Engagement-An Operator's Experience (2005) Proc. SPE Annual Technical Conference and Exhibition, , Dallas, USAIversen, F.P., Cayeux, E., Dvergsnes, E.W., Gravdal, J.E., Vefring, E.H., Mykletun, B., Torsvoll, A., Merlo, A., Monitoring and Control of Drilling Utilizing Continuously Updated Process Models (2005) Proc. IADC/SPE Drilling Conference, , Miami, USADupriest, F.E., Witt, J.W., Remmert, S.M., Maximizing ROP With Real-Time Analysis of Digital Data and MSE (2005) Proc. International Petroleum Technology Conference, , SPE 10607-MS, Doha, QatarLapierre, S., Courville, G., Song, J., Achieving Technical Limits: Expanded Application of Real-Time Pressure-While-Drilling Data Helps Optimize ROP and Hole Cleaning in LargeDiameter, Directional Intervals (2006) Proc. IADC/SPE Drilling Conference, , SPE 99142-MS, Miami, USAA.T. Bourgoyne Jr., F.S. Young Jr., A multiple regression approach to optimal drilling and abnormal detection, Society of Petroleum Engineers Journal, 14(4), 1974, 371-384R.V. Barragan, Otimização dos parâmetros mecânicos nas brocas para obter o custo mínimo de uma fase de um poço, (In Portuguese) Masters Dissertation, Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas. Campinas, Brazil, 1995S. Mohaghegh, R. Arefi, S. Ameri, K. Aminiand, R. Nutter, Petroleum reservoir characterization with the aid of artificial neural networks, Journal of Petroleum Sciences and Engineering, 16(4), 1996, 263- 274Coelho, D.K., Roisenberg, M., Freitas Filho, P.J., Jacinto, C.M.C., Risk Assessment of Drilling and Completion Operations in Petroleum Wells Using a Monte Carlo and a Neural Network Approach (2005) Proc. Winter Simulation Conference, , Orlando, USA1999 D. Dashevskiy, V. Dubinsky, J.D. Macpherson, Application of neural networks for predictive control in drilling dynamics. Proc. SPE Annual Technical Conference and Exhibition, Houston, USA, 1999Norgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K., (2000) Neural networks for modeling and control of dynamic systems: A practitioner's handbook, , London, UK: SpringerBilgesu, H.I., Tetrick, L.T., Altmis, U., Mohaghegh, S., Ameri, S., A new approach for the prediction of rate of penetration (ROP) values (1997) Proc. SPE Eastern Regional Meeting, , Lexington, USAApplication of ARX Neural Networks to Model the Rate of Penetration of Petroleum Wells Drilling, submitted for presentation at The LASTED International Conference on Computational Intelligence, San Francisco, USA, 2006Li, Y., Li, Y., Liu, G., Oil-Pumping System Control Using Nonlinear Homotopy BP Neural Network and Genetic Algorithm (2005) Proc. 2005 IEEE Conference on Control Applications, , Toronto, CanadaHoskins, D., Vagners, J., A Neural Network Based Model Reference Adaptive Controller (1990) Proc. 24th Asiolmar Conference on Signals, Systems and Computer, , Pacific Grove, USADimeo, R., Lee, K.Y., Boiler-Turbine Control System Design Using a Genetic Algorithm (1995) LEEE Transactions on Energy Conversion, 10 (4), pp. 752-759Malachi, Y., Singer, S., A Genetic Algorithm for the Corrective Control of Voltage and Reactive Power (2006) IEEE Transactions on Power Systems, 21 (1), pp. 295-300Sun, X., Mohanty, K.K., Estimation of Flow Functions During Drainage Using Genetic Algorithm (2003) Proc. SPE Annual Technical Conference and Exhibition, , SPE 84548-MS, Denver, USAM. Tavakkolian, F. Jalali F., M.A. Emadi, Production Optimization using Genetic Algorithm Approach, SPE 88901-MS, Proc. Nigeria Annual International Conference and Exhibition, Abuja, Nigeria, 2004Haykin, S., (1999) Neural networks: A comprehensive foundation, , Upper Saddle River, USA: Prentice-HallMarquardt, D., An algorithm for least-squares estimations of nonlinear parameters (1963) SIAM Journal on Applied Mathematics, 11 (2), pp. 431-441Astrom, K.J., Wittenmark, B., (1989) Adaptive Control, , Reading, USA: Addison-WesleyGoldberg, D.E., (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, , Reading, USA: Addison-WesleySiegel, A.F., (1988) Statistics and Data Analysis, , New York, USA: John Wiley & Son

Application Of Arx Neural Networks To Model The Rate Of Penetration Of Petroleum Wells Drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.152157Siqueira, C., Antes tarde do que sem contratos. (In Portuguese) (2005) Brasil Energia, 298, pp. 26-28Unneland, T., Hauser, M., Real-Time Asset Management: From Vision to Engagement-An Operator's Experience (2005) Proc. SPE Annual Technical Conference and Exhibition, , Dallas, USAIversen, F.P., Cayeux, E., Dvergsnes, E.W., Gravdal, J.E., Vefring, E.H., Mykletun, B., Torsvoll, A., Merlo, A., Monitoring and Control of Drilling Utilizing Continuously Updated Process Models (2005) Proc. IADC/SPE Drilling Conference, , Miami, USAThonhauser, G., Wallnoefer, G., Mathis, W., Ettl, J., Use of Real-Time Rig-Sensor Data To Improve Daily Drilling Reporting, Benchmarking, and Planning - A Case Study (2006) Proc. Intelligent Energy Conference and Exhibition, , Amsterdam, The NetherlandsBourgoyne Jr., A.T., Young Jr., F.S., A multiple regression approach to optimal drilling and abnormal detection (1974), pp. 371-384. , 4R.V. Barragan, Otimização dos parâmetros mecânicos nas brocas para obter o custo mínimo de uma fase de um poço, (In Portuguese) Masters Dissertation, Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas. Campinas, Brazil, 1995Mohaghegh, S., Arefi, R., Ameri, S., Aminiand, K., Nutter, R., Petroleum reservoir characterization with the aid of artificial neural networks (1996) Journal of Petroleum Sciences and Engineering, 16 (4), pp. 263-274Wang, F., Wang, X.J., Ma, Z.Y., Yan, J.H., Chi, Y., Wei, C.Y., Ni, M.J., Cen, K.F., The research on the estimation for the NOx emissive concentration of the pulverized coal boiler by the flame image processing technique (2002) Fuel, 81 (16), pp. 2113-2120Thaler, M., Grabec, I., Poredos, A., Prediction of energy consumption and risk of excess demand in a distribution system (2005) Physica A: Statistical Mechanics and its Applications, 355 (1), pp. 46-53Chong, A.Z.S., Wilcox, S.J., Ward, J., Prediction of gaseous emissions from a chain grate stoker boiler using neural networks of ARX structure (2001) IEE Proceedings Science, Measurement & Technology, 148 (3), pp. 95-102Coelho, D.K., Roisenberg, M., Freitas Filho, P.J., Jacinto, C.M.C., Risk Assessment of Drilling and Completion Operations in Petroleum Wells Using a Monte Carlo and a Neural Network Approach (2005) Proc. Winter Simulation Conference, , Orlando, USADashevskiy, D., Dubinsky, V., Macpherson, J.D., Application of neural networks for predictive control in drilling dynamics (1999) Proc. SPE Annual Technical Conference and Exhibition, , Houston, USABilgesu, H.I., Tetrick, L.T., Altmis, U., Mohaghegh, S., Ameri, S., A new approah for the prediction of rate of penetration (ROP) values (1997) Proc. SPE Eastern Regional Meeting, , Lexington, USANorgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K., (2000) Neural networks for modeling and control of dynamic systems: A practitioner's handbook, , London, UK: SpringerHaykin, S., (1999) Neural networks: A comprehensive foundation, , Upper Saddle River, USA: Prentice-HallMarquardt, D., An algorithm for least-squares estimations of nonlinear parameters (1963) SIAM Journal on Applied Mathematics, 11 (2), pp. 431-441Siegel, A.F., (1988) Statistics and Data Analysis, , New York, USA: John Wiley & Son

Rigs - Sistema Inteligente Para Auxiliar Na Definição De Sistemas Marítimos De Produção De Petróleo Offshore

[No abstract available]14Franco, K.P.M., (2003) Desenvolvimento de Um Sistema Inteligente Para Auxiliar A Escolha de Sistema Para Produção No Mar, p. 117. , Campinas: Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas,. Tese de MestradoKolodner, J., (1993) Case-Based Reasoning, , Morgan Kaufmann Publishers, Inc. SanMateo, CAPal, S.K., Shiu, S.C.K., (2004) Foundations of Soft Case-Based Reasoning, , New Jersey: Wiley and SonsMendes, J.R.P., Guilherme, I.R., Morooka, C.K., Case based system: Indexing and retrievel with fuzzy hypercube (2001) International 2001, Vancouver. The 9th IFSA World Congress-2001, pp. 818-823Aamodt, A., Plaza, E., Case-based reasoning: Foundational issues, methodological variations, and system approaches (1994) Artificial Intelligence Communications, 7 (1), pp. 39-59. , IOS PressMorooka, C.K., Carvalho, M.D.M., Evaluation of alternatives for offshore petroleum production system in deep and ultradeep water depth (2011) Proceedings of International Conference on Ocean, Offshore and Arctic Engineering, , Rotterdam, The NetherlandsFoster, E., (2007) AnArch: Uma Arquitetura Orientada A Serviços Para Aplicações Ricas Para A Internet, , Rio Claro, Trabalho de conclusão de curso -Universidade Estadual Paulista Júlio de Mesquita FilhoZadeh, L.A., Fuzzy sets (1965) Information and Control, 8, pp. 338-35