Artificial Immune Systems For Classification Of Petroleum Well Drilling Operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning. © Springer-Verlag Berlin Heidelberg 2007.4628 LNCS4758Unneland, T., Hauser, M., Real-Time Asset Management: From Vision to Engagement-An Operator's Experience (2005) Proc. SPE Annual Technical Conference and Exhibition, , Dallas, USAYue, 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. 573-612de Castro, L., Timmis, J., (2002) Artificial immune systems: A new computational approach, , Springer, London. UKde Castro, L.N., Von Zuben, F.J., The Clonal Selection Algorithm with Engineering Applications (2000) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '00), Workshop on Artificial Immune Systems and Their Applications, pp. 36-37. , Whitley, L.D, Goldberg, D.E, Cantú-Paz, E, Spector, L, Parmee, I.C, Beyer, H.-G, eds, Morgan Kaufmann, Las Vegas, Nevada, USAde Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Watkins, A., Boggess, L., A new classifier based on resource limited Artificial Immune Systems (2002) Proc. 2002 Congress on Evolutionary Computation (CEC2002), , Honolulu, Hawaii. IEEE Press, New YorkWatkins, A., Timmis, J., Boggess, L., Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm (2004) Genetic Programming and Evolvable Machines, 5 (3), pp. 291-317Watkins, A., Timmis, J., Artificial Immune Recognition System (AIRS): Revisions and Refinements (2002) Proc. of 1st International Conference on Artificial Immune Systems (ICARIS2002), pp. 173-181. , Timmis, J, Bentley, P.J, eds, University of Kent at CanterburyWatkins, A., Timmis, J.: Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, 3239, pp. 427-438. Springer, Heidelberg (2004)Tavares, 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) Proc. of 18th International Congress of Mechanical Engineer.Offshore & Petroleum and Engineering, , Ouro Preto, BrazilSerapião, A.B.S., Tavares, R.M., Mendes, J.R.P., Morooka, C.K., Classificação automática da operação de perfuração de poços de petróleo através de redes neurais (2005) Proc. of VII Brazilian Symposium on Intelligent Automation (SBAI), , São Luís-MA, BrazilSerapião, A.B.S., Tavares, R.M., Mendes, J.R.P., Guilherme, I.R., Classification of Petroleum Wells Drilling Operations Using Support Vectors Machine (SVM) (2006) Proc. of International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA'2006), , IEEE Computer Society, Sydney, AustraliaAtkeson, C.G., Moore, A.W., Schaal, S., Locally weighted learning (1997) Artificial Intelligence Review, 11 (1-5), pp. 11-73Kyllingstad, A., Horpestad, J.L., Klakegg, S., Kristiansen, A., Aadnoy, B.S., Factors Limiting the Quantitative Use of Mud-Logging Data (1993) Proc. of the SPE Asia Pacific Oil and Gas Conference, , SingaporeAda, G.L., Nossal, G.J.V., The Clonal Selection Theory (1987) Scientific American, 257 (2), pp. 50-57Berek, C., Ziegner, M., The Maturation of the Immune Response (1993) Imm. Today, 14 (8), pp. 400-40

Decision-making Tool For Knowledge-based Projects In Offshore Production Systems

The development of an offshore field demands knowledge of many experts to choose the different components of an offshore production system. All the specialized parts of this knowledge are intrinsically related. The aim of this paper is to use Fuzzy Sets and knowledge-based systems to describe and formalize the phases of development of an offshore production system project, in order to share and to manage the required knowledge for carrying out a project, while at the same time proposing alternatives for the oil field configuration. © Springer-Verlag Berlin Heidelberg 2012.7637 LNAI692701et al.,Sociedad Colombiana de Computacion (SCo2),Universidad de Caldas,Universidad Nacional de Colombia,Universidad Tecnologica de Bolivar en Cartagena,Universidad Tecnologica de PereiraPublisher: Springer VerlagWang, X., Chan, C.W., Hamilton, H.J., Design of knowledge-based systems with the ontology-domain-system approach (2002) 14th International Conference on Software Engineering and Knowledge Engineering, pp. 233-236. , Ischia, Italy, July 15-19Bargach, S., Martin, C.A., Smith, R.G., Managing drilling knowledge for improved efficiency and reduced operational risk (2001) SPE/IADC Drilling Conference, p. 67821Chao, K.M., Smith, P., Hills, W., Florida-James, B., Norman, P., Knowledge sharing and reuse for engineering design integration (1998) Expert Systems with App., 14 (3), pp. 399-408Soh, C.K., Soh, A.K., An approach to automate the design of fixed offshore platforms (1993) Computers & Structures, 46 (4), pp. 221-254Franco, K.P.M., Morooka, C.K., Mendes, J.R.P., Guilherme, I.R., Desenvolvimento de um sistema inteligente para auxiliar a escolha de sistema para produção no mar (2002) 2o Congresso Brasileiro de P&D em Petróleo & Gás, Rio de Janeiro, , in portugueseSerapião, A.B.S., Guilherme, I.R., Morooka, C.K., Mendes, J.R.P., Franco, K.P.M., Um sistema inteligente para escolha dentre alternativas de sistemas marítimos de produção (2003) VI Simpósio Brasileiro de Inte Ligência Artificial, Bauru-SP, , Brazil In PortugueseKlir, G.J., Yuan, B., (1995) Fuzzy Sets and Fuzzy Logic-Theory and Applications, , 1st edn Prentice-Hal

The Development And Application Of A Software To Assist The Drilling Engineer During Well Control Operations In Deep And Ultra Deep Waters

This paper presents the development and application of an integrated computational tool to assist the drilling engineer regarding prevention and detection of a kick and its circulation out of the well when operating in deepwater and ultra deepwater drilling scenarios. This tool, called UNIKICK, provides the drilling personnel with information and several computational resources covering many aspects related to well control in those drilling scenarios. The most important features provided by the system are as follows: (a) link to databases containing regulations, standards and operational procedures to be accessed from the rig site; (b) a module to display well control information and calculations such as kick tolerance, swabbing pressure, tripping and riser safety margin; (c) a killsheet which is calculated and displayed, automatically; and (d) an interface to show graphically the expected pressure behavior at selected points inside the well and fluids production during the kick removal process. Through the interface, the user enters the data necessary to the calculation of the killsheet and to the simulation of the pressure behavior inside the wellbore and the flow rates of the fluids from the well, using a built-in kick simulator. This simulator was developed specifically for the system and accounts for the two-phase nature of the mixture of gas and drilling fluid and for the peculiarities of an ultra deepwater well/rig equipment configuration. The interface also displays the output data generated by the simulator in a graphical format together with animation capability for the gas that is circulated out the well. To illustrate the usage of the system, the paper presents the results for three horizontal well control cases ranging from 1000 to 3000 meters of water depth. The paper also suggests that the system can also be used as a very powerful instructional tool for training purposes.10951100Sotomayor, G.P.G., (1997) Desenvolvimento de um Sistema Computacional para Suporte ao Controle de Poços em Águas Profundas, , M.Sc. thesis, in Portuguese, Universidade Estadual de Campinas (UNICAMP), Campinas (SP), BrazilNunes, J.O.L., Bannwart, A.C., Ribeiro, P.R., Mathematical Modeling of Gas Kicks in Deep Water Scenario (2002) IADC/SPE Asia Pacific Drilling Technology, , paper SPE 77253, Jakarta, Indonesia, Sept. 9-1

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