{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:06:33Z","timestamp":1760148393791,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU FP7 funded project TRIDEC","award":["FP7-258723-TRIDEC","869379"],"award-info":[{"award-number":["FP7-258723-TRIDEC","869379"]}]},{"name":"IlluMINEation project","award":["FP7-258723-TRIDEC","869379"],"award-info":[{"award-number":["FP7-258723-TRIDEC","869379"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, uncertainty arises due to the behavior of the formation being drilled into, which may cause crucial situations at the rig. To overcome such uncertainties, real-time sensor measurements are used to predict, and thus prevent, such crises. In addition, new understandings of the effective events were derived from raw data. In order to avoid the computational overhead of input feature analysis that hinders time-critical prediction, EventTracker sensitivity analysis, an incremental method that can support dimensionality reduction, was applied to real-world data from 1600 features per each of the 4 wells as input and 6 time series per each of the 4 wells as output. The resulting significant input series were then introduced to two classification methods: Random Forest Classifier and Neural Networks. Performance of the EventTracker method was understood correlated with a conventional manual method that incorporated expert knowledge. More importantly, the outcome of a Neural Network Classifier was improved by reducing the number of inputs according to the results of the EventTracker feature selection. Most important of all, the generation of results of the EventTracker method took fractions of milliseconds that left plenty of time before the next bunch of data samples.<\/jats:p>","DOI":"10.3390\/s23094292","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T02:18:34Z","timestamp":1682561914000},"page":"4292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis"],"prefix":"10.3390","volume":"23","author":[{"given":"Siamak","family":"Tavakoli","sequence":"first","affiliation":[{"name":"Computer Science Department, Maharishi International University, Fairfield, IA 52557, USA"}]},{"given":"Stefan","family":"Poslad","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK"}]},{"given":"Rudolf","family":"Fruhwirth","sequence":"additional","affiliation":[{"name":"Thonhauser Data Engineering GmbH, 8700 Leoben, Austria"}]},{"given":"Martin","family":"Winter","sequence":"additional","affiliation":[{"name":"JOANNEUM RESEARCH Forschungsgesellschaft mbH, 8010 Graz, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6913-8046","authenticated-orcid":false,"given":"Herwig","family":"Zeiner","sequence":"additional","affiliation":[{"name":"JOANNEUM RESEARCH Forschungsgesellschaft mbH, 8010 Graz, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","unstructured":"Jandl, B., Winter, M., Fruhwirth, R., Riedel, F., and Zeiner, H. (2012, January 22\u201327). Rig Side Online Drilling Support System for Prediction and Prevention of Upcoming Crises. Proceedings of the EGU General Assembly 2012, Vienna, Austria. EGU2012-9363-1."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coppes, J., and Rudd, N.E. (1976, January 8\u20139). Blowout Prevention is Mistake Prevention. Proceedings of the SPE European Spring Meeting, Amsterdam, The Netherlands.","DOI":"10.2118\/5756-MS"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Christman, S., Kelly, A., Plaisance, M., Kropla, S., Metcalf, J., Robinson, E., and Weddle, C. (1999, January 9\u201311). An Overview of the IADC Deepwater Well Control Guidelines. Proceedings of the SPE\/IADC Drilling Conference, Amsterdam, The Netherlands.","DOI":"10.2118\/52761-MS"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nunes, J.O.L., Bannwart, A.C., and Ribeiro, P.R. (2002, January 8\u201311). Mathematical Modeling of Gas Kicks in Deep Water Scenario. Proceedings of the IADC\/SPE Asia Pacific Drilling Technology, Jakarta, Indonesia.","DOI":"10.2118\/77253-MS"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Freithofnig, H.J., Spoerker, H.F., and Thonhauser, G. (2003, January 20\u201322). Analysis of Hook Load Data to Optimize Ream and Wash Operations. Proceedings of the SPE\/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, United Arab Emirates.","DOI":"10.2118\/85308-MS"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press Inc.","DOI":"10.1201\/9781420050646.ptb6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pritchard, D., Roye, J., and Cunha, J.C. Drilling and Completions, Management and Information, Successful Energy Practices International, LLC. Available online: https:\/\/www.successful-energy.com\/2012\/01\/20\/trends-in-monitoring-how-to-use-real-time-data-effectively\/.","DOI":"10.2118\/0112-0048-JPT"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cayeux, E., Daireaux, B., Dvergsnes, E.W., and S\u00e6levik, G. (2012, January 27\u201329). Early Symptom Detection Based on Real-Time Evaluation of Downhole Conditions: Principles and Results from Several North Sea Drilling Operations. Proceedings of the SPE Intelligent Energy International, Utrecht, The Netherlands.","DOI":"10.2118\/150422-MS"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gundersen, O.E., S\u00f8rmo, F., Aamodt, A., and Skalle, P. (2012, January 22\u201326). A Real-Time Decision Support System for High Cost Oil-Well Drilling Operations. Proceedings of the Twenty-Fourth Conference on Innovative Applications of Artificial Intelligence, Toronto, ON, Canada.","DOI":"10.1609\/aaai.v26i2.18959"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gulsrud, T.O., Bj\u00f8rkevoll, K.S., and Nyb\u00f8, R. (2009). Statistical Method for Detection of Poor Hole Cleaning and Stuck Pipe, Offshore Europe.","DOI":"10.2118\/123374-MS"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Arnaout, A., Alsallakh, B., Fruhwirth, R., Thonhauser, G., Esmael, B., and Prohaska, M. (2012, January 13\u201316). Diagnosing drilling problems using visual analytics of sensors measurements. Proceedings of the 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Graz, Austria.","DOI":"10.1109\/I2MTC.2012.6229708"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Murillo, A., Neuman, J., and Samuel, R. (2009, January 4\u20138). Pipe Sticking Prediction and Avoidance Using Adptive Fuzzy Logic and Neural Network Modeling. Proceedings of the SPE Production and Operations Symposium, Oklahoma City, OK, USA. SPE 120128.","DOI":"10.2118\/120128-MS"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.2118\/127761-PA","article-title":"Probabilistic Modeling for Decision Support in Integrated Operations","volume":"3","author":"Giese","year":"2011","journal-title":"SPE Econ. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"295","DOI":"10.2118\/126017-PA","article-title":"Real-Time Drilling Operations Centers: A History of Functionality and Organizational Purpose-The Second Generation","volume":"26","author":"Booth","year":"2011","journal-title":"SPE Drill. Complet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.neuroimage.2010.04.036","article-title":"Model-based feature construction for multivariate decoding","volume":"56","author":"Brodersen","year":"2010","journal-title":"NeuroImage"},{"key":"ref_16","first-page":"507","article-title":"Feature construction method of combined subdivision surface","volume":"16","author":"He","year":"2010","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_17","first-page":"527","article-title":"Feature construction scheme for efficient intrusion detection system","volume":"26","author":"Kim","year":"2010","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/978-3-642-05177-7_6","article-title":"Explicit feature construction and manipulation for covering rule learning algorithms","volume":"262","author":"Gamberger","year":"2010","journal-title":"Stud. Comput. Intell."},{"key":"ref_19","unstructured":"Buchenneder, K. (2007). Computer Aided Systems Theory\u2014EUROCAST 2007, Springer. Lecture Notes in Computer Science 4739."},{"key":"ref_20","unstructured":"Dias, N.S., Kamrunnahar, M., Mendes, P.M., Schiff, S.J., and Correia, J.H. (2009, January 14\u201317). Variable subset selection for brain-computer interface: PCA-based dimensionality reduction and feature selection. Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing, Porto, Portugal."},{"key":"ref_21","unstructured":"Qi, L., Kambhamettu, C., and Jieping, Y. (2006, January 17\u201322). Integrating Spatial and Discriminant Strength for Feature Selection and Linear Dimensionality Reduction. Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW\u201906), New York, NY, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zaman, S., and Karray, F. (2009, January 22\u201324). Features selection using fuzzy ESVDF for data dimensionality reduction. Proceedings of the 2009 International Conference on Computer Engineering and Technology, Singapore.","DOI":"10.1109\/ICCET.2009.36"},{"key":"ref_23","unstructured":"Hand, D.J., Mannila, H., and Smyth, P. (2001). Principles of Data Mining, MIT Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","unstructured":"Mirkin, B.G. (2012). Clustering for Data Mining: A Data Recovery Approach, Chapman & Hall\/CRC. [2nd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1081\/SAC-120017506","article-title":"Comparing methods for multivariate nonparametric regression","volume":"32","author":"Banks","year":"2003","journal-title":"Commun. Stat. Part B Simul. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1002\/hyp.6734","article-title":"Multi-Method Global Sensitivity Analysis (MMGSA) for modelling floodplain hydrological processes","volume":"22","author":"Cloke","year":"2008","journal-title":"Hydrol. Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1002\/(SICI)1099-1638(199809\/10)14:5<311::AID-QRE156>3.0.CO;2-H","article-title":"Sensitivity analysis of availability estimates to input data characterization using design of experiments","volume":"14","author":"Durkee","year":"1998","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0021-9991(78)90097-9","article-title":"Nonlinear sensitivity analysis of multiparameter model systems","volume":"26","author":"Cukier","year":"1978","journal-title":"J. Comput. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.ijpe.2006.12.027","article-title":"Global sensitivity analysis in inventory management","volume":"108","author":"Borgonovo","year":"2007","journal-title":"Int. J. Prod. Econ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1111\/0272-4332.00040","article-title":"Sensitivity analysis for importance assessment","volume":"22","author":"Saltelli","year":"2002","journal-title":"Risk Anal."},{"key":"ref_32","unstructured":"Isukapalli, S.S. (1999). Uncertainty Analysis of Transport-Transformation Models. [Ph.D. Thesis, New Burnswick Rutgers, The State University of New Jersey]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/S0096-3003(02)00056-5","article-title":"Green\u2019s function and positive solutions for higher-order ODE","volume":"136","author":"Yang","year":"2003","journal-title":"Appl. Math. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/S0096-3003(03)00800-2","article-title":"Numerical solutions of coupled differential equations and initial values using Maple software","volume":"155","author":"Faghihi","year":"2004","journal-title":"Appl. Math. Comput."},{"key":"ref_35","unstructured":"Krzykacz-Hausmann, B. (2001, January 18\u201320). Epistemic Sensitivity Analysis Based on the Concept of Entropy. Proceedings of the SAMO 2001: Third International Symposium on Sensitivity Analysis of Model Output, Madrid, Spain."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1016\/j.ress.2005.11.019","article-title":"An approximate sensitivity analysis of results from complex computer models in the presence of epistemic and aleatory uncertainties","volume":"91","year":"2006","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TKDE.2011.240","article-title":"Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)","volume":"25","author":"Tavakoli","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_38","unstructured":"(2013, August 02). WITSML Standard. Available online: https:\/\/www.energistics.org\/drilling-completions-interventions\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Criminisi, A., Shotton, J., and Konukoglu, E. (2011). Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, Microsoft Research. Technical Report No. MSR-TR-2011-114.","DOI":"10.1561\/9781601985415"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_43","unstructured":"Saffari, A., Leistner, C., Santner, J., Godec, M., and Bischof, H. (October, January 27). Online Random Forests. Proceedings of the 3rd IEEE ICCV Workshop on Online Computer Vision, Kyoto, Japan."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fruhwirth, R.K., Thonhauser, G., and Mathis, W. (2006, January 24\u201327). Hybrid Simulation Using Neural Networks to Predict Drilling Hydraulics in Real Time. Proceedings of the SPE Annual Technical Conference and Exhibition, San Antonio, TX, USA. SPE 103217.","DOI":"10.2118\/103217-MS"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:23:30Z","timestamp":1760124210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,26]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094292"],"URL":"https:\/\/doi.org\/10.3390\/s23094292","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,4,26]]}}}