Pattern Analysis in Drilling Reports using Optimum-Path Forest

Well drilling monitoring is an essential task to prevent faults, save resources, and take care of environmental and eco-planning businesses. During drilling, it is required that staff fill out a log to keep track of the activities that are currently occurring. With such data analyzed and processed, it is possible to learn how to prevent faults and take corrective actions in realtime. However, the most important information is usually stored in a free-text format, thus complicating the task of automated text mining. In this work, we introduce the Optimum-Path Forest (OPF) for sentence classification in drilling reports and compare its results against some state-of-art results. We show that OPF combined with text-based features are a compelling source to learn patterns in drilling reports.

[1]  João Paulo Papa,et al.  Supervised pattern classification based on optimum‐path forest , 2009, Int. J. Imaging Syst. Technol..

[2]  S. Dumais.  Latent Semantic Analysis. , 2005 .

[3]  Stephen E. Robertson,et al.  A probabilistic model of information retrieval: development and comparative experiments - Part 2 , 2000, Inf. Process. Manag..

[4]  M. Antoniak,et al.  Natural Language Processing Techniques on Oil and Gas Drilling Data , 2016 .

[5]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[6]  Avinash Wesley,et al.  Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing , 2017, Day 3 Wed, September 26, 2018.

[7]  Satyam Priyadarshy,et al.  Framework for Prediction of NPT causes using Unstructured Reports , 2017 .

[8]  Stephen Rassenfoss.  Mining Daily Driller’s Reports Looking for Telling Patterns , 2015 .

[9]  Frederick Jelinek,et al.  Interpolated estimation of Markov source parameters from sparse data , 1980 .

[10]  João Paulo Papa,et al.  Efficient supervised optimum-path forest classification for large datasets , 2012, Pattern Recognit..

[11]  Mohamed Sidahmed,et al.  Augmenting Operations Monitoring by Mining Unstructured Drilling Reports , 2015 .

[12]  Luciana S. Buriol,et al.  A study on the use of stemming for monolingual ad-hoc Portuguese information retrieval , 2007 .

[13]  R. K. Fruhwirth,et al.  A hybrid multiple classifier system for recognizing usual and unusual drilling events , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[14]  Michel Couprie,et al.  Some links between extremum spanning forests, watersheds and min-cuts , 2010, Image Vis. Comput..

[15]  Ivan Rizzo Guilherme,et al.  An Ontology Based for Drilling Report Classification , 2006, MICAI.

[16]  João Paulo Papa,et al.  Improving semi-supervised learning through optimum connectivity , 2016, Pattern Recognit..

[17]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[18]  João Paulo Papa,et al.  Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques , 2011, Eng. Appl. Artif. Intell..

[19]  João Paulo Papa,et al.  Fast Petroleum Well Drilling Monitoring Through Optimum-Path Forest , 2010, J. Next Gener. Inf. Technol..

[20]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[21]  Alexandre X. Falcão,et al.  Motion segmentation and activity representation in crowds , 2009 .

[22]  Peter Wiemer-Hastings,et al.  Latent semantic analysis , 2004, Annu. Rev. Inf. Sci. Technol..

[23]  Colin Dawson,et al.  From a Daily Drilling Report to a Data and Performance Management Tool , 2014 .

[24]  João Paulo Papa,et al.  Optimum-Path Forest based on k-connectivity: Theory and applications , 2017, Pattern Recognit. Lett..