Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing

Drilling activities in the oil and gas industry have been reported over decades for thousands of wells on a daily basis, yet the analysis of this text at large-scale for information retrieval, sequence mining, and pattern analysis is very challenging. Drilling reports contain interpretations written by drillers from noting measurements in downhole sensors and surface equipment, and can be used for operation optimization and accident mitigation. In this initial work, a methodology is proposed for automatic classification of sentences written in drilling reports into three relevant labels (EVENT, SYMPTOM and ACTION) for hundreds of wells in an actual field. Some of the main challenges in the text corpus were overcome, which include the high frequency of technical symbols, mistyping/abbreviation of technical terms, and the presence of incomplete sentences in the drilling reports. We obtain state-of-the-art classification accuracy within this technical language and illustrate advanced queries enabled by the tool.

[1]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[2]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[3]  Lukás Burget,et al.  Empirical Evaluation and Combination of Advanced Language Modeling Techniques , 2011, INTERSPEECH.

[4]  Claire Cardie,et al.  Opinion Mining with Deep Recurrent Neural Networks , 2014, EMNLP.

[5]  Holger Schwenk,et al.  Continuous space language models , 2007, Comput. Speech Lang..

[6]  Aapo Hyvärinen,et al.  Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics , 2012, J. Mach. Learn. Res..

[7]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[8]  Jimmy J. Lin,et al.  Quantitative evaluation of passage retrieval algorithms for question answering , 2003, SIGIR.

[9]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[10]  Volkmar Frinken,et al.  A Novel Word Spotting Method Based on Recurrent Neural Networks , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Li Fei-Fei,et al.  DenseCap: Fully Convolutional Localization Networks for Dense Captioning , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[13]  Geoffrey E. Hinton,et al.  Generating Text with Recurrent Neural Networks , 2011, ICML.

[14]  Gonzalo Navarro,et al.  A guided tour to approximate string matching , 2001, CSUR.

[15]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[18]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[19]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[20]  Charles L. A. Clarke,et al.  Information Retrieval - Implementing and Evaluating Search Engines , 2010 .

[21]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[22]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[23]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Ching Y. Suen,et al.  n-Gram Statistics for Natural Language Understanding and Text Processing , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  W. Bruce Croft,et al.  The History of Information Retrieval Research , 2012, Proceedings of the IEEE.