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

Hoffimann, Júlio; Mao, Youli; Wesley, Avinash; Taylor, Aimee

doi:10.2118/191505-ms

Cited by 23 publications

(8 citation statements)

References 23 publications

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“…Developed in 2015, TensorFlow has been used in text-based applications, voice search, image recognition, regression analysis, and classification predictions [41], [58]- [61]. In drilling engineering, TensorFlow has been applied in sequence mining and pattern analysis [62], and permanent downhole gauge data interpretations [63] with promising results. Thus, this study reports the application of TensorFlow in predicting the rheological properties of recirculating water-based drilling mud for the first time.…”

Section: Tensorflowmentioning

confidence: 99%

Prediction of Rheological Properties of Recirculating Water-Based Drilling Mud in Geothermal Exploration using Artificial Neural Networks with Tensor Flow

Mengich

Kabugu²,

Ondiaka

2022

EJENERGY

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Pipe sticking in drilling operations occurs by mechanical and differential forces caused by the loss in circulation of drilling fluids or muds. Rheological properties of recirculating drilling muds (RDMs) determine the drilling hydraulics and hole cleaning effectiveness. Uncertainty in real-time data on the rheological properties of RDMs challenges estimation of potential pipe sticking problems, a problem that can be improved using machine learning methods. This study reports first-time application of a supervised artificial neural networks (ANN) algorithm with TensorFlow to estimate plastic viscosity (PV), apparent viscosity (AV), yield point (YP), flow consistency index (k), and flow behavior index (n), five rheological properties of recirculating water-based drilling mud (R-WBDM). Model input variables were density (MD), marsh funnel viscosity (MFV), and percent solids content (% S) of R-WBDM. Model performance was tested using the root mean square error (RMSE) and coefficient of determination (R2) methods. Sensitivity analysis demonstrated the strength of each predictor variable. Five optimal models showed good generalization capability in estimating the n, k, PV, AV, and YP with RMSE of 0.022, 0.270, 7.890, 8.870, and 10.149, and R2 of 0.995, 0.956, 0.756, 0.724, and 0.701 in a similar order, respectively. High sensitivity was observed in PV, AV, YP, and k models, and n-model, to changes in MFV (0.615) and % S (0.067), respectively. Results show the potential use of ANN with TensorFlow to support decision-making in geothermal drilling engineering on pipe sticking problems using easy-to-determine physical properties as predictor variables.

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Section: Tensorflowmentioning

confidence: 99%

Prediction of Rheological Properties of Recirculating Water-Based Drilling Mud in Geothermal Exploration using Artificial Neural Networks with Tensor Flow

Mengich

Kabugu²,

Ondiaka

2022

EJENERGY

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“…Hoffimann et al [17] proposed retrieving data in drilling reports and classifying them into three classes to accomplish accident identification and operation optimization. Some of the challenges were unfinished sentences, technical symbols, and abbreviations.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed works by [7] and [17] used the LSTM network to resolve their problems. However, in our work, using a LSTM network would not be interesting since we do not have a large amount of labeled data, as will be detailed in Section IV-A.…”

Section: Related Workmentioning

confidence: 99%

A Text Classification Methodology to Assist a Large Technical Support System

Ohata¹,

Mattos

Gomes³

et al. 2022

IEEE Access

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Text-based tools for reporting technical issues and receiving support are widespread in commercial applications, such as customer services and internal corporate communication. Past issues recorded in such systems may provide valuable knowledge for better handling future interactions. Nevertheless, the predominance of short messages and the presence of specific domain subjects constitute additional challenges. In this work, we aim to build an assistant for a system operating in a large company that provides asynchronous services for technical support. It is known that some repetitive technical issues can be handled with simple standard messages, named templates. Thus, we propose a modular pipeline based on natural language processing and machine learning algorithms to enable raw text processing, feature extraction, and supervised learning to recommend suitable templates from a given textual description of the incoming issue. In a real-world scenario, the proposed pipeline achieved an average accuracy of 72.7%, a promising result for a setup with 9 classes and few labeled training instances. Moreover, a post hoc analysis shows how our methodology is able to correctly identify the words more closely related to the corresponding templates. INDEX TERMS natural language processing, text classification, technical support, automated assistantThis article has been accepted for publication in IEEE Access.

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“…Bhandari et al [32] reported that a Bayesian network could also usefully assist with well blow-out predictions in offshore fields. Hoffiman et al [33] applied various deep learning algorithms to data extracted from drilling reports to characterize different operational sections of wellbores. Based on their results, the LSTM network showed a more reliable performance for that purpose compared to ANN and CNN algorithms.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets

Mardanirad

Wood

Zakeri³

2021

SN Appl. Sci.

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In this paper, we present how precise deep learning algorithms can distinguish loss circulation severities in oil drilling operations. Lost circulation is one of the costliest downhole problem encountered during oil and gas well construction. Applying artificial intelligence can help drilling teams to be forewarned of pending lost circulation events and thereby mitigate their consequences. Data-driven methods are traditionally employed for fluid loss complexity quantification but are not able to achieve reliable predictions for field cases with large quantities of data. This paper attempts to investigate the performance of deep learning (DL) approach in classification the types of fluid loss from a very large field dataset. Three DL classification models are evaluated: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Five fluid-loss classes are considered: No Loss, Seepage, Partial, Severe, and Complete Loss. 20 wells drilled into the giant Azadegan oil field (Iran) provide 65,376 data records are used to predict the fluid loss classes. The results obtained, based on multiple statistical performance measures, identify the CNN model as achieving superior performance (98% accuracy) compared to the LSTM and GRU models (94% accuracy). Confusion matrices provide further insight to the prediction accuracies achieved. The three DL models evaluated were all able to classify different types of lost circulation events with reasonable prediction accuracy. Future work is required to evaluate the performance of the DL approach proposed with additional large datasets. The proposed method helps drilling teams deal with lost circulation events efficiently. Article Highlights Three deep learning models classify fluid loss severity in an oil field carbonate reservoir. Deep learning algorithms advance machine learning a large resource dataset with 65,376 data records. Convolution neural network outperformed other deep learning methods.

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

Cited by 23 publications

References 23 publications

Prediction of Rheological Properties of Recirculating Water-Based Drilling Mud in Geothermal Exploration using Artificial Neural Networks with Tensor Flow

Prediction of Rheological Properties of Recirculating Water-Based Drilling Mud in Geothermal Exploration using Artificial Neural Networks with Tensor Flow

A Text Classification Methodology to Assist a Large Technical Support System

The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets

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