Seeing the forest and the trees: Using machine learning to categorize and analyze incident reports for Alberta oil sands operators

Kurian, Daniel; Ma, Yongsheng; Lefsrud, Lianne; Sattari, Fereshteh

doi:10.1016/j.jlp.2020.104069

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…After performing an initial analysis using these ML techniques the overall metrics were not satisfactory, and then we used the smart grid parameter tuning method for finding the best parameters to get optimum performance of the models. 41,42,45 After measuring the predictive power of ML algorithms, we have used RST to explore the patterns behind the accident occurrence. The RST helps to understand the indiscernibility relationships between the given attributes under uncertain conditions even with noisy data.…”

Section: Discussionmentioning

confidence: 99%

“…Balasubramanian and Thangamani 41 ; Kurian et al 21,42 ; Xu et al 43 ; Cakir et al 44 ; Tamascelli et al 5 ). According to the findings, accident prediction models in the oil and gas industry are still in their early stages.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

“…The aim of splitting the data in this way is to decide how the models built operate on new data which was not seen before. 42 We split the data into 80:20 ratios namely, 155 accidents (80%) for training and 39 accidents (20%) for testing. To more robustly compare the outcomes of the prediction models, ten-fold (k = 10) cross-validation was introduced.…”

Section: Deployment Of Classification Algorithmsmentioning

confidence: 99%

“…Several authors have used ML to predict occupational accidents analysis in different sectors, in the aviation industry (Herrema et al 26 ; Burnett and Si 27 ), the mining industry (Rivas et al 22 ; Matias et al 28 ), construction industry (Tixier et al 4 ; Goh and Ubeynarayana 6 ; Chokor et al 29 ; Mistikoglu et al 30 ; Liao et al 31 ; Zhu et al 32 ; Choi et al 33 ), in steel and other mineral extraction industry (Sarkar et al 8,11,16,23 ; Shirali et al 34 ; Yang et al 35 ; Dumakor‐Dupey et al 36 ), railways (Alawad et al 37 ; Gao et al 38 ), autonomous vehicle safety (Wang et al 39 ), petrochemical industry (Mugunthan 40 ; Balasubramanian and Thangamani 41 ; Kurian et al 21,42 ; Xu et al 43 ; Cakir et al 44 ; Tamascelli et al 5 ). According to the findings, accident prediction models in the oil and gas industry are still in their early stages.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

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Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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Summary Recent advancements in machine learning techniques are helping researchers to develop predictive models that assist decision‐makers to get a quick, unbiased overview of the processes. But studies using machine learning approaches in analyzing and classifying the injury narratives of the petroleum industries are still in their early stages due to data unavailability and lack of trust in these models. Comparatively, other industries such as construction, manufacturing, aviation and so forth are using the findings from the predictive models but the use of machine learning techniques in analyzing petroleum industry accident data is not gaining much importance. This study aims to use available accident data from the Indian petroleum industry to develop a classification model for predicting possible outcomes of an accident. The data used in this study comprises 194 accident reports with 20 information attributes collected during the 2016–20 period. Six different machine learning algorithms are used to analyze and classify the possible outcome of the accident. It has been found that the Xgboost algorithm has achieved 95% accuracy following multilayer perceptron with 94% accuracy. The rough set theory is also used to extract the indiscernibility relationship between the given attributes causing accident occurrence. The results indicate that “skill‐based error, supervisory violation, no personal protective equipment usage, and lack of standard operating procedure compliance” have contributed to the majority of the accidents. The findings of this study can be used to assist safety professionals in decision‐making, mitigating the root causes of contributing factors.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

Section: Deployment Of Classification Algorithmsmentioning

confidence: 99%

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

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Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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“…In the oil industry, machine learning techniques were used to predict pressure, volume, and temperature (PVT) properties of crude oil [26,27], crude oil price [28,29], and enhanced oil recovery [30,31]. In oil sands operations, machine learning methods were applied to analyse incident reports and increase process safety [32,33], and predict crude oil production from in situ oil sands extraction [34,35]. To the best of our knowledge, data mining techniques have not been applied to the Canadian oil and gas data warehouse or, more broadly, to any oil and gas data warehouse.…”

Section: Introductionmentioning

confidence: 99%

Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations

Bai²,

2021

Sustainability

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Canada’s in situ oil sands can help meet the global oil demand. Because of the energy-intensive extraction processes, in situ oil sands operations also play a critical role in meeting the global carbon budget. The steam oil ratio (SOR) is an indicator used to measure energy efficiency and assess greenhouse gas (GHG) emissions in the in situ oil sands industry. A low SOR indicates an extraction process that is more energy efficient and less carbon intensive. In this study, we applied machine learning methods for data-driven discovery to a public database⁠, Petrinex⁠, containing operating data from 2015 to 2019 extracted from over 35 million records for 20 in situ oil sands extraction operations. Two unsupervised machine learning methods, including clustering and association rules, showed that the cyclic steam stimulation (CSS) recovery method was less efficient than the steam-assisted gravity drainage (SAGD) recovery method. Chi-square tests showed a statistically significant association between the CSS recovery method and high SOR (p <0.005). Two association rules suggested that the occurrence of non-condensable gas (NCG) co-injection produced a low SOR. Chi-square tests on the two rules identified a statistically significant relationship between gas co-injection and low SOR (p < 0.005). Association rules also indicated that there was no association between the production regions and SORs. For future in situ oil sands development, decision-makers should consider SAGD as the preferred method because it is less carbon intensive. Existing in situ oil sands projects and future development should explore the possibility of NCG co-injection with steam to reduce steam consumption and consequently reduce GHG emissions from the extraction processes.

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Introduction to Machine Learning in the Oil and Gas Industry

Bangert

2021

Machine Learning and Data Science in the Oil and Gas Industry

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Seeing the forest and the trees: Using machine learning to categorize and analyze incident reports for Alberta oil sands operators

Cited by 9 publications

References 11 publications

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations

Introduction to Machine Learning in the Oil and Gas Industry

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