Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations

Osarogiagbon, Augustine Uhunoma; Khan, Faisal; Venkatesan, R.; Gillard, P.

doi:10.1016/j.psep.2020.09.038

Cited by 77 publications

(24 citation statements)

References 68 publications

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“…Many research work have focused on the applications of AI technologies within field development, for activities such as drilling, reservoir engineering and infrastrucutre. Machine learning models and its hybrids reveal successful applications in drilling for prediction of optimal mud properties and drilling parameters to improve safety, drilling efficiency and cost effectiveness [13][14][15][16][17]. Similarly in reservoir engineering and infrastructure, ML and its hybrids are used for estimation and optimization purposes in activities such as estimating dew point pressure and optimizing waterflooding which helps to maximize hydrocarbon production, optimize oil production and maximize finanacial profits [2,18,19].…”

Section: Ai In Oil and Gas Upstreammentioning

confidence: 99%

“…Third challenge is the open collaboration among all echelons, which is a challenge due to the lack of open data source and cross-company, cross-border data sharing. Osarogiagbon et al (2021) [13] pointed to safety challenges in the area of ML algorithms being applied for dangerous events in drilling operations. Apart from a lack of publicly available datasets, a lack of customized deep learning algorithms primarily in the field of drilling activity was also observed.…”

Section: Challenges Of Implementing Ai In Oil and Gas Supply Chainmentioning

confidence: 99%

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Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

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The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PDF

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Section: Ai In Oil and Gas Upstreammentioning

confidence: 99%

Section: Challenges Of Implementing Ai In Oil and Gas Supply Chainmentioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

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“…Attempts to mitigate, early detect, or prevent such events using machine learning have been proposed. A recent survey showed that deep learning, support vector machine and random forest had lately become more popular in the application of hazard prediction [50]. Mamudu et al [43], [44] developed hybrid models based on neural network and Bayesian network algorithms that not only served as a risk monitoring system but also as product optimization.…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol¹,

Tung

Elyan

2021

IEEE Access

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A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders' concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.

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“…The robustness of DL algorithms over conventional ML networks has been identified in many studies. Osarogiagbon et al [56] illustrated the performance curve of DL algorithms in comparison to conventional ML and neural networks according to the amount of data processed. Figure 4 shows schematically that for studies involving large datasets, DL algorithms tend to outperform ML algorithms.…”

Section: Deep Learning Theorymentioning

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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Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations

Cited by 77 publications

References 68 publications

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

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

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