Machine learning in oil and gas; a SWOT analysis approach

Hajizadeh, Yasin

doi:10.1016/j.petrol.2019.01.113

Cited by 92 publications

(38 citation statements)

References 2 publications

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“…Many previous studies have used the SWOT analysis as a screening tool in their research to carry out strategic planning. It seems that SWOT analysis is expanding over time into a widening field of applications, such as implementation of Industry 4.0 [47,51], renewable energy schemes [48,52], water management [53], strategies for economic sustainability [54], oil and gas industry [55], waste management [56], and solar power development [57]. One of the major drawbacks of this technique, however, is that the influence of each criterion in decision-making cannot be gauged quantitatively.…”

Section: Literature Surveymentioning

confidence: 99%

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

et al. 2020

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The emergence of Industry 4.0, also referred to as the fourth industrial revolution, has entirely transformed how the industry or business functions and evolves. It can be attributed to its broadening focus on automation, decentralization, system integration, cyber-physical systems, etc. Its implementation promises numerous benefits in terms of higher productivity, greater volatility, better control and streamlining of processes, accelerated enterprise growth, sustainable development, etc. Despite the worldwide recognition and realization of Industry 4.0, its holistic adoption is constrained by the requirements of specific skills among the workforce. The personnel are expected to acquire adaptive thinking, cognitive and computational skills, predominantly in the area of information technology, data analytics, etc. Thus, the universities that laid the foundation for future talents or trends in society have to adapt and modernize the existing programs, facilities, and infrastructure. This reshaping of higher education in consonance with the vision of Industry 4.0 possesses its opportunities and challenges. There are, of course, a multitude of factors involved and they need a reasonable assessment to strategically plan this metamorphosis. Therefore, this work aims to explore and analyze the different factors that influence the progression and enactment of Industry 4.0 in universities for sustainable education. For this purpose, a systematic approach based on a questionnaire as well as a SWOT (strengths (S), weaknesses (W), opportunities (O), and threats (T)) integrated with the analytic hierarchy process (AHP) is adopted. The questionnaires are administered to university employees and students (or stakeholders) to assess their viewpoint, as well as to estimate the priority values for individual factors to be included in SWOT. The AHP is implemented to quantify the different factors in terms of weights using a pairwise comparison matrix. Finally, the SWOT matrix is established depending on the questionnaire assessment and the AHP weights to figure out stakeholders’ perspectives, in addition to the needed strategic scheme. The SWOT implementation of this research proposes an aggressive approach for universities, where they must make full use of their strengths to take advantage of the emerging opportunities in Industry 4.0. The results also indicate that there are fundamental requirements for universities in Industry 4.0, including effective financial planning, skilled staff, increased industrial partnerships, advanced infrastructure, revised curricula, and insightful workshops. This investigation undoubtedly underlines the importance of practical expertise and the implementation of digital technologies at the university level to empower novices with the requisite skills and a competitive advantage for Industry 4.0.

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Section: Literature Surveymentioning

confidence: 99%

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

et al. 2020

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“…ML experimentation refers to efforts focused on observing the problem; obtaining, exploring and preparing the data; selecting the algorithm; and model validation [ 69 , 70 ]. Operationalization of ML refers to the process of implementing models, consuming, and monitoring results, in an efficient and measurable way [ 69 ]. Figure 9 shows the ML experimentation steps used in the development of this work.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

Nascimento

Maitelli

et al. 2021

Sensors

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In sucker-rod pumping wells, due to the lack of an early diagnosis of operating condition or sensor faults, several problems can go unnoticed. These problems can increase downtime and production loss. In these wells, the diagnosis of operation conditions is carried out through downhole dynamometer cards, via pre-established patterns, with human visual effort in the operation centers. Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification task and best practices for solving the problem. In the search for answers to these questions, this work carried out sixty tests with more than 50,000 dynamometer cards from 38 wells in the Mossoró, RN, Brazil. In addition, it presented test results for three algorithms (decision tree, random forest and XGBoost), three descriptors (Fourier, wavelet and card load values), as well as pipelines provided by automated machine learning. Tests with and without the tuning of hypermeters, different levels of dataset balancing and various evaluation metrics were evaluated. The research shows that it is possible to detect sensor failures from dynamometer cards. Of the results that will be presented, 75% of the tests had an accuracy above 92% and the maximum accuracy was 99.84%.

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“…However, for the oil and gas industry, the use of such technologies has significantly increased in recent years [11][12][13][14][15][16][17][18]. An important task of the development of the oil and gas industry in the coming years is to increase the efficiency of producing oil and gas and drilling of a well and the main impetus to the introduction of methods of ML was the fall in oil prices.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors Evaluation for Monitoring of Well Drilling

Islamov¹,

Grigoriev²,

Белоглазов³

et al. 2021

Preprint

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Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work is describing a comparison of machine learning algorithms for anomaly detection during well drilling. Tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we use historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. Experiential results illustrated that a model based on gradient boosting can classify the complications in the drilling process best of all.

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Machine learning in oil and gas; a SWOT analysis approach

Cited by 92 publications

References 2 publications

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

Risk Factors Evaluation for Monitoring of Well Drilling

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