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Application of machine learning and artificial intelligence in oil and gas industry

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Petroleum Research 2021年第4期6卷 379-391页

作者： Anirbid Sircar Kriti Yadav Kamakshi Rayavarapu Namrata Bist Hemangi Oza Centre of Excellence for Geothermal Energy Pandit Deendayal Petroleum UniversityGandhinagarGujaratIndia

oil and gas industries are facing several challenges and issues in data processing and *** amount of data bank is generated with various techniques and *** proper technical analysis of this database is to be carried out to improve performance of oil and gas *** paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry *** also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and interpretation in different sectors of upstream oil and gas *** achievements and developments promise the benefits of machine learning and artificial intelligence techniques towards large data storage capabilities and high efficiency of numerical *** this paper a summary of various researchers work on machine learning and artificial intelligence applications and limitations is showcased for upstream and sectors of oil and gas *** existence of this extensive intelligent system could really eliminate the risk factor and cost of *** development and progress using this emerging technologies have become smart and makes the judgement procedure easy and *** study is useful to access intelligence of different machine learning methods to declare its application for distinct task in oil and gas sector.

关键词： Artificial intelligence Machine learning Upstream oil and gas industry Petroleum exploration

Risk Matrix as a Tool for Risk Analysis in Underwater Operations in the oil and gas industry

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Journal of Environmental Protection 2022年第11期13卷 856-869页

作者： John A. Jia Ify L. Nwaogazie Brilliance O. Anyanwu Centre for Occupational Health Safety and Environment University of Port Harcourt Choba Nigeria Department of Environmental Health Sciences University of Sharjah Sharjah UAE

The study is a cross-sectional design assessment of the likelihood, frequency and severity of hazards associated with underwater operations in the Niger Delta. Five oil and gas companies were used for this study selected by a purposive method given that they had the highest number of workers involved in underwater operations. A sample size of 418 was computed to which the questionnaires were administered with response rate of 95.93%. Data analyses were carried out to cover descriptive statistics, analysis of variance and Pearsonal correlation coefficients. The 4 by 4 risk assessment matrix for the likelihood and consequences showed that 8 out of 20 underwater hazards were categorized as having very high risk according to their risk ratings. The eight hazards categorized based on their risk IDs were H01, H03, H04, H08, H10, H11, H12, and H15. The 4 by 4 risk matrix for frequency and consequences revealed that two hazards (Piracy & bandit attack/kidnapping (H01) and Other main vessels/heavy object dropping or falling load/collision (H08)) were identified to be of very high risk.

关键词： Risk Matrix Risk Analysis Hazards Risks Underwater Operations oil and gas industry

Contract Labor and the Right to Freedom of Association in the oil and gas industry in Nigeria

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International Relations and Diplomacy 2014年第9期2卷 623-638页

作者： Rosemary Danesi University of Lagos Lagos Nigeria

Contract labor is one of several terms, which are widely used to describe work arrangements, which do not fall within the traditional definition of employment. Contracting out services and getting employees through contractors have become widespread practice by many employers because it helps them to cut cost since these contractors could often supply these workers more cheaply and efficiently. Labor rights and standards are sometimes seen as sacrificial lambs on the altar of competitive edge by companies due to the perception that they constitute ＂costs＂ which if eliminated or reduced to the barest minimum, will impact positively on the ability of companies to compete favorably in the global market and consequent improvement of their balance sheets, to the joy of shareholders. Contract work is increasingly becoming the norm in Nigeria＇s oil and gas industry as companies undergo restructuring, concentration on core activities and modifications in work organization and technology. These factors certainly affect traditional employment relations and the exercise of freedom of association and collective bargaining rights inherent in them. Contract workers now make up about 60% of the workforce in the industry and they are denied the right to organize into Unions. The denial of this right has resulted in exploitation of these workers. This paper seeks to examine the adequacy of labor law governing trade unionism in Nigeria in ensuring the right of contract workers to freedom of association, as well as its conformity to international labor standards. It is argued that Nigerian labor laws need to be enforced to ensure that this category of workers benefits from this right. In addition a proposal is made for a law reform which will define contract labor and provide a legal framework for the regulation and protection of contract workers in Nigeria in order to guarantee their right to unionize and enable them to improve their rights at work.

关键词： contract labor oil and gas industry Nigeria labor law

Geographical Space Distribution of China's oil and gas industry:Characteristics and Drivers

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Journal of Resources and Ecology 2014年第1期5卷 68-73页

作者：胡健焦兵西安财经学院资源环境与区域经济研究中心西安710100

Here, the geographical space distribution of the oil and gas industry in China is comprehensively investigated using the overal Moran’s I index and local Moran’s I index. We found that China’s oil and gas industry development from 2000 to 2010 has a differentiated geographical space distribution upstream （extractive industry） but not downstream （reifning industry）. To analyze upstream and downstream states a spatial econometrics model （SEM） was used to identify inlfuential factors resulting from the spatial concentration of the oil and gas industry. An external effect is the key factor promoting the spatial concentration of the upstream industry in China;governmental economic policy is another important factor.

关键词： oil and gas industry resource endowment external effect geographical space distribution

Risk perception and safety analysis on petroleum production system of three gas fields in Bangladesh

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Journal of Safety Science and Resilience 2022年第4期3卷 362-371页

作者： Md.Numan Hossain M.Farhad Howladar Department of Petroleum&Mining Engineering Shahjalal University of Science&TechnologySylhet 3114Bangladesh

Accidents are common in the petroleum *** risk of accidents can be easily minimized by understand-ing the harm early in the production *** study presents a perception-based risk and safety analysis of petroleum production *** were collected from three fields operated by Sylhet gas Fields Limited in *** Statistical Package for the Social Sciences(SPSS)software was used to analyze the *** results were then subjected to a frequency analysis,an analysis of variance(ANOVA),and a reliability *** frequency analysis indicated the overall safety situation,and the ANOVA models and reliability analysis sub-stantiated the results.A chi-squared test indicated the association between the *** outcomes of the risk matrix indicated various risk levels,such as low,moderate,and *** to the implicit risks,necessary measures were recommended for the industry’s future.

关键词： oil and gas industry Perceived risk Risk matrix Qualitative data Hazard identification

Fuzzy logic applied to value of information assessment in oil and gas projects

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Petroleum Science 2019年第5期16卷 1208-1220页

作者： Martin Vilela Gbenga Oluyemi Andrei Petrovski School of Engineering Robert Gordon UniversityGarthdee RoadAberdeen AB107QBScotlandUK School of Computing Robert Gordon UniversityGarthdee RoadAberdeen AB107QBScotlandUK

The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil *** classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface ***,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different *** classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new *** logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the *** this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of *** results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data *** this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the *** implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.

关键词： Value of information Fuzzy logic Uncertainty and risk management oil and gas industry

Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

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Energy and AI 2021年第1期3卷 73-82页

作者： Dmitry Koroteev Zeljko Tekic Skolkovo Institute of Science and Technology Russian Federation Digital Petroleum LLC Russian Federation

We analyze how artificial intelligence changes a significant part of the energy sector,the oil and gas *** focus on the upstream segment as the most capital-intensive part of oil and gas and the segment of enormous uncertainties to *** on the analysis of AI application possibilities and the review of existing applica-tions,we outline the most recent trends in developing AI-based tools and identify their effects on accelerating and de-risking processes in the *** investigate AI approaches and algorithms,as well as the role and availability of data in the ***,we discuss the main non-technical challenges that prevent the in-tensive application of artificial intelligence in the oil and gas industry,related to data,people,and new forms of *** also outline three possible scenarios of how artificial intelligence will develop in the oil and gas industry and how it may change it in the future(in 5,10,and 20 years).

关键词： Artificial Intelligence Digital transformation oil and gas industry Upstream Innovation

Review of application of artificial intelligence techniques in petroleum operations

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Petroleum Research 2023年第2期8卷 167-182页

作者： Saeed Bahaloo Masoud Mehrizadeh Adel Najafi-Marghmaleki Department of Petroleum Engineering Amirkabir University of TechnologyTehranIran Department of Petroleum Engineering School of Science and EngineeringKhazar UniversityBakuAzerbaijan Department of Petroleum Engineering Ahwaz Faculty of PetroleumPetroleum University of TechnologyAhwazIran

In the last few years,the use of artificial intelligence(AI)and machine learning(ML)techniques have received considerable notice as trending technologies in the petroleum *** utilization of new tools and modern technologies creates huge volumes of structured and un-structured *** and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of *** difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making *** research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies,drilling and production *** challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been ***,possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.

关键词： Artificial intelligence Machine learning Upstream operation oil and gas industry Petroleum systems Decision-making

A minimalist approach for detecting sensor abnormality in oil and gas platforms

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Petroleum Research 2022年第2期7卷 177-185页

作者： Pauline Wong W.K.Wong Filbert H.Juwono Lenin Gopal Mohd Amaluddin Yusoff Curtin Malaysia Digital Innovation(CMDI) Curtin University Malaysia98009MiriSarawakMalaysia Department of Electrical and Computer Engineering Curtin University Malaysia98009MiriSarawakMalaysia

Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation *** complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the *** ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of ***,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor *** this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM *** the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and *** histogram of residuals can be used for sensor abnormality *** general,sensor states can be monitored by observing the shifting of the mean in the histogram of *** sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition *** method is compact and would be relevant to monitor irregularity of the sensors.

关键词： Sensor fault detection Multistage turbine air compressor Multiple linear regression Neural network oil and gas industry