Artificial Intelligence Methods Application in Oil Industry

Aung, Zayar; Mikhaylov, Ilya S.; Aung, Ye Thu

doi:10.1109/eiconrus49466.2020.9039330

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Optimization using AI is mainly applied to improve production considering economic components. The input parameters often widely vary, so adaptable algorithms are used to compensate (Aung et al, 2020).…”

Section: Al Currently In the Ogimentioning

confidence: 99%

AI/ML Technology for Water Treatment in Oil and Gas Industry: A Review Paper

Rotan,

Goettel,

Smith

et al. 2024

Preprint

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The opportunities and advantages of implementing Artificial Intelligence (AI) and Machine Learning (ML) to optimize the process control of wastewater treatment in the Oil and Gas industry are discussed in this work. Discussion is centered around five major points: 1) Various types of Oil and Gas wastewater (Sour, Oily, Produced) and industry standards/methods for treatment, including specific case studies and challenges when treating certain wastewater. This section aims at the common types of wastewater in the Oil and Gas industry and how they are generally treated. 2) How AI/ML has been implemented in wastewater treatment in various industries; summary of which AI/ML languages were used along with a brief overview of how they work. This article focused on an understanding of the specific case studies and AI explanations to understand the effectiveness of wastewater treatment using AI/ML. 3) AI that is currently implemented in Oil and Gas such as personal protective equipment (PPE), smartwatches radiofrequency identification etc. This showed that AI/ML already plays a successful role in the Oil and Gas industry and has proven to help optimize many processes and components. Many of these AI/ML implementations are now industry standards and have greatly benefitted the industry. This section demonstrates that the implementation and the use of AI/ML have been proven to be a successful endeavor and that expanding the role of AI/ML would be beneficial. 5) Using the established points from the previous sections the paper outlines why AI/ML is ideal for Oil and Gas wastewater treatment; this argument centers around fit-for-purpose treatment, and the possible uses of the treated water. A determination of the best-suited AI languages, possible implementation scenarios, and how this would shift the way Oil and Gas industries treat and approach wastewater is presented to the reader.

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Section: Al Currently In the Ogimentioning

confidence: 99%

AI/ML Technology for Water Treatment in Oil and Gas Industry: A Review Paper

Rotan,

Goettel,

Smith

et al. 2024

Preprint

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“…Trabulsi (2018) describes AIS as a computerized system that monitors and generates financial reports and data to assist in planning, controlling, and decision-making processes. The implementation of AIS tools and strategies in the oil industry seeks to optimize time management and boost productivity, efficiency, and profitability (Aung et al, 2020;HUSSAIN, 2017).…”

Section: Accounting Information Systems and Time Managementmentioning

confidence: 99%

Time Management as a Critical Success Factor in the Oil Industry of Basra Governorate: An Accounting Information Systems Study

Hussain¹,

Alabdullah²,

Jamal³

et al. 2023

IJSMR

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This study looks into the importance of time management to the oil sector activities in Basra Governorate. The purpose of this study is to investigate how 200 oil companies' (OC) time management strategies and performance are impacted by accounting information systems (AIS). Surveys and interviews were utilized to gather data for this study using a mixed-methods approach. 150 oil companies were questioned using structured questionnaires to determine how they used AIS technologies, time management techniques, and OC performance metrics. To further understand their opinions and experiences about time management and the goal of AIS, industry professionals were interviewed in-depth. Participants were picked from a range of Basra Governorate-based energy companies. The connections between AIS usage, time management techniques, and OC performance were examined by statistical analysis, including regression analysis and correlation testing. The study's results point to a connection between improved time management techniques in hydrocarbon businesses and effective AIS use. The data also shows that better time management techniques have a significant impact on OC performance, leading to an improvement in effectiveness, productivity, and profitability. The results of this study offer 200 oil businesses in the Basra Governorate helpful tips for implementing and utilizing AIS to optimize their time management. This adds to the body of knowledge already available on time management in the oil business and helps the work being done by researchers, policymakers, and industry experts to raise the performance and success of the sector as a whole.

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“…The oil and gas industry has embraced Industry 4.0, leading to various Artificial Intelligence (AI) proposals for decision-making support, such as sedimentary deposit analysis [1]; inspection image analysis [2]; on-shore well aerial image analysis [3]; inspection selection support [4]; pressure loss prediction in drilling columns [5]; oil reservoir porosity and permeability analysis [6]; well drilling process monitoring [7]. An overview of potential uses is presented in [8]. In the context of Machine Learning (ML) applications, there are contributions on pipeline integrity concerning corrosion [9], predictive models for pipeline criticality assessment [10], extraction process applications [11], and other conceptual examples [12].…”

Section: Introductionmentioning

confidence: 99%

A Data-driven Approach for FPSO Electric Power System Modelling

Junior,

Ferreira,

Pessoa

et al. 2024

Preprint

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Industry 4.0 adoption in the oil and gas sector has introduced numerous AI-driven decision-making tools. However, data-driven modeling for oil platform simulators using historical production data is still relatively unexplored. Floating platforms, such as FPSOs, play a critical role in oil production, particularly in Brazil. Electrical power systems design for these platforms typically employs conservative risk measures, and historical data to determine electrical equipment factors is limited. Therefore, this paper presents the FPSO Power Demand Analytics (FPDA) tool for estimating electrical equipment load on oil and gas platforms, aiding daily production improvements, and supporting the design of new FPSOs based on operational history insights. FPDA is an open-source Python tool compatible with multiple platforms and utilizes historical data analytics and machine learning for modeling. Users can generate electrical diagrams and define loads through a configurable power flow model. FPDA consists of three distinct modules: Knowledge Discovery in Databases (KDD) for preliminary data analysis, Machine Learning (ML) for model training and demand forecasting, and Power Flow (PF) for aggregating demand projections and estimating generator/transformer demand. The algorithms were assessed using data from three different FPSOs with varying sampling periods and temporal horizons. Seven ML models were trained per device to predict active power demand based on process variables, and their performance was evaluated using two test scenarios and various statistical measures. The ML algorithms offered precise projections with minimal computational time, while the power flow module delivered consistent results requiring only a few seconds for simulation, making it suitable for planning environments.

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Artificial Intelligence Methods Application in Oil Industry

Cited by 12 publications

References 3 publications

AI/ML Technology for Water Treatment in Oil and Gas Industry: A Review Paper

AI/ML Technology for Water Treatment in Oil and Gas Industry: A Review Paper

Time Management as a Critical Success Factor in the Oil Industry of Basra Governorate: An Accounting Information Systems Study

A Data-driven Approach for FPSO Electric Power System Modelling

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