Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes

2021 ◽  
Author(s):  
Joy Ugoyah ◽  
Anita Mary Igbine
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
Health And Safety ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Gas Industry ◽  
Energy Needs ◽  
Safety Risks ◽  
Data Driven Modeling ◽  
Applications Of Ai

Abstract Faster and more accurate decisions are what the Oil and Gas industry needs with the world's fast-evolving energy needs and economy. The area of Artificial intelligence and Data-driven modelling is relatively new and has not found popular application in the industry. AI is an emerging technology that can be used to predict event outcomes and automate anomaly-detection processes. The various applications of AI in different industries were researched into. This paper highlighted important processes that can be improved with the application of Artificial Intelligence through data-driven modelling. It also highlights areas in the various industries where AI intelligence is already being applied and ways it can be improved. AI and data-driven modelling has the potential to improve exploration accuracy, reduce production down-time, reduce cost of maintenance, and reduce health and safety risks. This body of information can serve as a guideline for adopting AI in the oil and gas industry. A trend of industry-tailored intelligence solutions would be more effective in the evolving energy industry.

scholarly journals Predicting the Dynamic Parameters of Multiphase Flow in CFD (Dam-Break Simulation) Using Artificial Intelligence-(Cascading Deployment)

Fluids ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 44 ◽  
Author(s):  
S. Hosseini Boosari
Keyword(s):  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Multiphase Flow ◽  
Fast Track ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Dam Break ◽  
Data Driven ◽  
Time Step ◽  
Gas Industry

Multiphase flow of oil, gas, and water occurs in a reservoir’s underground formation and also within the associated downstream pipeline and structures. Computer simulations of such phenomena are essential in order to achieve the behavior of parameters including but not limited to evolution of phase fractions, temperature, velocity, pressure, and flow regimes. However, within the oil and gas industry, due to the highly complex nature of such phenomena seen in unconventional assets, an accurate and fast calculation of the aforementioned parameters has not been successful using numerical simulation techniques, i.e., computational fluid dynamic (CFD). In this study, a fast-track data-driven method based on artificial intelligence (AI) is designed, applied, and investigated in one of the most well-known multiphase flow problems. This problem is a two-dimensional dam-break that consists of a rectangular tank with the fluid column at the left side of the tank behind the gate. Initially, the gate is opened, which leads to the collapse of the column of fluid and generates a complex flow structure, including water and captured bubbles. The necessary data were obtained from the experience and partially used in our fast-track data-driven model. We built our models using Levenberg Marquardt algorithm in a feed-forward back propagation technique. We combined our model with stochastic optimization in a way that it decreased the absolute error accumulated in following time-steps compared to numerical computation. First, we observed that our models predicted the dynamic behavior of multiphase flow at each time-step with higher speed, and hence lowered the run time when compared to the CFD numerical simulation. To be exact, the computations of our models were more than one hundred times faster than the CFD model, an order of 8 h to minutes using our models. Second, the accuracy of our predictions was within the limit of 10% in cascading condition compared to the numerical simulation. This was acceptable considering its application in underground formations with highly complex fluid flow phenomena. Our models help all engineering aspects of the oil and gas industry from drilling and well design to the future prediction of an efficient production.

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

2021 ◽  
Vol 73 (01) ◽  
pp. 12-13
Author(s):  
Manas Pathak ◽  
Tonya Cosby ◽  
Robert K. Perrons
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Autonomous Systems ◽  
Oil And Gas Industry ◽  
Human Judgment ◽  
Gas Industry ◽  
The Media ◽  
And Function

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

Artificial Intelligence and Robotics in the Oil Industry: Will it Take My Job?

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Human Robot Interaction ◽  
Gas Industry ◽  
Deep Dive ◽  
The Future ◽  
And Robotics

Abstract Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals. This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world. The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take? The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?

Nimrod XV230—reflections on leadership, culture and priorities

2017 ◽  
Vol 57 (2) ◽  
pp. 374
Author(s):  
Martin Anderson
Keyword(s):  
Oil And Gas ◽  
Health And Safety ◽  
Oil And Gas Industry ◽  
Royal Air Force ◽  
Gas Industry ◽  
Leadership Culture ◽  
Key Issues ◽  
The Uk ◽  
Organisational Factors ◽  
Independent Inquiry

On 2 September 2006 a reconnaissance aircraft Royal Air Force Nimrod XV230 suffered a catastrophic mid-air fire on a mission over Afghanistan, leading to the total loss of the aircraft and the death of all 14 service personnel. This paper summarises key issues from an independent inquiry and challenges the oil and gas industry to reflect on these. The author, a Chartered specialist in human and organisational factors, contributed to The Nimrod Review as a Specialist Inspector with the UK Health and Safety Executive.

Status of Data-Driven Methods and their Applications in Oil and Gas Industry

2018 ◽  
Author(s):  
Karthik Balaji ◽  
Minou Rabiei ◽  
Vural Suicmez ◽  
Celal Hakan Canbaz ◽  
Zinyat Agharzeyva ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Gas Industry

scholarly journals Forecasting of Technology Development of the Arctic Hydrocarbon Resources’ Extraction

2020 ◽  
Vol 162 ◽  
pp. 01008
Author(s):  
Tatiana Chvileva
Keyword(s):  
Oil And Gas ◽  
Technology Development ◽  
Arctic Region ◽  
Oil And Gas Industry ◽  
Integrated Approach ◽  
The Arctic ◽  
Gas Industry ◽  
Industrial Systems ◽  
Technological Forecasting ◽  
Energy Needs

The Arctic region has a great potential in development of hydrocarbon resources and can play an important role in meeting future global energy needs. In the presented work the specific features of the Arctic hydrocarbon projects are identified. Key needs of oil and gas industry in technology development within the framework of projects of extraction of hydrocarbon resources in the Arctic are revealed. A critical analysis of technological forecasting methods is presented. Problems and prospects of their use in the conditions of the Arctic zones are established. The need for an integrated approach to forecasting the development of industrial systems of the Arctic zone is justified.

scholarly journals The State of Occupational Health and Safety Management Frameworks (OHSMF) and Occupational Injuries and Accidents in the Ghanaian Oil and Gas Industry: Assessing the Mediating Role of Safety Knowledge

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Suxia Liu ◽  
Edmund Nana Kwame Nkrumah ◽  
Linda Serwah Akoto ◽  
Emmanuel Gyabeng ◽  
Erasmus Nkrumah
Keyword(s):  
Occupational Health ◽  
Oil And Gas ◽  
Safety Management ◽  
Occupational Injuries ◽  
Health And Safety ◽  
Occupational Health And Safety ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Workplace Accidents ◽  
Safety Knowledge

Background. The study examines the mediation effect of safety knowledge in causal the relationship between Occupational Health and Safety Management Frameworks (OHSMF) and occupational injuries and workplace accidents in the Ghanaian Oil and Gas Industry. The study explores different dimensions of occupational health and safety management systems, workplace accidents, and occupational injuries. The study adopted a cross-sectional survey design. A total of 699 respondents through a convenience and purposive sampling technique were selected in three government-owned oil and gas organizations for the study. Correlation, multiple regression analysis, and bootstrapping methods were used for data analysis. The findings of both the regression and correlation analysis indicated that there is a moderately strong negative and significant relationship between Occupational Health and Safety Management Frameworks (OHSMF) and workplace accidents and occupational injuries. Safety knowledge significantly mediates the causal relationship between OHSMF and workplace accidents and injuries. Safety training was found to be a significant predictor of safety knowledge, work-related injuries, and workplace accidents. The negative relationship between OHSMF and workplace accidents and injuries shows that the existing OHSMF are either ineffective or lack the acceptable safety standards to control hazard exposures in the industry. Management must invest in frequent safety training and orientations to improve safety knowledge among workers. The study further recommends government and industry players to extend serious attention towards the promotion and improvement of occupational health and safety management systems in Ghana.

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