Johan Sverdrup: The Digital Flagship

Larsen, Paal Frode; Tønnessen, Tor; Schuchert, Florian; Khamassi, Ahmed; Jarraya, Haithem; Aarrestad, Henriette Dorthea; Imsland, Vegard; Lossius, Vibeke

doi:10.4043/30477-ms

Cited by 9 publications

(1 citation statement)

References 6 publications

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“…This not only saved significant manual effort but also led to improved reservoir characterization, enabling better-informed decision making regarding drilling and production strategies. Equinor, a leading energy company, applied ML methods for real-time production optimization in the Johan Sverdrup oilfield [214]. By analyzing complex datasets from sensors and production processes, ML algorithms provided actionable insights to operators, enabling them to fine-tune production parameters in real time.…”

Section: Supervisedmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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Section: Supervisedmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers

Perno

Hvam

Haug

2022

Computers in Industry

143

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Since the introduction of the concept of "digital twins" (DTs) in 2002, the number of practical applications in different industrial sectors has grown rapidly. Despite the hype surrounding this technology, companies face significant challenges upon deciding to implement DTs in their organizations due to the novelty of the concept. Furthermore, little research on DT has been conducted for the process industry, which may be explained by the high complexity of accurately representing and modeling the physics behind production processes. To consolidate the fragmented literature on the enabling factors and challenges in DT implementation in the process industry, this study organizes the existing studies on DTs with a focus on barriers and enablers. On this basis, this study contributes to the existing body of knowledge on DTs by organizing the DT literature and by proposing conceptual models describing enablers of and barriers to DT implementation, as well as their mutual relationships.

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Ground Robotics: The Route to Intelligent and Reliable Operation & Maintenance

Drenth¹,

Matthias²

2020

Day 1 Mon, November 09, 2020

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This paper identifies the interdependency and impact of the maintenance of assets with the use of robotics in combination with artificial intelligence to support the modern ‘Operator 4.0’. The execution of high-quality, well-planned and efficiently-executed maintenance is crucial in today's energy production process, both from a reliability/ functionality and from an economic point of view. Applying such smart maintenance is an important driver to create long-lasting reliable performance during the operational lifetime of an asset, which will also be extended by executing such intelligent maintenance. Since a few years, a number of Robotics and Big Data technologies have come to the market and these are two of the major market disruptors, accelerating innovation and noteworthy changes to the industry. Well-equipped ground robots will become the new data gatherers, using a suite of high-quality sensors and predictive algorithms to provide real-time advice. Interpreted by experienced maintenance engineers - this leads to intelligent decision-making to sustain or enhance high performance levels on any production site. Important breakthrough of this development is the strong reduction in cost, the ‘simplicity’ and the almost miniatured size of the associated equipment, and the acceleration in e.g. computing power to process the many data streams and convert it in easy to interprete information. We have assessed, in practice, how such robots can indeed act as the (extra) eyes, ears and brains on the ground, for today's - and tomorrow's - operator and/or maintenance technician. This paper will detail a number of examples of state-of-the-art robotic solutions for operation and maintenance purposes, coupled to Big Data technologies, and the challenges that lie ahead of the industry to create the high-capital industrial installations of the future, starting today. The information gathered during our demonstrations and described in this paper will lead to new, inspiring and smart ways of designing, operating and maintaining energy production infrastructures - brownfield and greenfield, onshore & offshore - by using ground robots and the latest sensor and predictive maintenance technologies, adapted to the workforce of the 21st century.

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Johan Sverdrup: The Digital Flagship

Cited by 9 publications

References 6 publications

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers

Ground Robotics: The Route to Intelligent and Reliable Operation & Maintenance

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