real time system

Automated drilling - PetroWiki

PetrowikiJune, 2022

Petrowiki

Country: North America > United States (1.00)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Well Drilling > Drilling Automation (1.00)
Well Drilling > Drilling Operations > Drilling operation management (0.51)
Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.35)

Technology:

Information Technology > Knowledge Management (0.40)
Information Technology > Communications > Collaboration (0.40)
Information Technology > Artificial Intelligence > Representation & Reasoning > Case-Based Reasoning (0.35)
Information Technology > Architecture > Real Time Systems (0.31)

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Immiscible gas injection monitoring - PetroWiki

PetrowikiJune, 2022

Petrowiki

displacement, Engineer, enhanced recovery, gas injection method, gas injection monitoring, gas sample, goc movement, GOR, knowledge management, PetroWiki, production well, real time system, reservoir, Upstream Oil & Gas

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines: Reservoir Description and Dynamics > Improved and Enhanced Recovery > Gas-injection methods (1.00)

Technology:

Information Technology > Architecture > Real Time Systems (0.57)
Information Technology > Knowledge Management (0.42)
Information Technology > Communications > Collaboration (0.42)

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Real-Time-as-a-Service Increases Efficiency, Agility, and Consistency - SPE Energy Stream

SPE Energy StreamJune, 2022

Oil and gas operators use data collected from well operations for real-time monitoring, performance analysis, and remote operations. To address issues from manually provisioning capex-heavy IT infrastructure and applications, the authors commissioned a public-cloud, real-time-as-a-service solution, which increased performance and consistency of real-time data access for a Norwegian operator, as well as improved operating efficiency and innovation.

SPE Energy Stream

agility, consistency, operation, real time system, real-time-as-a-service increase efficiency, spe energy stream

SPE Disciplines: Data Science & Engineering Analytics > Information Management and Systems (1.00)

Technology: Information Technology > Architecture > Real Time Systems (1.00)

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SPE Online Education: Artificial Intelligence Applications in the E&P Industry and an Example of Short-Term Production Prediction Using Neural Networks

SPE TrainingJune, 2022

He worked as Production Engineering Senior Advisor at Hess Corporation (2009-2012), Halliburton (2003-2009) where he acted as technical lead of various major projects such as Petrobras' Barracuda-Caratinga fields Real Time Operations project (2005-2006), PDVSA integrated modeling and field exploitation plans for Carito and Orocual fields (2006-2007). Dr. Saputelli later acted as Production Operations Regional Practice Manager and Field Development Global Practice Manager for Halliburton; also worked in PDVSA E&P (1990-2001) and acted as a well planning senior advisor, production technologist, reservoir modeling and simulation engineer. He has worked in various countries: Venezuela, Argentina, Brazil, Colombia, Saudi Arabia, Kuwait, Arab Emirates, Nigeria, Thailand, Malaysia, Indonesia, England, Scotland and USA. He is an industry recognized researcher, lecturer, SPE Liaison and member of various committees. He has published over 50 industry papers and has three patents on applied technologies for reservoir management, real time optimization and production operations.

SPE Training

artificial intelligence application, example, Halliburton, machine learning, neural network, practice manager, real time system, short-term production prediction, spe online education, Upstream Oil & Gas

Country:

South America > Venezuela > Monagas (0.30)
South America > Brazil > Rio de Janeiro > South Atlantic Ocean (0.30)
Europe > United Kingdom > Scotland (0.30)
(3 more...)

Genre: Instructional Material > Online (0.40)

Industry:

Energy > Oil & Gas > Upstream (1.00)
Education > Educational Technology > Educational Software > Computer Based Training (0.85)

Oilfield Places:

South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Orocual Field > San Juan Formation (0.98)
South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Orocual Field > Las Piedras Formation (0.98)
South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Orocual Field > Carapita Formation (0.98)

SPE Disciplines:

Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Data Science & Engineering Analytics > Information Management and Systems > Neural networks (0.76)

Technology:

Information Technology > Architecture > Real Time Systems (1.00)
Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.40)

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SPE Online Education: A Multi-Faceted, Multi-Disciplinary, Machine Learning-Driven Dashboard for Safe and Efficient Drilling

SPE TrainingJune, 2022

In this webinar, the speaker presents a next generation vision for a state-of-the-art dashboard (QDashRT) driven by machine learning that provides drillers and operations geoscientists with real-time guidance for several mission-critical parameters and measurements. These include a sub-seismic resolution earth model, parameterized with pore pressure and geomechanical properties that are updated in real-time with new LWD data and enables ahead-of-the-bit and away-from-the bit visual awareness of potential drilling hazards; also included is a drilling parameter advisory system driven by an ROP simulator that considers transitions in rock properties; a synthetic logging and seismic-to-well tie product that, in the first instance, simulates DTC, DTS and RHOB for use in real-time pore pressure and geomechanical analysis, and in the second instance, facilitates the most accurate location of the well relative to geologic structure available in real-time. Finally, a novel implementation of a recurrent neural network is used to detect, in advance, the onset of a potentially hazardous formation, such as highly fractured carbonates or depleted sands. The individual components of the workflow will be described, along with the underlying data science framework and the Midland Basin geology targeted by the real-time earth model. This webinar is categorized under the Drilling technical discipline.

SPE Training

Artificial Intelligence, efficient drilling, gareth taylor, machine learning-driven dashboard, multi-disciplinary, multi-faceted, neural network, real time system, Reservoir Characterization, reservoir geomechanics, seismic interpretation, seismic surveying, spe online education, Upstream Oil & Gas, webinar, well tie

Country: North America > United States > Texas (0.28)

Genre: Instructional Material > Online (1.00)

Geophysics: Geophysics > Seismic Surveying > Seismic Interpretation > Well Tie (1.00)

Industry:

Energy > Oil & Gas > Upstream (1.00)
Education > Educational Technology > Educational Software > Computer Based Training (0.85)

Oilfield Places: North America > United States > Texas > Permian Basin > Midland Basin (0.99)

SPE Disciplines:

Well Drilling (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Technology:

Information Technology > Architecture > Real Time Systems (1.00)
Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.62)

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SPE Online Education: AI/ML Drilling Systems Need Timely Trusted Data to Deliver Trusted Results

SPE TrainingJune, 2022

Real-time data now represents a growing stream of scores of channels of digital data being fed concurrently to numerous receiving entities in the operator's remote monitoring amenities, at contractor centers and other facilities. Analytics are applied to data channels to signal or predict deviations from expected readings that require attention. For such systems to work effectively and reliably the input data must be trustworthy. Establishing trust in a digital, i.e. binary, manner requires more rigor than what would be performed by a human observer. The boundary conditions for trust must be codified into rules that are grouped in a policy suitable for each data stream.

SPE Training

ai ml drilling system, deliver trusted result, Jay Hollingsworth, real time system, spe online education, webinar

Genre: Instructional Material > Online (0.76)

Industry:

Education > Educational Setting > Online (1.00)
Education > Educational Technology > Educational Software > Computer Based Training (0.85)

SPE Disciplines: Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.40)

Technology:

Information Technology > Sensing and Signal Processing (0.62)
Information Technology > Architecture > Real Time Systems (0.62)
Information Technology > Communications (0.46)

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fundamental-thermal-wellreservoir-surveillance

SPE TrainingJune, 2022

Thermal development for producing heavy oil rely on a heat delivery mechanism to the reservoir mainly through injecting hot fluid (mainly steam). To increase the operational efficiency and ultimate recovery the surveillance and tracking of the heat placement would be critical. Throughout the years there was several advancements in developing key technologies for temperature, pressure and displacement in thermal recovery. This course discusses the operational practice on design, field operation of the surveillance method implemented in thermal wells and different technique in monitoring and analyzing the data for real time decision making. Fiber-optic sensing and its applications have been in use in the oilfield since the mid-1990's.

SPE Training