Chairs: Detlef Hohl, Shell International Exploration and Production; Serkan Dursun, Halliburton; Michael Strathman, The Trinity Group; Fred Cornell, Devon Energy
The oil and gas industry has more “uncertainty-of-the-measurements” than other industries that use applied data-driven analytics. Typically, we have few direct measurements of the asset that are miles below the surface. However, we have many indirect and interpretive measurements about that asset. Data-driven analytics, for oil and gas, focus on identifying patterns and understanding the relationships between the measurements to more accurately predict the assets performance.
Chairs: Luigi Saputelli, Frontender; Serkan Dursun, Halliburton; Akshay Makhare, PetroLink
Drilling is a complex operation challenged with safety issues, operational difficulties, and financial risks. These challenges necessitate that all drilling processes are planned and executed efficiently to minimize the costs and risks. The majority of drilling operation time is not spent on drilling but rather on dealing with drilling, rig equipment, or tool recovery problems. This leads to non-productive time (NPT). The solution to decreasing NPT is hidden in the vast amount of historical and real-time data collected. Therefore, data-driven analysis has a more responsible role in the drilling process, by decreasing NPT, and building safer operational environments.
Chairs: Amr El-Bakry, ExxonMobil Production; Cesar Bravo, Halliburton; Carol Piovesan, APO Offshore; Fred Cornell, Devon Energy
The key objective of asset management teams is to obtain the maximum value from asset-related data. Data-driven analytics help achieve this goal by extracting the greatest value from production data, and identifying patterns that allow for early event detection, failure prevention, production optimization, and uncertainty reduction for reservoir management. Furthermore, data-driven workflow automation is a key enabler to operation efficiency, technical effectiveness, continuous improvement, and innovation.
Chairs: Andrei Popa, Chevron; Akshay Makhare, PetroLink
With more than 80,000 wells drilled each year in unconventional reservoirs alone, a large amount of well completion data is captured. Additional microseismic monitoring or real-time fiber-optic data is recorded creating the “big data” of well completion. New tools, processes, and approaches used by data-driven analytics to optimize well completion and maximize the recovery factor from unconventional, deepwater, and mature fields will be discussed.
Chairs: Andrei Popa, Chevron; Yasin Hajizadeh, Schlumberger Information Solutions; Shahab D. Mohaghegh, West Virginia University
Reservoir characterization is one of the oldest and most prolific areas of contribution to data-driven analytics in the upstream exploration and production. Early works of virtual rock properties measurements to synthetic well logs will be reviewed. The long history of seismic interpretation contributions through to the most recent works in developing geomechnical characterization of shale plays will be mapped.
Chairs: Amr El-Bakry, ExxonMobil Production; Yasin Hajizadeh, Schlumberger Information Solutions; Shahab D. Mohaghegh, West Virginia University
For the past six decades reservoir modeling has been dominated by solutions to the partial differential equations that govern fluid flow in porous media. Recent advances in data-driven analytics have demonstrated the viability and value of data-driven reservoir modeling. Are these two approaches complementing or competing technologies? How do they compare, or differ, in the way they deal with major reservoir modeling challenges, such as uncertainty?
Chairs: Detlef Hohl, Shell International Exploration and Production; Shawn Shirzadi, BP; Luigi Saputelli, Frontender
The oil and gas industry is rapidly moving to the "digitization" of assets, enabling a real-time optimization opportunity for operations. There is a large value proposition for upstream operations to be at maximum production capacity at all times. This capacity is known as a short-loop optimization cycle. The historical challenge in upstream operations has been the usage of the data captured in the reservoirs, wells, and facilities and linkage with high fidelity, easy-to-update and useful models of the operations to achieve this optimization cycle.
Chairs: Cesar Bravo, Halliburton; Keith Holdaway, SAS Institute Inc.; Carol Piovesan, APO Offshore; Akshay Makhare, PetroLink
Although the oil and gas industry has rigorous surveillance practices in place, there is a lack of a comprehensive flow assurance methodology that models the flow of a hydrocarbon stream from reservoir to point-of-sale. There is a need to explore a data-driven suite of workflows that encapsulate the inherent uncertainties and risks in flow assurance models across the diverse and discrete engineering disciplines to address reliability and integrity issues. Exploratory data analysis and predictive analytics can bring to the surface hidden patterns, and quantify real-time production problems along the complete hydrocarbon flow process.
Chairs: Detlef Hohl, Shell International Exploration and Production; Cesar Bravo, Halliburton; Andrei Popa, Chevron; Amr El-Bakry, ExxonMobil Production
Participants will engage in interactive discussions to continue topics that were curtailed earlier in the program due to time constraints, or raise issues that have not yet been discussed.