Integrated Planning to Optimize Field Development Strategies and Real Time Decision Making During Project Execution for Large Scale Shale Resource Projects

2016 ◽  
Author(s):  
M. Mitschanek ◽  
M. Prohaska ◽  
G. Thonhauser
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Development Strategies ◽  
Integrated Planning ◽  
Field Development

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Collaborative Systems for Decision Making for Disaster Preparedness and Response, Department of Information Systems

2010 ◽  
pp. 41-57
Author(s):  
Deidre Hahn ◽  
Jessica Block ◽  
Mark Keith ◽  
Ajay Vinze
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Disaster Preparedness ◽  
Large Scale ◽  
Emergency Situation ◽  
Group Processes ◽  
Group Decision Support Systems ◽  
Emergency Situations ◽  
Collaborative Technologies ◽  
Disaster Preparedness And Response

Real time collaboration solutions are critical during a large scale emergency situation and necessitate the coordination of multiple disparate groups. Collaborative technologies may be valuable in the planning and execution of disaster preparedness and response. Yet, research suggests that specific collaborative technologies, such as group decision support systems, are not often leveraged for decision-making during real time emergency situations in the United States. In this chapter, we propose a theoretical model of the impact of disaster immediacy and collaboration systems on group processes and outcomes. Using a 3D model of the dimensions of space, time, and situation, we explore media richness and group polarization within the context of collaboration technologies and disaster situations. We also present the next generation of collaboration technology extensions in order to address the need for more contemporary decisional settings. This set of principles and theories suggest how collaborative technologies may be positioned to better manage future disasters.

Technology Focus: Field Development (September 2021)

2021 ◽  
Vol 73 (09) ◽  
pp. 43-43
Author(s):  
Reza Garmeh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Development Strategies ◽  
Digital Transformation ◽  
Data Driven ◽  
Development Work ◽  
Field Development

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

scholarly journals An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time

2020 ◽  
Vol 12 (13) ◽  
pp. 2162 ◽  
Author(s):  
Michael Nolde ◽  
Simon Plank ◽  
Torsten Riedlinger
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Near Infrared ◽  
Vegetation Type ◽  
Time Lag ◽  
Burnt Area ◽  
Continental Scale ◽  
High Detection ◽  
Burnt Areas

In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment.

Economic MPC and real-time decision making with application to large-scale HVAC energy systems

2018 ◽  
Vol 114 ◽  
pp. 89-98 ◽  
Author(s):  
James B. Rawlings ◽  
Nishith R. Patel ◽  
Michael J. Risbeck ◽  
Christos T. Maravelias ◽  
Michael J. Wenzel ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Energy Systems

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

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