Production-Performance Diagnostics Using Field-Production Data and Analytical Models: Method and Case Study for the Hydraulically Fractured South Belridge Diatomite

2012 ◽  
Vol 15 (06) ◽  
pp. 712-724 ◽  
Author(s):  
Zhengming Yang
Keyword(s):  
Production Performance ◽  
Analytical Models ◽  
Production Data ◽  
Performance Diagnostics ◽  
Field Production

Improving Production Performance Through Multi-Plant Cross Learning

2021 ◽  
pp. 1-17
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Yu Qian ◽  
Jorge Arinez ◽  
Guoxian Xiao
Keyword(s):  
Manufacturing Sector ◽  
Production Performance ◽  
System Model ◽  
Production Data ◽  
Production Lines ◽  
Product Lines ◽  
Work Production ◽  
Critical Machines ◽  
Feasible Action

Abstract The advancement in Web-/Internet-based technologies and applications in manufacturing sector have increased utilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to improve the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increase of throughput is predicted.

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

scholarly journals Defining and Quantifying Intermittency in the Power Sector

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3366
Author(s):  
Daniel Suchet ◽  
Adrien Jeantet ◽  
Thomas Elghozi ◽  
Zacharie Jehl
Keyword(s):  
Power Source ◽  
Production Data ◽  
Power Sector ◽  
Predictive Capacity ◽  
The Public ◽  
Current State ◽  
Definition Of ◽  
Quantitative Definition ◽  
The Impact

The lack of a systematic definition of intermittency in the power sector blurs the use of this term in the public debate: the same power source can be described as stable or intermittent, depending on the standpoint of the authors. This work tackles a quantitative definition of intermittency adapted to the power sector, linked to the nature of the source, and not to the current state of the energy mix or the production predictive capacity. A quantitative indicator is devised, discussed and graphically depicted. A case study is illustrated by the analysis of the 2018 production data in France and then developed further to evaluate the impact of two methods often considered to reduce intermittency: aggregation and complementarity between wind and solar productions.

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