Data-Driven Deconvolution Using Echo-State Networks Enhances Production Data Analysis in Unconventional Reservoirs

2019 ◽  
Author(s):  
Yuewei Pan ◽  
Lichi Deng ◽  
John Lee
Keyword(s):  
Data Analysis ◽  
Unconventional Reservoirs ◽  
Data Driven ◽  
Production Data ◽  
Echo State Networks

scholarly journals A method for production data analysis considering significant discontinuities in unconventional reservoirs

2018 ◽  
Vol 15 (5) ◽  
pp. 1835-1842 ◽  
Author(s):  
Qingyu Li ◽  
Peichao Li ◽  
Wei Pang ◽  
Jianping Huang ◽  
Haibo Liang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Unconventional Reservoirs ◽  
Production Data

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

Machine learning-assisted production data analysis in liquid-rich Duvernay Formation

2021 ◽  
Vol 200 ◽  
pp. 108377
Author(s):  
Bing Kong ◽  
Zhuoheng Chen ◽  
Shengnan Chen ◽  
Tianjie Qin
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Production Data

scholarly journals Data-driven deep density estimation

2021 ◽  
Author(s):  
Patrik Puchert ◽  
Pedro Hermosilla ◽  
Tobias Ritschel ◽  
Timo Ropinski
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Population Data ◽  
Training Data ◽  
Data Driven ◽  
Discrete Observations ◽  
Efficient Manner ◽  
Continuous Models ◽  
3D Scans ◽  
Spatial Locations

AbstractDensity estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

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