Test data analytics — Exploring spatial and test-item correlations in production test data

2013 ◽  
Author(s):  
Chun-Kai Hsu ◽  
Fan Lin ◽  
Kwang-Ting Cheng ◽  
Wangyang Zhang ◽  
Xin Li ◽  
...  
Keyword(s):  
Test Data ◽  
Test Item ◽  
Data Analytics ◽  
Production Test

scholarly journals Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Jae Phil Park ◽  
Subhasish Mohanty ◽  
Chi Bum Bahn ◽  
Saurin Majumdar ◽  
Krishnamurti Natesan
Keyword(s):  
Fatigue Life ◽  
Fatigue Test ◽  
Safety Factor ◽  
Test Data ◽  
Correction Factor ◽  
Data Analytics ◽  
Probabilistic Approach ◽  
Fatigue Curve ◽  
Pressurized Water ◽  
Best Fit

Abstract In general, the fatigue life of a safety critical pressure component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, reactor component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly reactor components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water reactor water on the reactor component. In the discussed approach, in addition to the probabilistic fatigue curves, we suggest using a probabilistic environment correction factor Fen. We assume the probabilistic fatigue curve and Fen would capture the S-N data scatter associated with the bulk effect of material grades, surface finish, strain rate, etc. on the material/component fatigue life.

scholarly journals An Investigation of the Flow Distribution Inside a Radial Sidestream Inlet of a Centrifugal Compressor

2000 ◽  
Author(s):  
Zheji Liu ◽  
D. Lee Hill ◽  
Gary Colby
Keyword(s):  
Test Data ◽  
Centrifugal Compressor ◽  
Flow Distribution ◽  
Production Test ◽  
Cfd Model ◽  
Cfd Models ◽  
Multi Stage ◽  
Final Design ◽  
Computational Fluid Dynamics Cfd ◽  
Return Channel

A radial sidestream inlet is commonly utilized in multi-stage centrifugal compressors to introduce additional gas into the mid-stage of the compressor. The flow distribution after the junction of the sidestream and the main return channel of the upstream stage can significantly affect the performance of the next stage. In this study, the mixing between the fluid from the sidestream component and the fluid from the main return channel was investigated numerically using Computational Fluid Dynamics (CFD). A variety of CFD models of different geometry, different boundary conditions, and different grid density were developed to analyze the uniformity of the flow entering the impeller of the next stage. The flow distribution difference between the sidestream CFD model and the CFD model with the sidestream coupled to the main return channel suggests that both the return channel and the sidestream have to be modeled together to get meaningful results. The results of this effort were used in conjunction with production test data to help resolve a performance shortfall of a multi-stage centrifugal compressor with sidestream injection. The test data from the final design is also provided to show the resulting improvement in head rise.

Classification and Evaluation of Oil Reservoir of Fuyang Formation in Xingbei Area

2013 ◽  
Vol 868 ◽  
pp. 196-201
Author(s):  
Peng Guo ◽  
Shi Zhong Ma
Keyword(s):  
Test Data ◽  
Water Distribution ◽  
Basic Structure ◽  
Data Interpretation ◽  
Production Test ◽  
Oil Reservoir ◽  
Data Logging ◽  
Basic Understanding ◽  
Oil Water ◽  
Geological Background

Based on the Fuyang formation in Xingbei geological background research, to understand the basic structure of the study area conditions and the forming conditions. Combined study area forming conditions analysis, the study area as a whole basic understanding of oil-water distribution. Finally, the study area production test data, logging data interpretation and analysis of oil-bearing core, and thus the oil reservoir in the study area were evaluated in the study area in order to find favorable block.

scholarly journals Using data analytics to quantify the impact of production test uncertainty on oil flow rate forecast

2020 ◽  
Vol 75 ◽  
pp. 7 ◽  
Author(s):  
Danielle D. Monteiro ◽  
Maria Machado Duque ◽  
Gabriela S. Chaves ◽  
Virgílio M. Ferreira Filho ◽  
Juliana S. Baioco
Keyword(s):  
Multiphase Flow ◽  
Flow Rate ◽  
Data Analytics ◽  
Flow Analysis ◽  
Flow Simulation ◽  
Production Test ◽  
Total Production ◽  
Oil Flow ◽  
Using Data ◽  
The Impact

In general, flow measurement systems in production units only report the daily total production rates. As there is no precise control of individual production of each well, the current well flow rates and their parameters are determined when production tests are conducted. Because production tests are performed periodically (e.g., once a month), information about the wells is limited and operational decisions are made using data that are not updated. Meanwhile, well properties and parameters from the production test are typically used in multiphase flow models to forecast the expected production. However, this is done deterministically without considering the different sources of uncertainties in the production tests. This study aims to introduce uncertainties in oil flow rate forecast. To do this, it is necessary to identify and quantify uncertainties from the data obtained in the production tests, consider them in production modeling, and propagate them by using multiphase flow simulation. This study comprises two main areas: data analytics and multiphase flow simulation. In data analytics, an algorithm is developed using R to analyze and treat the data from production tests. The most significant stochastic variables are identified and data deviation is adjusted to probability distributions with their respective parameters. Random values of the selected variables are then generated using Monte Carlo and Latin Hypercube Sampling (LHS) methods. In multiphase flow simulation, these possible values are used as input. By nodal analysis, the simulator output is a set of oil flow rate values, with their interval of occurrence probabilities. The methodology is applied, using a representative Brazilian offshore field as a case study. The results show the significance of the inclusion of uncertainties to achieve greater accuracy in the multiphase flow analysis of oil production.

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