scholarly journals Biopolymer Drill-in Fluid Performance for Different Rheological Models using Statistical Characterisation

2019 ◽  
Vol 19 (1) ◽  
pp. 86-92
Author(s):  
M. Owusu ◽  
H. Osei
Keyword(s):  
Rheological Model ◽  
Rheological Behaviour ◽  
Primary Objective ◽  
Least Square ◽  
Model Parameters ◽  
Drilling Fluids ◽  
Cleaning Efficiency ◽  
Rheological Models ◽  
Least Square Regression ◽  
Best Fit

Appropriate selection of rheological models is important for hydraulic calculations of pressure loss prediction and hole cleaning efficiency of drilling fluids. Power law, Bingham-Plastic and Herschel-Bulkley models are the conventional fluid models used in the oilfield. However, there are other models that have been proposed in literature which are under/or not utilized in the petroleum industry. The primary objective of this paper is to recommend a rheological model that best-fits the rheological behaviour of xanthan gum-based biopolymer drill-in fluids for hydraulic evaluations. Ten rheological models were evaluated in this study. These rheological models have been posed deterministically and due to the unrealistic nature have been replaced by statistical models, by adding an error (disturbance) term and making suitable assumptions about them. Rheological model parameters were estimated by least-square regression method. Models like Sisko and modified Sisko which are not conventional models in oil industry gave a good fit. Modified Sisko model which is a four parameter rheological model was selected as the best-fit model since it produced the least residual mean square of 0.61 Ibf2/100ft4. There is 95% certainty that the true best-fit curve lies within the confidence band of this function of interest. Keywords: Biopolymer; Least-Square Regression; Residual Mean Squares; Rheologram

Structural response reconstruction using inclinometer and velocimeter

2019 ◽  
Author(s):  
Yixian Li ◽  
Limin Sun ◽  
Wei Zhang
Keyword(s):  
Balance Equation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Model Parameters ◽  
Time Interval ◽  
Dynamic Distortion ◽  
Nodal Force ◽  
Least Square Regression ◽  
Response Reconstruction

<p>This paper proposes a structural dynamic response reconstruction algorithm using inclinometer and velocimeter, combining in-situ measured data with finite element model. Using a small number of inclination and velocity data, the dynamic deflection, rotation, and strain at any position of a structure can be estimated. Firstly, static structural deformation estimation method is introduced as the base. The key content is to solve an underdetermined static equation using partial least square regression and calculate equivalent nodal force. By rewriting dynamic balance equation into state space, an equivalent static balance equation is obtained. Use partial least square regression to solve this equation and compute time histogram of equivalent nodal force, in which dynamic distortion exists. Accordingly, a frequency response-based time interval correction method is proposed to correct the dynamic distortion and is validated to be effective. Finally, numerical simulation is adopted to validate accuracy and robustness of the algorithm through changing parameters including sampling time interval, input frequency components, model parameters and introducing measurement noise. All results have demonstrated that the algorithm is of good adaptability to various changes and maintains high accuracy.</p>

scholarly journals Selection of a Suitable Rheological Model for Drilling Fluid Using Applied Numerical Methods

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3192 ◽  
Author(s):  
Rafał Wiśniowski ◽  
Krzysztof Skrzypaszek ◽  
Tomasz Małachowski
Keyword(s):  
Linear Regression ◽  
Rheological Model ◽  
Drilling Fluid ◽  
Regression Method ◽  
Rheological Parameters ◽  
Drilling Fluids ◽  
Linear Regression Method ◽  
Technological Parameters ◽  
Rheological Models ◽  
Bingham Plastic

The accuracy of fitting the rheological model to the properties of actual drilling fluid minimises the errors of the calculated technological parameters applied while drilling oil wells. This article presents the methodology of selecting the optimum drilling fluid rheological model. Apart from classical rheological models, i.e., the Newtonian, Bingham Plastic, Casson, Ostwald de Waele and Herschel–Bulkley models, it has been proposed to consider the Vom Berg and Hahn-Eyring models, which have not been applied to describe drilling fluids so far. In the process of determining rheological parameters for the Bingham Plastic, Casson, Ostwald de Waele and Newtonian models, it is proposed to use a linear regression method. In the case of the Herschel–Bulkley, Vom Berg and Hahn-Eyring models, it is suggested to use a non-linear regression method. Based on theoretical considerations and mathematical relations developed in the Department of Drilling and Geoengineering, Drilling, Oil and Gas Faculty, at AGH University of Science and Technology, an original computer program called Rheosolution was developed, which enables automation of the process of determining the optimum drilling fluid rheological model. Some examples show the practical application of the method of selecting the optimum drilling fluid rheological model. Taking into account data from actual measurements of drilling fluid properties, it has been proven that the Vom Berg and Hahn-Eyring rheological models are best fitted to the description of drilling fluid rheological parameters.

Nonlinear Rheological Modeling of Asphalt Using White-Metzner Model with Structural Parameter Variation Based Asphaltene Structural Build-up and Breakage

2008 ◽  
Vol 18 (2) ◽  
pp. 23214-1-23214-14 ◽  
Author(s):  
Rohit Vijay ◽  
Abhijit P. Deshpande ◽  
Susy Varughese
Keyword(s):  
Experimental Data ◽  
Rheological Behavior ◽  
Rheological Model ◽  
Structural Changes ◽  
Chemical Constituents ◽  
Rheological Behaviour ◽  
Structural Parameter ◽  
Model Parameters ◽  
Order Kinetic ◽  
Shear Modes

Abstract Rheological behavior of asphalt is strongly affected by loading conditions, temperature and environment. One of the main challenges in understanding the rheology of asphalt is to relate the chemical constituents and the micro-structure of asphalt on one hand to its rheological behavior on the other hand. In this work, nonlinear rheological behaviour of asphalt was investigated using a structural rheological model. A first order kinetic equation to describe structural changes in asphalt has been incorporated with the nonlinear rheological model of White- Metzner. The resulting set of governing equations was solved numerically to describe the rheology of asphalts. Different modes of rheological testing and asphalts with different compositions were considered. An analysis and comparison of model behaviour with experimental data from the literature is carried out in both stress growth at constant shear rate and oscillatory shear modes. A strategy is proposed for the estimation and tuning of the model parameters based on available experimental data and literature. Qualitatively, the model can capture the rheological behaviour of non-Newtonian fluids such as asphalt under different modes of rheological testing. Quantitative analysis from this work shows that the model describes the rheological behaviour of asphalt for the temperature range of 20 – 60ºC. It is demonstrated that a single set of equations tuned with the steady shear experimental data can be used to predict the nonlinear rheological behaviour of asphalts. In addition, it is shown that the model parameters can be related to the chemical composition of asphalts.

Parametric identification of integrated model of a coal-fired boiler in a thermal power plant

2019 ◽  
Vol 234 (4) ◽  
pp. 520-533
Author(s):  
Sreepradha Chandrasekharan ◽  
Rames C Panda ◽  
Bhuvaneswari Natrajan Swaminathan ◽  
Atanu Panda ◽  
T Thyagarajan
Keyword(s):  
Power Plant ◽  
Comparative Study ◽  
Expectation Maximization ◽  
Power Plants ◽  
Thermal Power ◽  
Parametric Identification ◽  
Primary Objective ◽  
Least Square ◽  
Model Parameters ◽  
Identification Methods

Retrofit or replacement of few units in a subcritical facility may not only improve overall efficiency of conversion of energy in a power plant but also support sustainability issues. The primary objective of this article is to identify model parameters of a coal-fired integrated boiler and to present a comparative study on three different identification methods. This leads to select most suitable models that are applied for the developed model of the boiler of 210 MW coal-fired thermal power plants. The mathematical models of economizer, drum, and super-heater assembly are derived using mass balance and energy balance equations. The derived multi input–multi output model is then validated, and the model parameters are identified using three different identification methods namely nonlinear least square technique, maximum likelihood estimation, and expectation maximization algorithms. Identification of the plant model will essentially help to frame a good controller. In this article, parameter estimation has been carried out from real-time plant as it provides selective tool through quantitative comparative study of the three methods. The expectation maximization method has been found to provide suitable results compared to the other two methods. Model parameters of integrated boiler of a comprehensive structure have been obtained for the first time using expectation maximization method.

Direct Assessment of Effective Renal Plasma Flow from Renoscintigraphy with a Gamma Camera and an On-Line Computer

1981 ◽  
Vol 20 (06) ◽  
pp. 274-278
Author(s):  
J. Liniecki ◽  
J. Bialobrzeski ◽  
Ewa Mlodkowska ◽  
M. J. Surma
Keyword(s):  
Count Rate ◽  
Renal Plasma Flow ◽  
Early Period ◽  
Least Square Method ◽  
Least Square ◽  
Uptake Coefficient ◽  
Positive Linear Correlation ◽  
On Line ◽  
Best Fit ◽  
Plasma Activity

A concept of a kidney uptake coefficient (UC) of 131I-o-hippurate was developed by analogy from the corresponding kidney clearance of blood plasma in the early period after injection of the hippurate. The UC for each kidney was defined as the count-rate over its ROI at a time shorter than the peak in the renoscintigraphic curve divided by the integral of the count-rate curve over the "blood"-ROI. A procedure for normalization of both curves against each other was also developed. The total kidney clearance of the hippurate was determined from the function of plasma activity concentration vs. time after a single injection; the determinations were made at 5, 10, 15, 20, 30, 45, 60, 75 and 90 min after intravenous administration of 131I-o-hippurate and the best-fit curve was obtained by means of the least-square method. When the UC was related to the absolute value of the clearance a positive linear correlation was found (r = 0.922, ρ > 0.99). Using this regression equation the clearance could be estimated in reverse from the uptake coefficient calculated solely on the basis of the renoscintigraphic curves without blood sampling. The errors of the estimate are compatible with the requirement of a fast appraisal of renal function for purposes of clinical diagknosis.

Ease of Doing Business and Its Impact on Inward FDI

2018 ◽  
Vol 1 (1) ◽  
pp. 52 ◽  
Author(s):  
Mohamed Tareq Hossain ◽  
Zubair Hassan ◽  
Sumaiya Shafiq ◽  
Abdul Basit
Keyword(s):  
Regression Model ◽  
Causal Relationship ◽  
World Bank ◽  
Least Square ◽  
Doing Business ◽  
Least Square Regression ◽  
Foreign Countries ◽  
Ease Of Doing Business ◽  
The Impact ◽  
Starting A Business

This study investigates the impact of Ease of Doing Business on Inward FDI over the period from 2011 to 2015 across the globe. This study measures ease of doing business using starting a business, getting credit, registering property, paying taxes and enforcing contracts. The research used a sample of 177 countries from 190 countries listed in World Bank. Least square regression model via E-views software used to examine causal relationship. The study found that ease of doing business indicators ‘Enforcing Contracts’ was found to have a positive significant impact on Inward FDI. Nevertheless, ‘Getting Credit’ and ‘Registering Property’ were found to have a negative significant impact on Inward FDI. However, ‘Starting a Business’ and ‘Paying Taxes’ have no significant impact on Inward FDI in the studied timeframe of this research. The findings of the study suggested the ease of doing business enables inward FDI through better contract enforcements, getting credit and registering property. The findings of the research will assist international managers and companies to know the importance of ease of doing business when investing in foreign countries through FDI.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, &gt;0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Fitting Weibull Strength Data and Applying It to Stochastic Mechanical Design

1992 ◽  
Vol 114 (1) ◽  
pp. 35-41 ◽  
Author(s):  
C. R. Mischke
Keyword(s):  
Mechanical Design ◽  
Least Square ◽  
Stochastic Methods ◽  
Coordinate Transformations ◽  
Strength Data ◽  
The Third ◽  
Tension Tests ◽  
Property Data ◽  
Simple Tension ◽  
Best Fit

This is the second paper in a series relating to stochastic methods in mechanical design. The first is entitled, “Some Property Data and Corresponding Weibull Parameters for Stochastic Mechanical Design,” and the third, “Some Stochastic Mechanical Design Applications.” When data are sparse, many investigators prefer employing coordinate transformations to rectify the data string, and a least-square regression to seek the best fit. Such an approach introduces some bias, which the method presented here is intended to reduce. With mass-produced products, extensive testing can be carried out and prototypes built and evaluated. When production is small, material testing may be limited to simple tension tests or perhaps none at all. How should a designer proceed in order to achieve a reliability goal or to assess a design to see if the goal has been realized? The purpose of this paper is to show how sparse strength data can be reduced to distributional parameters with less bias and how such information can be used when designing to a reliability goal.

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