Form friction factor of armored riverbeds

2020 ◽  
Vol 47 (11) ◽  
pp. 1238-1248
Author(s):  
Saeid Okhravi ◽  
Saeed Gohari

Resistance to flow because of the presence of bed forms over armored riverbeds is of paramount importance, leading to the effective design of water-resources-related projects. Based on the findings over bed armored surfaces, it is shown that the controlling roughness (ks) can be taken as equal to the median diameter of the armor layer. Analytical methodologies for total and grain friction factors have been proposed here that take flow non-uniformity into account using the velocity distribution and friction slope. The percentage composition of form friction factor in the total friction factor was estimated to be 40%. The results were explained in light of the coupling of the sediment threshold problem with the friction factor and coarse-grain rearrangement in armor layer. The computed form friction factor by proposed method was compared with Keulegan’s method and is found to give satisfactory results, showing 80% agreement of all field data sets.

1987 ◽  
Vol 109 (4) ◽  
pp. 200-205 ◽  
Author(s):  
T. B. Jensen ◽  
M. P. Sharma

Published annular pressure drop field data have been compared with values predicted by the Bingham plastic and power law models. Several different equivalent diameter equations and friction factor correlations were utilized to estimate the frictional pressure gradients. The estimated frictional pressure drop gradients were then compared with the experimental gradients statistically to determine which combination of friction factor correlation and equivalent diameter equation predicted the experimental data best. Finally, new correlations for friction factors were developed. These new correlations predict the field data better than previously published correlations.


2021 ◽  
Author(s):  
John E. McCormick ◽  
Yanghua Xiang ◽  
Matt Tourigny ◽  
Kevin J. Hollerich ◽  
Aaron Berarducci ◽  
...  

Abstract Completions operations, especially in modern day extended laterals, presents challenges related to tripping to total depth, applying weight down and pull up, and rotating. As dozens of stages in laterals exceeding 10,000 ft stepout have become frequent, numerous technologies have risen to assist with pushing the envelope for reliable completions operations in these long laterals. This paper examines a combination of three technologies that are more commonly being applied when drilling out frac plugs in long horizontals in the USA: hydraulic completion units, torque and drag software, and data acquisition systems. Coiled tubing units (CTU) have historically been used to drill out frac plugs in shorter horizontal shale wells for the last two decades, and where coil has mechanical limitations, Hydraulic Completion Units (HCU) have taken over drilling out frac plugs in the longer laterals of >10,000 ft. As the limits of drilling out frac plugs have been tested for HCUs, accurate real time data has enabled the crews to make the most of their equipment to reliably complete wells with longer and longer lateral sections. Torque and drag software modeling is a tool commonly used to predict axial force and torsional values during completions that result in the available hook load and the rotary torque requirements. The largest unknown in the planning phase is the appropriate friction factor to use for the upcoming well, with accurate friction factor prediction therefore the key to accurate prejob analysis. As of 2019 remote telemetry data acquisition systems (DAS) have been used on the HCUs, which has allowed key performance indicators (KPIs) to be automatically calculated. The program provides live feed to the service company and operator so that real time changes can be made if necessary. In addition to tracking KPIs in real time to provide the field crew positive or negative feedback, friction factors can be matched to predictive torque plots to identify trends prior to problems arising. Post-job analysis is needed to produce accurate predictive friction factors for future offset wells. The two main components to a successful post-job analysis are a software model that correctly represents the prior wellbore operations and accurate field data to compare with that model. Unfortunately, the software models in use are commonly limited by necessary assumptions with input data, such as rotary speed and tripping speed, and field data collected for comparison is often rudimentary. Experienced field personnel using engineering best practices can make use of current tools in combination to overcome the limitations commonly inhibiting accurate performance planning and predictive modeling. The inclusion of the DAS present on the HCU has greatly enhanced the accuracy and amount of rig data gathered, which can then be used in conjunction with operational procedures and torque and drag software to accurately plan and execute completions operations in the wellbore. Using data acquisition software, a constant stream of data was collected in one-second intervals in over two dozen wells. This system has the ability to measure both rotary speed and rotary torque, which are critical parameters when drilling out frac plugs. By removing these assumptions in the post-job analysis over a number of wells, a range of friction factors have been established for the Appalachian Basin in the Utica and Marcellus plays. The authors will present field data from two wells as representative case studies, along with the range of predictive friction factors established from 13 wells for the particular completions operations evaluated in the Permian and Appalachia plays. It is the goal of the authors to disseminate technical information on the methodology and practice of modeling wells post-job, calibrating friction factors, and establishing predictive ranges for successful use in future projects.


2010 ◽  
Vol 132 (7) ◽  
Author(s):  
Henrique Stel ◽  
Rigoberto E. M. Morales ◽  
Admilson T. Franco ◽  
Silvio L. M. Junqueira ◽  
Raul H. Erthal ◽  
...  

This article describes a numerical and experimental investigation of turbulent flow in pipes with periodic “d-type” corrugations. Four geometric configurations of d-type corrugated surfaces with different groove heights and lengths are evaluated, and calculations for Reynolds numbers ranging from 5000 to 100,000 are performed. The numerical analysis is carried out using computational fluid dynamics, and two turbulence models are considered: the two-equation, low-Reynolds-number Chen–Kim k-ε turbulence model, for which several flow properties such as friction factor, Reynolds stress, and turbulence kinetic energy are computed, and the algebraic LVEL model, used only to compute the friction factors and a velocity magnitude profile for comparison. An experimental loop is designed to perform pressure-drop measurements of turbulent water flow in corrugated pipes for the different geometric configurations. Pressure-drop values are correlated with the friction factor to validate the numerical results. These show that, in general, the magnitudes of all the flow quantities analyzed increase near the corrugated wall and that this increase tends to be more significant for higher Reynolds numbers as well as for larger grooves. According to previous studies, these results may be related to enhanced momentum transfer between the groove and core flow as the Reynolds number and groove length increase. Numerical friction factors for both the Chen–Kim k-ε and LVEL turbulence models show good agreement with the experimental measurements.


Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V115-V128 ◽  
Author(s):  
Ning Wu ◽  
Yue Li ◽  
Baojun Yang

To remove surface waves from seismic records while preserving other seismic events of interest, we introduced a transform and a filter based on recent developments in image processing. The transform can be seen as a weighted Radon transform, in particular along linear trajectories. The weights in the transform are data dependent and designed to introduce large amplitude differences between surface waves and other events such that surface waves could be separated by a simple amplitude threshold. This is a key property of the filter and distinguishes this approach from others, such as conventional ones that use information on moveout ranges to apply a mask in the transform domain. Initial experiments with synthetic records and field data have demonstrated that, with the appropriate parameters, the proposed trace transform filter performs better both in terms of surface wave attenuation and reflected signal preservation than the conventional methods. Further experiments on larger data sets are needed to fully assess the method.


Weed Science ◽  
2007 ◽  
Vol 55 (6) ◽  
pp. 652-664 ◽  
Author(s):  
N. C. Wagner ◽  
B. D. Maxwell ◽  
M. L. Taper ◽  
L. J. Rew

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R2= 0.71), a double-hyperbolic equation including three input predictor variables (R2= 0.63), and a nonlinear model including all five predictor variables (R2= 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our five-variable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with anR2of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation.


1985 ◽  
Vol 107 (2) ◽  
pp. 280-283 ◽  
Author(s):  
D. J. Zigrang ◽  
N. D. Sylvester

A review of the explicit friction factor equations developed to replace the Colebrook equation is presented. Explicit friction factor equations are developed which yield a very high degree of precision compared to the Colebrook equation. A new explicit equation, which offers a reasonable compromise between complexity and accuracy, is presented and recommended for the calculation of all turbulent pipe flow friction factors for all roughness ratios and Reynold’s numbers.


Author(s):  
Francisco Fernando Hernandez ◽  
Federico Mendez ◽  
Jose Joaquin Lizardi ◽  
Ian Guillermo Monsivais

Abstract This work presents the numerical solution for different velocity profiles and friction factors on a rectangular porous microchannel fully saturated by the flow of a nanofluid introducing different viscosity models, including one nanofluid density model. The Darcy-Brinkman-Forchheimer equation was used to solve the momentum equation in the porous medium. The results show that the relative density of the fluid, the nanoparticle diameters and their volumetric concentration have a direct influence on the velocity profiles only when the inertial effects caused by the presence of the porous matrix are important. Finally, it was found that only viscosity models that depend on temperature and nanoparticle diameter reduce the friction factor by seventy percent compared to a base fluid without nanoparticles; furthermore, these models show a velocity reduction of even ten percent along the symmetry axis of the microchannel.


2019 ◽  
Vol 344 ◽  
pp. 443-453 ◽  
Author(s):  
Bhargav Bharathan ◽  
Maureen McGuinness ◽  
Sharun Kuhar ◽  
Mehrdad Kermani ◽  
Ferri P. Hassani ◽  
...  

2020 ◽  
Vol 224 (1) ◽  
pp. 669-681
Author(s):  
Sihong Wu ◽  
Qinghua Huang ◽  
Li Zhao

SUMMARY Late-time transient electromagnetic (TEM) data contain deep subsurface information and are important for resolving deeper electrical structures. However, due to their relatively small signal amplitudes, TEM responses later in time are often dominated by ambient noises. Therefore, noise removal is critical to the application of TEM data in imaging electrical structures at depth. De-noising techniques for TEM data have been developed rapidly in recent years. Although strong efforts have been made to improving the quality of the TEM responses, it is still a challenge to effectively extract the signals due to unpredictable and irregular noises. In this study, we develop a new type of neural network architecture by combining the long short-term memory (LSTM) network with the autoencoder structure to suppress noise in TEM signals. The resulting LSTM-autoencoders yield excellent performance on synthetic data sets including horizontal components of the electric field and vertical component of the magnetic field generated by different sources such as dipole, loop and grounded line sources. The relative errors between the de-noised data sets and the corresponding noise-free transients are below 1% for most of the sampling points. Notable improvement in the resistivity structure inversion result is achieved using the TEM data de-noised by the LSTM-autoencoder in comparison with several widely-used neural networks, especially for later-arriving signals that are important for constraining deeper structures. We demonstrate the effectiveness and general applicability of the LSTM-autoencoder by de-noising experiments using synthetic 1-D and 3-D TEM signals as well as field data sets. The field data from a fixed loop survey using multiple receivers are greatly improved after de-noising by the LSTM-autoencoder, resulting in more consistent inversion models with significantly increased exploration depth. The LSTM-autoencoder is capable of enhancing the quality of the TEM signals at later times, which enables us to better resolve deeper electrical structures.


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