Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models

Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa–Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.

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Scaling Formulae for the Wellbore Hydraulics Similitude with Drill Pipe Rotation and Eccentricity

10.2118/204637-ms ◽

2021 ◽

Author(s):

Thad Nosar ◽

Pooya Khodaparast ◽

Wei Zhang ◽

Amin Mehrabian

Keyword(s):

Power Law ◽

Flow Rate ◽

Annular Flow ◽

Rotation Speed ◽

Drilling Fluid ◽

Drill Pipe ◽

Flow Loop ◽

Annular Flows ◽

Fluid Rheology ◽

Pipe Rotation

Abstract Equivalent circulation density of the fluid circulation system in drilling rigs is determined by the frictional pressure losses in the wellbore annulus. Flow loop experiments are commonly used to simulate the annular wellbore hydraulics in the laboratory. However, proper scaling of the experiment design parameters including the drill pipe rotation and eccentricity has been a weak link in the literature. Our study uses the similarity laws and dimensional analysis to obtain a complete set of scaling formulae that would relate the pressure loss gradients of annular flows at the laboratory and wellbore scales while considering the effects of inner pipe rotation and eccentricity. Dimensional analysis is conducted for commonly encountered types of drilling fluid rheology, namely, Newtonian, power-law, and yield power-law. Appropriate dimensionless groups of the involved variables are developed to characterize fluid flow in an eccentric annulus with a rotating inner pipe. Characteristic shear strain rate at the pipe walls is obtained from the characteristic velocity and length scale of the considered annular flow. The relation between lab-scale and wellbore scale variables are obtained by imposing the geometric, kinematic, and dynamic similarities between the laboratory flow loop and wellbore annular flows. The outcomes of the considered scaling scheme is expressed in terms of closed-form formulae that would determine the flow rate and inner pipe rotation speed of the laboratory experiments in terms of the wellbore flow rate and drill pipe rotation speed, as well as other parameters of the problem, in such a way that the resulting Fanning friction factors of the laboratory and wellbore-scale annular flows become identical. Findings suggest that the appropriate value for lab flow rate and pipe rotation speed are linearly related to those of the field condition for all fluid types. The length ratio, density ratio, consistency index ratio, and power index determine the proportionality constant. Attaining complete similarity between the similitude and wellbore-scale annular flow may require the fluid rheology of the lab experiments to be different from the drilling fluid. The expressions of lab flow rate and rotational speed for the yield power-law fluid are identical to those of the power-law fluid case, provided that the yield stress of the lab fluid is constrained to a proper value.

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Comprehensive experimental investigation of hole cleaning performance in horizontal wells including the effects of drillstring eccentricity, pipe rotation and cuttings size

Journal of Energy Resources Technology ◽

10.1115/1.4052102 ◽

2021 ◽

pp. 1-11

Author(s):

Ahmed K. Abbas ◽

Mortadha T. Alsaba ◽

Mohammed F. Al Dushaishi

Keyword(s):

Flow Rate ◽

Oil And Gas ◽

Drilling Fluid ◽

Drill Pipe ◽

Real Field ◽

Operational Parameters ◽

Cleaning Efficiency ◽

Hole Cleaning ◽

Cleaning Performance ◽

Pipe Rotation

Abstract Extended reach (ERD) wells with a horizontal and highly deviated section are widely applied in the oil and gas industry because they provide higher drainage area than vertical wells; and hence, increase the productivity or injectivity of the well. Among many issues encountered in a complex well trajectory, poor hole cleaning is the most common problem, which occurs mainly in the deviated and horizontal section of oil and gas wells. There are significant parameters that have a serious impact on hole cleaning performance in high-angle and horizontal sections. These include flow rate, rheology and density of the drilling fluid, drillstring eccentricity, pipe rotation, and cuttings size. It has been recognized that the action of most of these parameters to transport drilled cuttings is constantly a point of controversy among oilfield engineers. In the present study, extensive experiments were conducted in an advanced purpose-built flow rig to identify the main parameters affecting on circulate the cuttings out of the test section in a horizontal position. The flow-loop simulator has been designed to allow easy variation of operational parameters in terms of flow rate, mud density, drillstring eccentricity, pipe rotation, and cuttings size. In addition, the study covers the impacts of laminar, transition, and turbulent flow regimes. The goal of such variation in the operational conditions is to simulate real field situations. The results have shown that drill string rotation and flow rate were the operational parameters with the highest positive influence on the cuttings transports process. In contrast, drill pipe eccentricity has a negative influence on cuttings removal efficiency. The cuttings transportation performance is further improved by pipe rotation at different levels of eccentricity, especially at fully eccentric annuli. It was also shown that larger cuttings appeared to be easier to remove in a horizontal annulus than smaller ones. The experimental results would provide a more in-depth understanding of the relationship between drilling operation parameters and hole cleaning efficiency in ERD operations. This will help the drilling teams to realize what action is better to take for efficient cutting transportation.

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Multieffects on Mixing Quality of an Active Micromixer

Volume 2: Fora, Parts A and B ◽

10.1115/fedsm2007-37155 ◽

2007 ◽

Cited By ~ 1

Author(s):

Thien X. Dinh ◽

Yoshifumi Ogami

Keyword(s):

Flow Rate ◽

Pressure Loss ◽

Rotation Speed ◽

Mixing Efficiency ◽

Mean Velocity ◽

Mixing Performance ◽

Convective Mixing ◽

Wide Range ◽

Computational Fluid Dynamics Cfd

The convective mixing performance of an active micromixer is analyzed by using computational fluid dynamics (CFD). The mixer consists of a Y-shaped channel and an N-paddle (3, 4, and 5) rotor with radius R suspended in the junction of the channel. Numerical simulations are performed for a wide range of rotation speed of the rotor, ω, and mean velocity in the mixer, U. The asymptotic mixing performance is investigated by means of Lagrangian particle tracking simulation, stretching of a material line, dispersive and distributive mixing efficiencies. The results show that the mixing performance depends on the combined variable ωR/U, whereas paddle number has ignorable effects. Physically, the convective ratio of rotation speed to mean velocity governs the mixing process in the mixer. Contrastively, paddle number affects significantly to pressure loss and fluid torque exercising on the rotor. The time-averaged fluid torque depends linearly on rotation speed regardless of flow rate. Pressure loss relates linearly to flow rate, negligibly to rotation speed. It shows that a smaller paddle number produces lesser pressure loss and fluid torque for the same mixing efficiency.

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Decoding individual differences in STEM learning from functional MRI data

10.31234/osf.io/6ac7f ◽

2018 ◽

Author(s):

Joshua S. Cetron ◽

Andrew C. Connolly ◽

Solomon G. Diamond ◽

Vicki V. May ◽

James V. Haxby ◽

...

Keyword(s):

Individual Differences ◽

Conceptual Understanding ◽

Brain Activity ◽

Data Driven ◽

Stem Learning ◽

Concept Knowledge ◽

Wide Range ◽

Concept Inventories ◽

Using Data ◽

The Brain

Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

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Estimation of Cuttings Concentration and Frictional Pressure Losses During Drilling Using Data-Driven Models

10.1115/omae2021-63653 ◽

2021 ◽

Author(s):

Murat Ozbayoglu ◽

Evren Ozbayoglu ◽

Baris Guney Ozdilli ◽

Oney Erge

Keyword(s):

Oil And Gas ◽

Mechanistic Model ◽

Data Driven ◽

P Value ◽

Cuttings Transport ◽

Frictional Loss ◽

Pressure Losses ◽

Dimensionless Groups ◽

Wide Range ◽

Pipe Rotation

Abstract Drilling practice has been evolving parallel to the developments in the oil and gas industry. Current supply and demand for oil and gas dictate search for hydrocarbons either at much deeper and hard-to-reach fields, or at unconventional fields, both requiring extended reach wells, long horizontal sections, and 3D complex trajectories. Cuttings transport is one of the most challenging problems while drilling such wells, especially at mid-range inclinations. For many years, numerous studies have been conducted to address modeling of cuttings transport, estimation of the concentration of cuttings as well as pressure losses inside the wellbores, considering various drilling variables having influence on the process. However, such attempts, either mechanistic or empirical, have many limitations due to various simplifications and assumptions made during the development stage. Fluid thixotropy, temperature variations in the wellbore, uncertainty in pipe eccentricity as well as chaotic motion of cuttings due to pipe rotation, imperfections in the wellbore walls, variations in the size and shape of the cuttings, presence of tool joints on the drillstring, etc. causes the modeling of the problem extremely difficult. Due to the complexity of the process, the estimations are usually not very accurate, or not reliable. In this study, data-driven models are used to address the estimation of cuttings concentration and frictional loss estimation in a well during drilling operations, instead of using mechanistic or empirical methods. The selected models include Artificial Neural Networks, Random Forest, and AdaBoost. The training of the models is determined using the experimental data regarding cuttings transport tests collected in the last 40 years at The University of Tulsa – Drilling Research Projects, which includes a wide range of wellbore and pipe sizes, inclinations, ROPs, pipe rotation speeds, flow rates, fluid and cuttings properties. The evaluation of the models is conducted using Root Mean Square Error, R-Squared Values, and P-Value. As the inputs of the data-driven models, independent drilling variables are directly used. Also, as a second approach, dimensionless groups are developed based on these independent drilling variables, and these dimensionless groups are used as the inputs of the models. Moreover, performance of the data-driven model results are compared with the results of a conventional mechanistic model. It is observed that in many cases, data-driven models perform significantly better than the mechanistic model, which provides a very promising direction to consider for real time drilling optimization and automation. It is also concluded that using the independent drilling variables directly as the model inputs provided more accurate results when compared with dimensional groups are used as the model inputs.

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The Principal as a Data-Driven Instructional Leader

Advances in Early Childhood and K-12 Education - Data Leadership for K-12 Schools in a Time of Accountability ◽

10.4018/978-1-5225-3188-3.ch005 ◽

2018 ◽

pp. 76-97

Author(s):

Sonya D. Hayes ◽

Carlos G. Lee

Keyword(s):

No Child Left Behind ◽

School Improvement ◽

Student Learning ◽

Learning Community ◽

Professional Learning ◽

Instructional Leader ◽

Data Driven ◽

Wide Range ◽

Using Data ◽

Improve Instruction

The No Child Left Behind Act (NCLB) began an educational reform movement that iterated standardization and accountability. Since the onset of NCLB, educational leaders have focused more attention on using data to guide and inform school improvement efforts. Although most school leaders and teachers have access to a wide-range of data, the examination and interpretation of data to inform teaching and to improve student learning has been a challenge for educators. In this chapter, the authors review the literature on data-driven decision making (DDDM) and elaborate on how the principal, as an instructional leader, uses the professional learning community (PLC) process to support the development of teachers in creating an authentic approach to data analysis in order to improve instruction and support student learning.

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Electron sources: Past, present, and future

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100149659 ◽

1993 ◽

Vol 51 ◽

pp. 764-765

Author(s):

David C Joy

Keyword(s):

Glass Tube ◽

Source Size ◽

Electron Gun ◽

Operating Parameters ◽

Operational Parameters ◽

Electron Sources ◽

Difficult Decision ◽

Probe Size ◽

Wide Range ◽

Overall Performance

The electron source is the most important component of the Scanning electron microscope (SEM) since it is this which will determine the overall performance of the machine. The gun performance can be described in terms of quantities such as its brightness, its source size, its energy spread, and its stability and, depending on the chosen application, any of these factors may be the most significant one. The task of the electron gun in an SEM is, in fact, particularly difficult because of the very wide range of operational parameters that may be required e.g a variation in probe size of from a few angstroms to a few microns, and a probe current which may go from less than a pico-amp to more than a microamp. This wide range of operating parameters makes the choice of the optimum source for scanning microscopy a difficult decision.Historically, the first step up from the sealed glass tube ‘cathode ray generator’ was the simple, diode, tungsten thermionic emitter.

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