Determination of drilling fluid rheology under downhole conditions by using real-time distributed pressure data

Vajargah, Ali Karimi; Oort, Eric van

doi:10.1016/j.jngse.2015.04.004

Cited by 48 publications

(17 citation statements)

References 27 publications

(31 reference statements)

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“…Mokhtari et al [29] studied the impact on both velocity profiles and pressure loss across annulus by eccentricity. Vajargah and Van Oort et al [30] concluded that laminar flow is comparatively affected more by eccentricity in comparison to turbulent flow. Erge et al [31] suggested that while circulating, YPL fluids eccentricity has a dominant effect on annular pressure loss.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

et al. 2020

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Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel-Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

et al. 2020

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“…In this case, rapid improvement of parameters like flow rate, annular velocity, pipe rotation, ROP, mud weight (density), mud rheology and cutting size/shape should be performed. These parameters directly affect the standpipe pressure and frictional pressure down the hole [25]. The first and most effective parameter in this relation is the mud density.…”

Section: Net Rising Calculationsmentioning

confidence: 99%

Wiper Trips Effect on Wellbore Instability Using Net Rising Velocity Methods

Darwesh¹,

Rasmussen²,

Al‐Ansari³

2018

TOPEJ

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Background:This paper discusses the wiper trip effects on well instability in shale formations.Objectives:Problematic shale interval sections have been studied for the time spent on the wiper trip operations. Lifting efficiency and well wall instability change with the time analyzed. Detailed drilling operation, formation heterogeneity, rheological and filtration characteristics of polymer water-based mud are discussed. Physical and chemical properties of the drilled formation and drilling fluid are also studied.Materials and Methods:Wiper trips are analyzed using a typical drawing program to find the relations between the most controllable parameters. For that, two calculation models have been implemented to find the net rising cutting particles velocity in the annular. The relation between the net rising velocity and wiper trips is analyzed. Laboratory works have been done to support the findings of field work.Results:Strong relations have been found between the wiper trip impacts and lithology types of the penetrated shale.Conclusion:A modified drilling program is proposed in relation to changes in casing setting depth and drilling fluid properties that make the operations more efficient in cost and time.

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“…We should mention that there are other models for drilling fluids for high pressure and high temperature (HPHT) conditions. Most of these models are based on the Herschel-Bulkley model (see Khan and May [48], Salehi et al [49], Vajargah and van Oort [50]). Our model not only includes the effects of temperature and shear-rate dependency of the viscosity, but also allows for a volume fraction dependent viscosity and thermal conductivity.…”

Section: Stress Tensormentioning

confidence: 99%

“…Our model not only includes the effects of temperature and shear-rate dependency of the viscosity, but also allows for a volume fraction dependent viscosity and thermal conductivity. Next we discuss the modeling of the particle flux N. Khan and May [48], Salehi et al [49], Vajargah and van Oort [50]). Our model not only includes the effects of temperature and shear-rate dependency of the viscosity, but also allows for a volume fraction dependent viscosity and thermal conductivity.…”

Section: Stress Tensormentioning

confidence: 99%

Heat Transfer in a Drilling Fluid with Geothermal Applications

Aubry

Antaki

et al. 2017

Energies

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Abstract:The effects of various conditions on the fluid flow, particle migration and heat transfer in non-linear fluids encountered in drilling and geothermal applications are studied. We assume that the drilling fluid is a suspension composed of various substances, behaving as a non-linear complex fluid, where the effects of particle volume fraction, shear rate, and temperature on the viscosity and thermal diffusivity are considered. The motion of the particles is described using a concentration flux equation. Two problems are studied: flow in a vertical pipe and flow between two (eccentric) cylinders where the inner cylinder is rotating. We consider effects of earth temperature, the rotational speed of the inner cylinder, and the bulk volume fraction on the flow and heat transfer.

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Determination of drilling fluid rheology under downhole conditions by using real-time distributed pressure data

Cited by 48 publications

References 27 publications

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

Wiper Trips Effect on Wellbore Instability Using Net Rising Velocity Methods

Heat Transfer in a Drilling Fluid with Geothermal Applications

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