Simulation on Cuttings Transport with Drillpipe Rotation in Highly-Inclined Well Section

Xiao-le, Guo; Li, Wenyin; Liu, Jilin

doi:10.12783/dtetr/amma2017/13334

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Sufficient sweep of drilled cuttings from hole and transport it to the surface is a main challenge in oil well drilling, for vertical well operation this issue was well studied since that any parameters can reduce the slip velocity of the cuttings can efficiently influence on the hole cleaning , but for the deviated well operation hole cleaning is still a big challenge [4].So proper strategy of hole cleaning is an important to get good drilling operations because insufficient hole cleaning may lead to many problems such as [5]: a)…”

Section: -Introductionmentioning

confidence: 99%

A Comparative Study of Pressure Losses and Hole Cleaning Efficiency of Water and Polymer solutions in Horizontal Wells

Mohammed¹,

Alhaleem²

2019

IJCPE

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The main objective of this study is to experimentally investigate the effect of the CMC polymeric drag reducer on the pressure drop occurred along the annulus of the wellbore in drilling operation and investigate the optimum polymer concentration that give the minimum pressure drop. A flow loop was designed for this purpose consist from 14 m long with transparent test section and differential pressure transmitter that allows to sense and measure the pressure losses along the test section. The results from the experimental work show that increasing in polymer concentration help to reduce the pressure drop in annulus and the optimum polymer concentration with the maximum drag reducing is 0.8 kg/m3. Also increasing in flow rate and corresponding fluid velocity in the gap of the annulus helped to reduce the pressure losses due to fluid flow.

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Section: -Introductionmentioning

confidence: 99%

A Comparative Study of Pressure Losses and Hole Cleaning Efficiency of Water and Polymer solutions in Horizontal Wells

Mohammed¹,

Alhaleem²

2019

IJCPE

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Applications of Machine Learning Methods to Predict Hole Cleaning in Horizontal and Highly Deviated Wells

Mendez

Ahmed

Karami

et al. 2023

SPE Drilling &Amp; Completion

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Summary Machine learning (ML) has become a robust method for modeling field operations based on measurements. For example, wellbore cleanout is a critical operation that needs to be optimized to enhance the removal of solids to reduce problems associated with poor hole cleaning. However, as wellbore geometry becomes more complicated, predicting the cleaning performance of fluids becomes more challenging. As a result, optimization is often difficult. Therefore, this research focuses on developing a data-driven model for predicting hole cleaning in deviated wells to optimize drilling performance. More than 500 flow loop measurements from eight studies are used to formulate a suitable ML model to forecast hole cleanout in directional wells. Measurements were obtained from hole-cleaning experiments that were conducted using different loop configurations. Experiments ranged in test-section length from 22 to 100 ft, in hole diameter from 4 to 8 in., and in pipe diameter from 2 to 4.5 in. The experiments provided measured equilibrium bed height at a specific flow rate for various fluids, including water-based and synthetic-based fluids and fluids containing fibers. Several relevant test parameters, including fluid and cutting properties, well inclination, and drillstring rotation speed (drillpipe rev/min), were also considered in the analysis. The collected data have been analyzed using the Cross-Industry Standard Process for Data Mining. This paper is unique because it systematically evaluates various ML models for their ability to describe hole cleanout processes. Six different ML techniques: boosted decision tree (BDT), random forest (RF), linear regression, multivariate adaptive regression spline (MARS), neural networks, and support vector machine (SVM) have been evaluated to select the most appropriate method for predicting bed thickness in a wellbore. Also, we compared the predictions of the selected ML method with those of a mechanistic model for cases without drillstring rotation. Finally, using the ML model, a parametric study has been conducted to examine the impact of various parameters on the cleanout performance of selected fluids. The results show the relative influence of different variables on the prediction of cuttings bed. Accordingly, flow rate, drillpipe rev/min, and fluid behavior index have a strong impact on dimensionless bed thickness, while other parameters such as fluid consistency index, solids density and diameter, fiber concentration, and well inclination angle have a moderate effect. The BDT algorithm has provided the most accurate prediction with an R2 of 92%, a root-mean-square error (RMSE) of 0.06, and a mean absolute error (MAE) of roughly 0.05. A comparison between a mechanistic model and the selected ML technique shows that the ML model provided better predictions.

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Simulation on Cuttings Transport with Drillpipe Rotation in Highly-Inclined Well Section

Cited by 2 publications

References 12 publications

A Comparative Study of Pressure Losses and Hole Cleaning Efficiency of Water and Polymer solutions in Horizontal Wells

A Comparative Study of Pressure Losses and Hole Cleaning Efficiency of Water and Polymer solutions in Horizontal Wells

Applications of Machine Learning Methods to Predict Hole Cleaning in Horizontal and Highly Deviated Wells

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