Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network

Rooki, Reza; Rakhshkhorshid, M.

doi:10.1016/j.ejpe.2016.08.001

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…Ulker and Sorgun (2016) ob-tained lower cuttings bed thickness prediction errors with their ANN model compared to MLR, SVM, and K-nearest neighbor (KNN) models applied to an experimentally-derived dataset for wellbore with inclinations from 60°to 90°. Rooki and Rakhshkhorshid (2017) applied a radial basis function neural network to model cuttings concentration during underbalanced drilling from drilling variables. Agwu et al (2020) developed an ANN model to predict drilling cuttings settling velocity.…”

Section: Machine Learning For Multivariate Cuttings-bed Dataset Analysismentioning

confidence: 99%

Variable interaction empirical relationships and machine learning provide complementary insight to experimental horizontal wellbore cleaning results

Wood

2023

Adv. Geo-Energy Res.

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Long horizontal wellbore sections are now a key requirement of oil and gas drilling, particularly for tight reservoirs. However, such sections pose a unique set of borehole-cleaning challenges which are quite distinct from those associated with less inclined wellbores. Experimental studies provide essential insight into the downhole variables that influence borehole cleaning in horizontal sections, typically expressing their results in multivariate empirical relationships with dimensionless cuttings bed thickness/concentration (H%). This study demonstrates how complementary empirical H% relationships focused on pairs of influential variables can be obtained from published experimental data using interpolated trends and optimizers. It also applies five machine learning algorithms to a compiled multivariate (10-variable) interpolated dataset to illustrate how reliable H% predictions can be derived based on such information. Seven optimizer-derived empirical relationships are derived using pairs of influential variables which are capable of predicting H% with root mean squared errors of less than 1.8%. The extreme gradient boosting model provides the lowest H% prediction errors from the 10-variable dataset. The results suggest that in drilling situations where sufficient, locally-specific, information for multiple influential variables is available, machine learning methods are likely to be more effective and reliable at predicting H% than empirical relationships. On the other hand, in drilling conditions where information is only available for a limited number of influential variables, empirical relationships involving pairs of influential variables can provide valuable information to assist with drilling decisions.

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Section: Machine Learning For Multivariate Cuttings-bed Dataset Analysismentioning

confidence: 99%

Variable interaction empirical relationships and machine learning provide complementary insight to experimental horizontal wellbore cleaning results

Wood

2023

Adv. Geo-Energy Res.

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“…They compared these two data-driven methods to a mechanistic model and showed that ANN provided an overall better accuracy with predictions. In a later study, Rooki and Rakhshkhorshid [30] presented a radial basis function network (RBFN) method to predict the cuttings concentration during underbalanced drilling. They compared RBFN to a more conventional backpropagation neural network (BPNN).…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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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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“…Rooki [42,43] used General Regression Neural Network (GRNN) and BPNN for predicting pressure drop using the Herschel-Bulkley fluid model. Rooki and Rakhshkhorshid [44] used Radial Basis Neural Network (RBFN) for determining the hole cleaning condition during foam drilling. The fuzzy logics were used for mud density estimation by Ahmadi et al [45].…”

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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Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network

Cited by 13 publications

References 22 publications

Variable interaction empirical relationships and machine learning provide complementary insight to experimental horizontal wellbore cleaning results

Variable interaction empirical relationships and machine learning provide complementary insight to experimental horizontal wellbore cleaning results

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

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

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