Evaluation of PDC bit ROP models and the effect of rock strength on model coefficients

Soares, Cesar; Daigle, Hugh; Gray, K. E.

doi:10.1016/j.jngse.2016.08.012

Cited by 64 publications

(12 citation statements)

References 2 publications

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“…Using traditional ROP models is a conventional approach in ROP management studies (Bezminabadi et al, 2017). Either as an assistant model for artificial intelligence algorithms or as a basic ROP management tool, they have been used repeatedly (Bahari and Baradaran Seyed, 2007a;Rastegar et al, 2008;Bataee et al, 2010;Sui et al, 2013;Formighieri and Freitas, 2015;Kutas et al, 2015;Soares et al, 2016;Hegde et al, 2019). In this section, the history of developing different ROP models is reviewed and they are categorized in different subsections of RCB and PDC bits and also empirical correlations.…”

Section: The History Of Rop Modelsmentioning

confidence: 99%

“…In this method, instead of determining fixed values for the model coefficients, creating probabilistic distributions for these parameters were replaced which improved the performance of this ROP model significantly. Soares et al (2016) conducted a performance comparison study in Williston Basin, located in North Dakota. They compared the performance of three ROP models including Bingham (1964), Hareland and Rampersad (1994) and Motahhari et al (2010) in several formations.…”

Section: Almentioning

confidence: 99%

See 1 more Smart Citation

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

Najjarpour

Jalalifar

Norouzi-Apourvari

2021

Adv. Geo-Energy Res.

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Rate of penetration management is a matter of importance in drilling operations and it has been used in some research studies. Although conventional approaches for rate of penetration management are mainly focused on analytical and semi-analytical models, several correlations have also been developed for this purpose. The history of rate of penetration management studies extends back more than half a century and ever since, research interest in this concept has never declined, making it a focus of industry and academic studies. In this article, some of these studies are reviewed to achieve a better understanding of rate of penetration management concept, its financial benefits and also its research capacities. This review reveals the most common rate of penetration management methods which applied analytical, semi-analytical and empirical correlations in different fields around the world. In other words, the main purpose of this study is to evaluate the research studies in which different models and correlations have been used as the main approach for rate of penetration management. Based on the results of this review, the best models for performing rate of penetration management studies and the best objective functions for optimization algorithms are introduced.

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Section: The History Of Rop Modelsmentioning

confidence: 99%

Section: Almentioning

confidence: 99%

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

Najjarpour

Jalalifar

Norouzi-Apourvari

2021

Adv. Geo-Energy Res.

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“…Originally, different traditional models were optimized to evaluate the ROP; these models were developed from regression analysis to assess the ROP based on various inputs, the accuracy of these models is significantly affected by the inputs considered [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

et al. 2022

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This study presented an empirical correlation to estimate the drilling rate of penetration (ROP) while drilling into a sandstone formation. The equation developed in this study was based on the artificial neural networks (ANN) which was learned to assess the ROP from the drilling mechanical parameters. The ANN model was trained on 630 datapoints collected from five different wells; the suggested equation was then tested on 270 datapoints from the same training wells and then validated on three other wells. The results showed that, for the training data, the learned ANN model predicted the ROP with an AAPE of 7.5%. The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with AAPE of 8.1%. The equation was then validated on three wells, and it determined the ROP with AAPEs of 9.0%, 10.7%, and 8.9% in Well-A, Well-B, and Well-D, respectively. Compared with the available empirical equations, the equation developed in this study was most accurate in estimating the ROP.

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“…The Bourgoyne and Young model considers a variety of influencing factors-mechanical parameters, hydraulic parameters, and formation parameters-and has been widely used in the prediction of ROP (Bahari and Baradaran Seyed 2007;Bahari et al 2009;Hua 2010;Rahimzadeh et al 2011;Nascimento et al 2015;Ahmed and Ibrahim 2019). Soares et al (2016) compared Hareland and Rampersad's model (Hareland and Rampersad 1994) and Motahhari's model Qinghai Bureau of Environmental Geology Exploration, Xining 810000, China (Motahhari 2008;Motahhari et al 2010) in three different sandstone formations, concluding that the former works best. Al-Abduljabbar et al (2019) established a robust ROP model using 7000 real-time data measurements from a carbonate formation.…”

Section: Introductionmentioning

confidence: 99%

A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang

Liu

Gao

Zhao

et al. 2021

J Petrol Explor Prod Technol

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In gas drilling operations, the rate of penetration (ROP) parameter has an important influence on drilling costs. Prediction of ROP can optimize the drilling operational parameters and reduce its overall cost. To predict ROP with satisfactory precision, a stacked generalization ensemble model is developed in this paper. Drilling data were collected from a shale gas survey well in Xinjiang, northwestern China. First, Pearson correlation analysis is used for feature selection. Then, a Savitzky-Golay smoothing filter is used to reduce noise in the dataset. In the next stage, we propose a stacked generalization ensemble model that combines six machine learning models: support vector regression (SVR), extremely randomized trees (ET), random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGB). The stacked model generates meta-data from the five models (SVR, ET, RF, GB, LightGBM) to compute ROP predictions using an XGB model. Then, the leave-one-out method is used to verify modeling performance. The performance of the stacked model is better than each single model, with R2 = 0.9568 and root mean square error = 0.4853 m/h achieved on the testing dataset. Hence, the proposed approach will be useful in optimizing gas drilling. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the relevant ROP parameters.

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Evaluation of PDC bit ROP models and the effect of rock strength on model coefficients

Cited by 64 publications

References 2 publications

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang

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