Implementation of a novel eccentric dog leg reamer in oil well drilling

Shokry, Amir; Elgibaly, Ahmed A.; Salem, Adel M.

doi:10.1007/s13202-021-01096-3

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Teal model was the base for Armenta model which considered the effect of the fluid hydraulics on the ROP 17 . Shokry et al developed their model for two cutting structure BHA based on the MSE concept when they considered NOV eccentric dog-leg reamer 18 .…”

Section: Introductionmentioning

confidence: 99%

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Shokry,

Elkatatny,

Abdulraheem

2023

Sci Rep

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Drilling rate of penetration (ROP) is one of the most important factors that have their significant effect on the drilling operation economically and efficiently. Motorized bottom hole assembly (BHA) has different applications that are not limited to achieve the required directional work but also it could be used for drilling optimization to enhance the ROP and mitigate the downhole vibration. Previous work has been done to predict ROP for rotary BHA and for rotary steerable system BHA; however, limited studies considered to predict the ROP for motorized BHA. In the present study, two artificial intelligence techniques were applied including artificial neural network and adaptive neurofuzzy inference system for ROP prediction for motorized assembly in the rotary mode based on surface drilling parameters, motor downhole output parameters besides mud parameters. This new robust model was trained and tested to accurately predict the ROP with more than 5800 data set with a 70/30 data ratio for training and testing respectively. The accuracy of developed models was evaluated in terms of average absolute percentage error, root mean square error, and correlation coefficient (R). The obtained results confirmed that both models were capable of predicting the motorized BHA ROP on Real-time. Based on the proposed model, the drilling parameters could be optimized to achieve maximum motorized BHA ROP. Achieving maximum ROP will help to reduce the overall drilling cost and as well minimize the open hole exposure time. The proposed model could be considered as a robust tool for evaluating the motorized BHA performance against the different BHA driving mechanisms which have their well-established models.

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Section: Introductionmentioning

confidence: 99%

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Shokry,

Elkatatny,

Abdulraheem

2023

Sci Rep

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Deployment of an Eccentric Borehole Conditioning Tool Yields Significant Drilling Efficiency Improvements

Bailey

Roberts

Suleiman

et al. 2023

Day 1 Sun, February 19, 2023

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Wellbore conditioning has become prominent in recent years as operators strive to reduce flat time costs. This paper examines how an eccentric wellbore conditioning tool (WCT) improves drilling performance and reduces flat time in challenging sections. Torque and drag are common challenges in extended laterals, with micro-doglegs and ledges being major culprits, especially in hard carbonate formations. The performance of the WCT is benchmarked in terms of flat time components: trip speed, system torque, rate of penetration (ROP), casing operations, and on-bottom performance. The paper also scrutinizes the allowable parameter envelope for wells drilled with and without the WCT. Laboratory testing was conducted to observe the behavior and effects of the eccentric WCT when confronted by the major wellbore conditioning challenges. Following these initial laboratory tests, a proprietary software program was created to determine the WCT's ideal bottom hole assembly (BHA) placement for field trials. Several field trials were conducted to validate the placement software's accuracy as well as the key benefits achieved by the WCT. Development testing has proven that the WCT's capabilities reliably improve wellbore quality and reduce overall torque and drag. Also, a proprietary software program confirmed the most effective WCT placement. Overall, there was a net flat time reduction and a marked improvement in ROP—up to 35% when directly compared with and without the WCT. Reduced system torque and less stress on downhole tools enable a wider and, more importantly, higher drilling parameter window. A new generation of eccentric WCTs has been introduced to solve a legacy drilling pain. Poor wellbore conditioning contributes to significant flat time costs for operators worldwide. A proven, effective solution has been long overdue; however, the WCT has shown to be a cost-effective tool, improving flat time and drilling performance. The benchmarking and exploration of drilling performance and reduced flat time provided by wellbore conditioning tools will allow others to exploit lessons learned and recapture drilling efficiency.

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Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

Youssef,

Gowida,

Elkatatny

et al. 2024

Day 2 Wed, May 08, 2024

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Predicting and optimizing the drilling rate of penetration (ROP) poses a significant challenge due to its dependence on various factors, prompting increased attention towards achieving precise ROP estimations given its direct influence on overall drilling expenses. Among the factors influencing ROP, the driving mechanism of the bottom hole assembly (BHA) plays a pivotal role. Motorized BHAs offer versatile applications beyond directional drilling, including optimization of ROP and mitigation of downhole vibration. While several models have been proposed to forecast ROP for rotary and rotary steerable system BHAs, limited attention has been directed towards motorized BHAs. In this study, a novel artificial intelligence (AI)-based model employing gradient boosting regression (GBR) was developed to predict ROP for motorized BHAs, leveraging surface drilling parameters, mud characteristics, and motor output features. The dataset used for model training, validation, and testing was sourced from six wells spanning two adjacent fields in the Egyptian Western Desert, comprising over 5,800 data points. Mean absolute percentage error (MAPE) served as an evaluation metric for prediction accuracy, while the correlation coefficient (R) quantified the extent of agreement between real and predicted ROP values. Results demonstrated that the GBR model accurately estimated ROP for motorized BHAs, exhibiting a high correlation (R of 0.95) between predicted and real values. The GBR-based model consistently performed well without exhibiting underfitting or overfitting issues. Furthermore, the developed model enables exploration of the impact of different drilling parameters on motorized BHA ROP, thereby facilitating ROP optimization, reduction of open hole exposure duration, and overall drilling cost minimization.

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Implementation of a novel eccentric dog leg reamer in oil well drilling

Cited by 3 publications

References 14 publications

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Deployment of an Eccentric Borehole Conditioning Tool Yields Significant Drilling Efficiency Improvements

Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

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