Applications and theoretical perspectives of artificial intelligence in the rate of penetration

Ossai, Chinedu I.; Duru, Ugochukwu Ilozurike

doi:10.1016/j.petlm.2020.08.004

Cited by 7 publications

(2 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many research work have focused on the applications of AI technologies within field development, for activities such as drilling, reservoir engineering and infrastrucutre. Machine learning models and its hybrids reveal successful applications in drilling for prediction of optimal mud properties and drilling parameters to improve safety, drilling efficiency and cost effectiveness [13][14][15][16][17]. Similarly in reservoir engineering and infrastructure, ML and its hybrids are used for estimation and optimization purposes in activities such as estimating dew point pressure and optimizing waterflooding which helps to maximize hydrocarbon production, optimize oil production and maximize finanacial profits [2,18,19].…”

Section: Ai In Oil and Gas Upstreammentioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

View full text Add to dashboard Cite

The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PDF

show abstract

Section: Ai In Oil and Gas Upstreammentioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

View full text Add to dashboard Cite

show abstract

“…On the other hand, mathematical models can only be developed for one valve, and when there are several valves in a well, a separate model must be developed for each valve. In recent years, many soft computing techniques and machine-learning methods, some hybridized with efficient optimization algorithms, have been adopted as powerful approaches to predict various parameters associated with complex systems in the oil and gas industry (Rabiei et al, 2015;Jovic et al, 2016;Wood, 2018;Yavari et al, 2018;Ashfari et al, 2019;Barbosa et al, 2019;Rashid et al, 2019;Sabah et al, 2019;Yilmaz et al, 2019;Elkatatny, 2020;Gamal et al, 2020;Ghorbani et al, 2020;Mehrad et al, 2020;Moazzeni and Khamehchi, 2020;Ossai and Duru, 2020;Somehsaraei et al, 2020;Abad et al, 2021;Hazbeh et al, 2021;Mardanirad et al, 2021;Mohamadian et al, 2021).…”

Section: Aquifer Oil Rim Low Permeabilitymentioning

confidence: 99%

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

Yavari

Khosravanian

Wood

et al. 2021

Adv. Geo-Energy Res.

View full text Add to dashboard Cite

An Automated Approach for Discriminating Hole Cleaning Efficiency While Predicting Penetration Rate in Egyptian Western Desert Wells

Saad,

Salem,

Mahmoud

2024

Arab J Sci Eng

View full text Add to dashboard Cite

Higher rate of penetration (ROP) indicates successful drilling operation but is not the only drilling success measure. However, Conventional ROP prediction methods focus on increasing ROP and neglect the hole cleaning state, which can be altered by ROP changes. Higher ROP in vertical and deviated wells may increase cutting concentration, leading to hole cleaning problems such as overpulling and stuck pipe. With this problem in mind, this paper utilized geological, rheological, and drilling data of 31 vertical wells across four oil fields located in the Egyptian Western Desert, developed intelligent ROP prediction model through back propagation neural network (BPNN), and compared the proposed BPNN results with an empirical model. Finally, the pattern recognition algorithms including discriminant analysis, support vector machines, and neural network pattern recognition (NNPR) were implemented to discriminate hole cleaning efficiency following the ROP prediction process. Recognition models were developed based on predicted ROP, bit wear rate, specific energy, and drilling fluid carrying capacity index to evaluate hole cleaning. The accuracy of the multi-strategy classifier was evaluated using area under curve, confusion matrix, and receiver operating characteristic. The BPNN model outperformed the empirical model in terms of linear correlation coefficient (R = 98.6%) and average absolute error (AAE = 5.5%). Additionally, the best classification performance was achieved using the NNPR algorithm with 91% accuracy and a cross-validation error equal to zero. For validity, the proposed approach predicted ROP and classified hole cleaning efficiency for new vertical well in adjacent oil field, resulting in a 6% improvement in ROP.

show abstract

Applications and theoretical perspectives of artificial intelligence in the rate of penetration

Cited by 7 publications

References 57 publications

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

An Automated Approach for Discriminating Hole Cleaning Efficiency While Predicting Penetration Rate in Egyptian Western Desert Wells

Contact Info

Product

Resources

About