A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

Osarogiagbon, Augustine Uhunoma; Muojeke, Somadina; Venkatesan, R.; Khan, Faisal; Gillard, P.

doi:10.1016/j.psep.2020.05.046

Cited by 35 publications

(2 citation statements)

References 13 publications

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“…Different input parameters have been tested which include majorly prior highlighted primary and secondary kick indicators. Different models have been tested such as Bayesian classifier, decision tree, k-nearest neighbor, random forest, support vector machine, different neural networks, and autoregressive models [31,32]. Extensive gas-kick datasets were generated autonomously via 108 tests from a pilot-scale test well experimental setup equipped with a complete drilling system and a comprehensive mud logging system for surface monitoring of relevant drilling and geological parameters complimented with Doppler wave sensors just above the BOP for riser monitoring of gas migration and downhole pressure monitoring via pressure gauges.…”

Section: The Use Of Numerical Modeling and Machine Learning To Aid Ea...mentioning

confidence: 99%

Advances in Well Control: Early Kick Detection and Automated Control Systems

Olamigoke¹,

James²

2022

Drilling Engineering and Technology - Recent Advances New Perspectives and Applications

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The devastating impact of the Macondo blowout incident has underscored the need for effective well barriers, early kick detection systems, and increased automation of well-control operations toward successful drilling and completion operations particularly in deep offshore environments. Early kick detection systems should be capable of detecting a gas influx both during drilling and tripping operations regardless of the drilling fluid system with minimal false-negative alarms, while automated control systems regain well-control eliminating delays or omissions due to human error. In this chapter, developments in the deployment of early kick detection and automated control systems in conventional and managed pressure drilling operations are reviewed. We discuss the use and placement of surface sensors such as the Coriolis flowmeter, smart flowback fingerprinting when the rig pumps are off, real-time gas monitoring along the marine riser and downhole measurements complimented with machine learning algorithms for early kick detection. We then focus on the application of automated well-control systems for managed pressure drilling operations for which gas kicks are circulated without stopping the pumps or shutting in the well and in conventional well operations requiring intelligent tool joint space-out prior to well shut in especially for deep offshore operations.

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Section: The Use Of Numerical Modeling and Machine Learning To Aid Ea...mentioning

confidence: 99%

Advances in Well Control: Early Kick Detection and Automated Control Systems

Olamigoke¹,

James²

2022

Drilling Engineering and Technology - Recent Advances New Perspectives and Applications

View full text Add to dashboard Cite

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“…At present, the foreign research trend in overflow early warning is, on the basis of the existing overflow detection equipment, constantly explore new overflow detection equipment, and combined with the cutting-edge artificial intelligence technology, explore new, able to carry out automated, intelligent comprehensive analysis and judgement of the measurement information overflow intelligent early warning methods. The intelligent early warning methods used abroad mainly include the early overflow warning method based on Bayesian decision framework [2], the overflow warning method based on fully connected network [3][4] [5] and the overflow warning method based on long and shortterm memory network [6] [7] and so on.…”

Section: Introductionmentioning

confidence: 99%

Overflow Intelligent Early Warning System Based on BP Neural Networks

Yang,

Wang,

Liu

2023

AJST

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Overflow is one of the most serious drilling accidents that affect the safety of drilling construction. The analysis of integrated logging parameters and the judgement of overflow situation in China still remain in the stage of "manual judgement" and "threshold warning". Based on this, we propose the method of combining the real time measurement information of comprehensive logging instrument with artificial intelligence technology, and design a BP neural network based intelligent early warning software system using SQL Server 2008 database management platform and C# program development language. Through the experimental test, the system runs well, the timeliness of overflow early warning is good, the accuracy of early warning results is high, and it can meet the needs of field application, and it can provide effective technical support for the field overflow early warning, and it has a better prospect of field application.

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A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

101

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

show abstract

A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

Cited by 35 publications

References 13 publications

Advances in Well Control: Early Kick Detection and Automated Control Systems

Advances in Well Control: Early Kick Detection and Automated Control Systems

Overflow Intelligent Early Warning System Based on BP Neural Networks

A systematic review of data science and machine learning applications to the oil and gas industry

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