Intelligent kick warning based on drilling activity classification

Duan, Shiming; Song, Xianzhi; Cui, Yi; Xu, Zhengming; Liu, Wei; Fu, Jiasheng; Zhu, Zhaopeng; Li, Dayu

doi:10.1016/j.geoen.2022.211408

Cited by 9 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model utilized three omen parameters: annular flow rate, annular pressure, and annular temperature to classify and predict the severity of the overflow. [14] developed different input parameter combinations based on the type of operating conditions. This improved the accuracy of gas invasion warning.…”

Section: Intelligent Warning and Dynamic Control Of Drilling Risksmentioning

confidence: 99%

Application and Progress of Artificial Intelligence in Oilfield Drilling

Liu,

Zhang,

Qin

2023

AETR

View full text Add to dashboard Cite

Intelligent drilling and completion technology was an organic integration of drilling and completion engineering with advanced technologies such as artificial intelligence, big data, and cloud computing. It could achieve precise characterization, optimized decision-making, and closed-loop control of the oil and gas drilling and completion process. It was expected to significantly improve drilling and completion efficiency, reservoir drilling rate, and oil and gas recovery rate. It was a research frontier and hotspot in the field of oil and gas. This article constructed an artificial intelligence application scenario system for oil and gas drilling and completion based on engineering practice. It also elaborated on the current research status of intelligent drilling and completion theory and technology at home and abroad. Apart from that, the article summarized the challenges and key research directions faced by intelligent drilling and completion technology research. The aim was to provide a reference for accelerating basic theoretical research, technology promotion, and application of intelligent drilling and completion technology in China.

show abstract

Section: Intelligent Warning and Dynamic Control Of Drilling Risksmentioning

confidence: 99%