Di Qingyun, Li Shouding, Fu Changmin, et al. 2021. Intelligent steering drilling technology method based on cloud big data[J]. Journal of Engineering Geology, 29(1): 1178-1185. doi: 10.13544/j.cnki.jeg.2021-0055.
    Citation: Di Qingyun, Li Shouding, Fu Changmin, et al. 2021. Intelligent steering drilling technology method based on cloud big data[J]. Journal of Engineering Geology, 29(1): 1178-1185. doi: 10.13544/j.cnki.jeg.2021-0055.

    INTELLIGENT STEERING DRILLING TECHNOLOGY METHOD BASED ON CLOUD BIG DATA

    • Steering drilling technology is one of the most important technologies in the global petroleum industry in the 21st century, and it is also a key component of horizontal drilling, the core technology of the American "The Shale Gas Revolution". At present, the main research goal of steering drilling is to increase the drilling speed, reduce the drilling time and risk, and intelligence is an important way to achieve this goal. The article analyzes the application of big data and artificial intelligence in the petroleum industry at home and abroad, establishes a cloud big data intelligent steering drilling method framework, proposes an artificial intelligence inversion method for logging while drilling parameters, and points out the way to realize the management of cloud big data and intelligent algorithms, and draw the following conclusions: (1)The intelligent guided drilling method based on cloud big data mainly includes the things perception layer, the big data storage layer and the cloud platform decision layer. The things perception layer realizes the collection and transmission of key information of the wellsite to the big data center. The big data storage center is mainly responsible for data storage and cloud management. The cloud platform decision layer relies on the massive data in the big data center to perform cloud ground software control, artificial intelligence decision-making, and cloud platform management. (2)Select six geophysical parameters such as SP, GR, DEN, AC, CNL, and RT, and use different Machine Learning algorithms to build models to realize the independent identification of formation lithology. The Decision Tree model and the Random Forest model have an accuracy of 0.81 and 0.89 respectively, forming a set of schemes that can quickly and automatically describe the classification of lithological characteristics. (3)The cloud platform management decision is mainly used to decode real-time upload data downhole, and obtain drilling trajectories and logging curves. The cloud artificial intelligence decision-making module performs intelligent inversion and prediction of stratum and drilling parameters, realizes intelligent correction of drilling trajectories and intelligent optimization of drilling parameters, and ensures the accuracy and speed of drilling of intelligent steering engineering.
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