Application in Drilling Tool Combination Aided Design Based on Data Intelligence
- Qi Zhu (CNPC BoHai Drilling Engineering Company Limited)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 7.2 Risk Management and Decision-Making, 1.12 Drilling Measurement, Data Acquisition and Automation, 1.10 Drilling Equipment, 7.2.1 Risk, Uncertainty and Risk Assessment, 1.6 Drilling Operations, 7 Management and Information, 1.12.6 Drilling Data Management and Standards, 1.11 Drilling Fluids and Materials
- Aided Design, Drilling Tool Assembly, Data Intelligence, Application
- 20 in the last 30 days
- 22 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Drilling, as a direct and effective method of opening oil and gas layers, has been widely used. A reasonable combination of drilling tools plays a key role in increasing the rate of mechanical drilling, reducing drilling costs, and reducing downhole accidents. Conventional drilling relies on years of experience of on-site workers and reference to the operation of drilling wells, making use of drilling tools and lacking scientific basis.
However, the reservoir situation is erratic, the unknown factors are very numerous, unpredictable, and the difficulty of drilling is increased. Drilling into unknown reservoirs, especially high-temperature and high-temperature risk wells, poses a huge threat to the lives of workers on site. Conventional drilling of known reservoirs will also encounter unknown problems such as drilling distance growth, stuck drilling, drilling tools falling, increased inclination, and deviation from the intended target position, which seriously reduces drilling efficiency, increases operating time, risk and drilling difficulty affected by the reasonable use of the drilling tool combination.
With the development and application of computational intelligence, through the accumulation of massive geological property data, reservoir structure data, drilling tool parameters, construction data, drilling fluid parameters and other drilling data, intelligent drilling is used to predict unknown drilling information which can reduce the risk of drilling and improve drilling efficiency.
In this paper, the work mode of "data running first, operation post" is used to further strengthen the application of drilling tools combination to improve the rate of mechanical drilling and reduce downhole problems.
|File Size||1 MB||Number of Pages||10|
Christine I. Noshi and Jerome J. Schubert. 2018. The Role of Machine Learning in Drilling Operations; A Review. Presented at the SPE/AAPG Eastern Regional Meeting, Pittsburgh, Pennsylvania, USA, 7-11 October. SPE-191823-18ERM-MS. 10.2118/191823-18ERM-MS.
Arash Shadravan, Mohammadali Tarrahi, Mahmood Amani. Intelligent Tool To Design Drilling, Spacer, Cement Slurry, and Fracturing Fluids by Use of Machine-Learning Algorithms. SPE Drilling & Completion. 2017, 32(02): 1–10. 10.2118/175238-PA.
O. Akimov, A. Hohl, H. Oueslati. . 2018. Evolution of Drilling Dynamics Measurement Systems. Presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, UAE, 29-31 January. SPE-189431-MS. 10.2118/189431-MS.
Eric Maidla, William Maidla, John Rigg 2018. Drilling Analysis Using Big Data has been Misused and Abused. Presented at the IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, USA, 6-8 March. SPE-189583-MS. 10.2118/189583-MS.
Zheren Ma, Ali Karimi Vajargah, Hanna Lee 2018. Applications of Machine Learning and Data Mining in SpeedWise® Drilling Analytics: A Case Study. Presented at the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, 12-15 November. SPE-193224-MS. 10.2118/193224-MS.
Abdullah H. AlBar, Bader M. Alotaibi, Hasan M. Asfoor 2018. A Journey Towards Building Real- Time Big Data Analytics Environment for Drilling Operations: Challenges and Lessons Learned. Presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23-26 April. SPE-192285-MS. 10.2118/192285-MS.
David Castiñeira, Robert Toronyi, Nansen Saleri 2018. Machine Learning and Natural Language Processing for Automated Analysis of Drilling and Completion Data. Presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23-26 April. SPE-192280-MS. 10.2118/192280-MS.
Mahmoud Elzenary, Salaheldin Elkatatny, Khaled Z. Abdelgawad 2018. New Technology to Evaluate Equivalent Circulating Density While Drilling Using Artificial Intelligence. Presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23-26 April. SPE-192282-MS. 10.2118/192282-MS.
Christine I. Noshi, Ahmed I. Assem, Jerome J. Schubert. 2018. The Role of Big Data Analytics in Exploration and Production: A Review of Benefits and Applications. Presented at the SPE International Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, 10-12 December. SPE-193776-MS. 10.2118/193776-MS.
Vitaliy Koryabkin, Sergei Stishenko, Pavel Kolba. 2018. Presented at the Application of the Combined Real-Time Petrophysical and Geosteering Model to Increase Drilling Efficiency. SPE Russian Petroleum Technology Conference, Moscow, Russia, 15-17 October. SPE-191689-18RPTC-MS. 10.2118/191689-18RPTC-MS.