Pattern Lab

Technical Paper

Optimize Well Planning and Predict Drilling Risk with AI

April 15, 2021

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Society: SPE, April 15, 2021


Gain deeper insights for better and safer well construction

In this technical paper Schlumberger and ADNOC illustrate the impact of AI based solutions to automatically extract information from a wide range of data sources for effective offset well analysis. 

Integrating multi-disciplinary data, which comprises of historical drilling events and risks, as well as offset well information and up to date subsurface models, is required to ensure effective collaboration between subsurface and well construction teams, with the lowest level of uncertainty. Extracting key knowledge from such a variety of data-sources and reports can be a lengthy process and usually remains a very manual task.

The solution defined by this paper systematically extracts, validates and integrates, information related to well trajectories, completion, historical events, petrophysical data, geo-mechanical data, and technical reports from relevant offset wells. Analytics results are kept alive by ingesting data continuously, enabling effective well planning, predicting operational hazards and planning mitigation, and ensuring optimal drilling parameterization

Learn about:

  • Natural language processing and machine learning applied to drilling

  • Automated offset well analysis

  • Impact of digital on drilling performance



Download the PDF to read the full article.


Authors: Richard Mohan, ADNOC HQ; Ahmad Hussein, Schlumberger; Arwa Mawlod and Bashaer Al Jaberi, ADNOCOnshore; Velizar Vesselinov, Schlumberger; Fouad Abdul Salam and Khaled Al Hadidy, ADNOC Onshore; AnikPal, Schlumberger; Haifa Al Yazeedi, ADNOC HQ; Khadija Al Daghar, ADNOC Onshore; Hussein Mustapha andAli Razouki, Schlumberger; Imad Al Hamlawi and Bassem El Yossef, ADNOC HQ