US 12,129,755 B2
Information extraction from daily drilling reports using machine learning
Mohamed Saad Kisra, Calgary (CA); Francisco Jose Gomez, Abingdon (GB); Karsten Fischer, Aachen (DE); Ivan Diaz Granados Pertuz, Abingdon (GB); and Athithan Dharmaratnam, Abingdon (GB)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Appl. No. 17/753,759
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
PCT Filed Sep. 14, 2020, PCT No. PCT/US2020/070538
§ 371(c)(1), (2) Date Mar. 14, 2022,
PCT Pub. No. WO2021/051141, PCT Pub. Date Mar. 18, 2021.
Claims priority of provisional application 62/899,997, filed on Sep. 13, 2019.
Prior Publication US 2022/0372866 A1, Nov. 24, 2022
Int. Cl. E21B 47/12 (2012.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01)
CPC E21B 47/12 (2013.01) [G06F 40/284 (2020.01); G06F 40/30 (2020.01); E21B 2200/20 (2020.05); E21B 2200/22 (2020.05)] 20 Claims
OG exemplary drawing
 
1. A method for extracting information regarding a drill site, the method comprising:
forming one or more documents having raw data regarding a well site, wherein the raw data includes one or more raw comments directed to operational details of the well site;
extracting the raw data from the one or more documents regarding the well site to produce extracted raw data;
pre-processing the extracted raw data by removing one or more of ambiguity, artifacts, and formatting errors from the one or more raw comments to produce pre-processed data;
extracting topics data from the pre-processed data using a natural language processing (NLP) algorithm to produce extracted topics data, the NLP algorithm including a first NLP model for extracting the topics data;
extracting measurement data from the pre-processed data using the NLP algorithm to produce extracted measurement data, the NLP algorithm including a second NLP model for extracting the measurement data;
aggregating the extracted topics data and the extracted measurement data to form a set of discrete data points per comment to produce aggregated data;
identifying one or more discrete data points from the aggregated data; and
applying the one or more discrete data points as calibration points for geomechanical post drill analysis used to generate drilling risks and maps to improve well design or mitigation strategies in a field or area.