The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 20, 2025

Filed:

Oct. 05, 2022
Applicant:

Saudi Arabian Oil Company, Dhahran, SA;

Inventors:

Mohammed Albassam, Alkhobar, SA;

Arturo Magana Mora, Dhahran, SA;

Chinthaka Pasan Gooneratne, Dhahran, SA;

Mohammad Aljubran, Sayhat, SA;

Peter Boul, Houston, TX (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
E21B 49/08 (2006.01); C09K 8/24 (2006.01); E21B 21/06 (2006.01); G01N 33/28 (2006.01);
U.S. Cl.
CPC ...
E21B 49/0875 (2020.05); C09K 8/24 (2013.01); G01N 33/2841 (2013.01); E21B 21/068 (2013.01);
Abstract

Systems and methods include techniques for using smart polymers. Units of smart polymers with hydrogen sulfide (H2S) sensitivity are inserted by a monitoring system into drilling fluid pumped into a well. The smart polymers are configured to be triggered by increasing H2S concentrations. An insertion timestamp associated with each unit is stored. Each insertion timestamp indicates a time that each unit was inserted. Continuous images and observed characteristics of returning mud exiting through an annulus of the well and containing the units of smart polymer are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of H2S levels at a drill bit of the drilling operation is determined using continuous images, observed characteristics, and insertion timestamps, and based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to drilling parameters are suggested.


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