Abstract: Systems and methods include techniques for using smart polymers. Units of smart polymers with heat sensitivity are inserted by a monitoring system into drilling fluid pumped into a well. The smart polymers are configured to be triggered by exposure to increasing levels of heat. 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 temperatures at a drill bit of the drilling operation is determined using continuous images, observed characteristics, and insertion timestamps, based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Suggested changes to be made to drilling parameters are provided.

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.

Abstract: Systems and methods include techniques for using smart polymers. Units of smart polymers with chloride ion sensitivity are inserted into drilling fluid pumped into a well. The smart polymers are configured to be triggered by chlorine ion 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 polymers are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of salinity in the drilling fluid 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 based on the estimate of the salinity.

Abstract: Systems and methods include techniques for using smart polymers. Units of smart polymers with per-hydrogen (pH) sensitivity are inserted into drilling fluid pumped into a well during drilling. The smart polymers are configured to be triggered by hydrogen ion concentrations. An insertion timestamp associated with each unit is stored and 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 pH values at a drill bit of the drilling operation is determined using the 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 be made to drilling parameters are suggested.

Abstract: Systems and methods include a computer-implemented method for determining well pressure. Units of pressure-responsive smart polymers are inserted into drilling fluid pumped into a well during a drilling operation. An insertion timestamp associated with each unit is stored indicating times that each unit was inserted. Continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera. An estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to be made to drilling parameters for the drilling operation are suggested based on the estimated BHP.

Abstract: Systems and methods include a computer-implemented method for determining well pressure. Units of pressure-responsive smart polymers are inserted into drilling fluid pumped into a well during a drilling operation. An insertion timestamp associated with each unit is stored indicating times that each unit was inserted. Continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera. An estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to be made to drilling parameters for the drilling operation are suggested based on the estimated BHP.