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.

Abstract: Automated drill cutting monitoring system includes a digital imaging device mounted to a shale shaker of a wellbore drilling assembly and a computer system. The digital imaging device captures digital images of solid objects released when drilling a subterranean zone. The computer system receives the digital images and determines a space occupied by the solid objects on the shale shaker. Using the space occupied by the solid objects on the shale shaker, the computer system determines wellbore conditions.

Abstract: A system having an instrumented cutter of a drill bit including an on-cutter sensor for monitoring drilling performance metrics while performing drilling operations based on offset well data and a computing device is disclosed. The computing device executes a model development system configured to use the drilling performance metrics, surface drilling parameters, and characteristics of the instrumented cutter to train a machine learning (ML) model. The trained ML model is used to optimize drilling parameters and predict drill bit performance in a current well.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for monitoring the flow of drilling mud from a wellbore by image processing. One method includes: capturing, using a digital imaging device, images of drilling mud at a surface of the wellbore as the drilling mud flows through one or more image capture zones of a circulation system that circulates drilling mud through the wellbore and a wellbore drilling assembly; receiving, by one or more processors of a computer system operatively coupled to the digital imaging device, the images captured by the digital imaging device; and processing, by the one or more processors, the images captured by the digital imaging device to determine a rate of flow of the drilling mud through the one or more image capture zones.

Abstract: An instrumented cutter including a polycrystalline diamond table bonded to a substrate with a sensor, for monitoring the condition of the polycrystalline compact diamond table, embedded in the substrate. Further the instrumented cutter includes a wireless transmitter equipped with a power supply to power to the wireless transmitter.

Abstract: A method for a well includes obtaining a dynamic input parameter combination for the well, determining measurements for a rotating control device using a sensor, and predicting a rotating control device wear estimate using rotating control device estimation methods, the measurements, and the dynamic input parameter combination. The method for a well further includes maximizing an instantaneous rate of penetration, while reducing rotating control device failure, to determine an optimized average rate of penetration using the rotating control device wear estimate. Finally, the optimized average rate of penetration is executed for a well.

Abstract: A method includes receiving, by a computer system, a video of a visible state of a component of a generator, the generator powering at least a portion of a rig equipment system at a wellsite. The computer system can determine an operational parameter based on the visible state of the component of the generator imaged in the video, and can transmit the operational parameter to an output device.

Abstract: Methods, systems, and computer-readable medium to perform operations including: obtaining, in real-time from one or more sensors of a drilling system, real-time drilling data of a drilling operation of drilling a wellbore; using the drilling data to calculate a carrying capacity index (CCI) of a drilling fluid and a cutting concentration in an annulus (CCA) of the wellbore; determining that at least one of the CCI and the CCA is outside a respective range; determining a corrective action to adjust the at least one of the CCI and the CCA to be within the respective range; and performing the corrective action.

Abstract: Analysis of drilling slurry solids by image processing includes receiving images of a solid slurry including a mixture of wellbore drilling mud and solid objects found in the wellbore while drilling the wellbore through a subterranean zone. The solid objects include drill cuttings and non-drilled solids. By implementing image processing techniques on the images, the solids in the solid slurry are classified a drill cutting or as a non-drilled solid.

Abstract: An instrumented cutter including a polycrystalline diamond table bonded to a substrate with a sensor, for monitoring the condition of the polycrystalline compact diamond table, embedded in the substrate. Further the instrumented cutter includes a wireless transmitter equipped with a power supply to power to the wireless transmitter.

Abstract: A method for monitoring solids content during drilling operations may include collecting real-time cuttings image data at a surface outlet of a natural resource well, determining cuttings characteristics data based on the real-time cuttings image data, collecting real-time surface mud data, and determining real-time, one-dimensional downhole cuttings information based on a multi-dimensional computational fluid dynamics model. The cuttings characteristics data may include cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, cuttings area, or combinations thereof. The real-time surface mud data may include inlet mud parameters, drilling operational parameters, well planning parameters, or combinations thereof.

Abstract: A testing device that includes a wear testing device, a sensor array, and a controller. The wear testing device includes a sample rotation element configured to hold and to rotate a sample; and a cutting element holder configured to hold a cutting element and to engage the cutting element with the sample as the sample rotates. The sensor array includes an acoustic emissions (AE) sensor array comprising a plurality of AE sensors, the plurality of AE sensors configured to measure a plurality of acoustic signals generated during engagement between the cutting element and the sample; and a load sensor. The controller is communicably connected to the sensor array and configured to determine a toughness and a wear resistance of the cutting element using the plurality of acoustic signals, the applied load, and a wear state of the cutting element.

Abstract: A method includes taking at least one image of a plurality of returned rock fragments from a wellbore using a camera, analyzing the at least one image with an image analysis program to detect a caving in the plurality of returned rock fragments, constructing a model of the caving, and incorporating the model of the caving into a finite element geomechanics model of the wellbore using a meshing program to create an adjusted model of the wellbore.

Abstract: A system having an instrumented cutter of a drill bit including an on-cutter sensor for monitoring drilling performance metrics while performing drilling operations based on offset well data and a computing device is disclosed. The computing device executes a model development system configured to use the drilling performance metrics, surface drilling parameters, and characteristics of the instrumented cutter to train a machine learning (ML) model. The trained ML model is used to optimize drilling parameters and predict drill bit performance in a current well.

Abstract: A method for optimizing drilling performance is disclosed.