17960350. MEASURING DRILLING FLUID HYDROGEN SULFIDE WITH SMART POLYMERS simplified abstract (Saudi Arabian Oil Company)
Contents
- 1 MEASURING DRILLING FLUID HYDROGEN SULFIDE WITH SMART POLYMERS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MEASURING DRILLING FLUID HYDROGEN SULFIDE WITH SMART POLYMERS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MEASURING DRILLING FLUID HYDROGEN SULFIDE WITH SMART POLYMERS
Organization Name
Inventor(s)
Mohammed Albassam of Alkhobar (SA)
Arturo Magana Mora of Dhahran (SA)
Chinthaka Pasan Gooneratne of Dhahran (SA)
Mohammad Aljubran of Sayhat (SA)
MEASURING DRILLING FLUID HYDROGEN SULFIDE WITH SMART POLYMERS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17960350 titled 'MEASURING DRILLING FLUID HYDROGEN SULFIDE WITH SMART POLYMERS
Simplified Explanation
The patent application describes a system and method for using smart polymers with hydrogen sulfide (H2S) sensitivity in drilling operations. The smart polymers are triggered by increasing H2S concentrations, and their insertion timestamps are stored for monitoring purposes. Continuous images and observed characteristics of returning mud containing the smart polymers are captured and used to estimate H2S levels at the drill bit.
- Smart polymers with H2S sensitivity are inserted into drilling fluid for monitoring H2S concentrations.
- Insertion timestamps are stored for each unit of smart polymer.
- Continuous images and observed characteristics of returning mud are captured and used to estimate H2S levels.
- Image processing algorithms, machine-learning models, and deep-learning models are used to analyze data and suggest changes to drilling parameters.
Potential Applications
The technology can be applied in oil and gas drilling operations to monitor and manage H2S levels, ensuring safety and efficiency.
Problems Solved
This technology addresses the challenge of detecting and responding to increasing H2S concentrations during drilling operations, helping to prevent potential hazards.
Benefits
The use of smart polymers with H2S sensitivity provides real-time monitoring and control of H2S levels, improving safety and operational effectiveness in drilling activities.
Potential Commercial Applications
The technology can be utilized by oil and gas companies, drilling contractors, and service providers to enhance safety protocols and optimize drilling processes.
Possible Prior Art
Prior art may include technologies for monitoring and controlling gas levels in drilling operations, such as gas sensors and real-time monitoring systems.
What are the specific image processing algorithms used in this technology?
The specific image processing algorithms used in this technology are not explicitly mentioned in the abstract. However, it can be inferred that the algorithms are designed to analyze continuous images of returning mud containing smart polymers to estimate H2S levels at the drill bit.
How are the machine-learning models trained to estimate H2S levels in this technology?
The abstract does not provide details on how the machine-learning models are trained to estimate H2S levels. It can be assumed that the models are trained using historical data and observations to correlate image characteristics with H2S concentrations.
Original Abstract Submitted
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.
