17959408. MEASURING DRILLING FLUID PH WITH SMART POLYMERS simplified abstract (Saudi Arabian Oil Company)
Contents
- 1 MEASURING DRILLING FLUID PH WITH SMART POLYMERS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MEASURING DRILLING FLUID PH 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 Unanswered Questions
- 1.11 Original Abstract Submitted
MEASURING DRILLING FLUID PH WITH SMART POLYMERS
Organization Name
Inventor(s)
Mohammed Albassam of Al-Khobar (SA)
Arturo Magana Mora of Dhahran (SA)
Chinthaka Pasan Gooneratne of Dhahran (SA)
Mohammad Aljubran of Dhahran (SA)
MEASURING DRILLING FLUID PH WITH SMART POLYMERS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17959408 titled 'MEASURING DRILLING FLUID PH WITH SMART POLYMERS
Simplified Explanation
The patent application describes a system and method for using smart polymers with pH sensitivity in drilling operations. These smart polymers are triggered by hydrogen ion concentrations and can provide real-time pH values at the drill bit.
- Smart polymers with pH sensitivity are inserted into drilling fluid during drilling operations.
- The smart polymers are triggered by hydrogen ion concentrations.
- An insertion timestamp is stored for each unit of smart polymer.
- Continuous images and observed characteristics of returning mud containing the smart polymers are captured by a camera.
- pH values at the drill bit are estimated using the continuous images, observed characteristics, and insertion timestamps.
- Changes to drilling parameters are suggested based on the estimated pH values.
Potential Applications
The technology can be applied in various drilling operations, such as oil and gas exploration, geothermal drilling, and mining.
Problems Solved
This technology allows for real-time monitoring of pH values at the drill bit, which can help in optimizing drilling parameters and preventing equipment damage.
Benefits
The use of smart polymers with pH sensitivity can improve drilling efficiency, reduce downtime, and enhance overall operational safety.
Potential Commercial Applications
Potential commercial applications include offering drilling services with real-time pH monitoring capabilities, selling smart polymer units to drilling companies, and licensing the technology to equipment manufacturers.
Possible Prior Art
Prior art may include similar systems using smart polymers for monitoring drilling parameters or pH levels in other industrial processes.
Unanswered Questions
How does the system handle variations in hydrogen ion concentrations in different geological formations?
The system may need to be calibrated or adjusted based on the specific characteristics of each drilling site to ensure accurate pH monitoring.
What is the cost-effectiveness of implementing this technology compared to traditional drilling methods?
An analysis of the cost savings and operational benefits of using smart polymers for pH monitoring in drilling operations would be valuable for potential adopters of this technology.
Original Abstract Submitted
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.
