17960355. MEASURING DRILLING FLUID SALINITY WITH SMART POLYMERS simplified abstract (Saudi Arabian Oil Company)

MEASURING DRILLING FLUID SALINITY WITH SMART POLYMERS

Organization Name

Saudi Arabian Oil Company

Inventor(s)

Mohammed Albassam of Alkhobar (SA)

Arturo Magana Mora of Dhahran (SA)

Chinthaka Pasan Gooneratne of Dhahran (SA)

Mohammad Aljubran of Sayhat (SA)

Peter Boul of Houston TX (US)

MEASURING DRILLING FLUID SALINITY WITH SMART POLYMERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17960355 titled 'MEASURING DRILLING FLUID SALINITY WITH SMART POLYMERS

Simplified Explanation

The patent application describes a system and method for using smart polymers with chloride ion sensitivity in drilling operations. Here are the key points of the innovation:

• Smart polymers with chloride ion sensitivity are inserted into drilling fluid pumped into a well.
• The smart polymers are triggered by chlorine ion concentrations.
• An insertion timestamp is stored for each unit to track when it was inserted.
• Continuous images and observed characteristics of returning mud containing the smart polymers are captured by a camera positioned at a sensing location.
• An estimate of salinity in the drilling fluid is determined using continuous images, observed characteristics, and insertion timestamps.
• Changes to drilling parameters are suggested based on the estimate of salinity.

Potential Applications

The technology can be applied in various drilling operations to monitor and control salinity levels in real-time, leading to improved drilling efficiency and wellbore stability.

Problems Solved

This innovation addresses the challenge of accurately monitoring and controlling salinity levels in drilling fluids, which is crucial for successful drilling operations and preventing costly wellbore instability issues.

Benefits

The use of smart polymers with chloride ion sensitivity allows for precise and timely adjustments to drilling parameters based on real-time salinity estimates, leading to optimized drilling performance and reduced risks of operational disruptions.

Potential Commercial Applications

This technology has potential applications in the oil and gas industry, geothermal drilling, and other drilling operations where monitoring and controlling salinity levels are critical for operational success.

Possible Prior Art

One possible prior art could be the use of sensors or chemical additives in drilling fluids to monitor salinity levels, but the specific use of smart polymers with chloride ion sensitivity as described in this patent application may be a novel approach.

How does the system handle variations in chloride ion concentrations in different geological formations?

The patent application does not provide details on how the system adapts to different chloride ion concentrations in various geological formations during drilling operations.

What is the cost-effectiveness of implementing this technology compared to traditional methods of monitoring salinity in drilling fluids?

The patent application does not discuss the cost implications of using smart polymers with chloride ion sensitivity in drilling operations compared to conventional methods of monitoring salinity levels.

Original Abstract Submitted

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.