17959405. MEASURING BOTTOM-HOLE PRESSURE WITH SMART POLYMERS simplified abstract (Saudi Arabian Oil Company)

MEASURING BOTTOM-HOLE PRESSURE WITH SMART POLYMERS

Organization Name

Saudi Arabian Oil Company

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)

Peter Boul of Houston TX (US)

MEASURING BOTTOM-HOLE PRESSURE WITH SMART POLYMERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17959405 titled 'MEASURING BOTTOM-HOLE PRESSURE WITH SMART POLYMERS

Simplified Explanation

The patent application describes a method for determining well pressure during a drilling operation by using pressure-responsive smart polymers and image processing technologies.

• Pressure-responsive smart polymers are inserted into drilling fluid during a well drilling operation.
• An insertion timestamp is stored for each unit of smart polymer to track when it was inserted.
• Continuous images and observed characteristics of drilling mud containing the smart polymers are captured by a camera.
• Bottom hole pressure (BHP) at the drill bit is estimated using the images, observed characteristics, and insertion timestamps.
• Image processing algorithms, machine-learning models, and deep-learning models are used to determine the BHP estimate.
• Suggestions for adjusting drilling parameters are made based on the estimated BHP.

Potential Applications

This technology can be applied in the oil and gas industry for real-time monitoring and optimization of drilling operations.

Problems Solved

This technology helps in accurately determining well pressure, which is crucial for maintaining drilling efficiency and preventing wellbore instability.

Benefits

The use of pressure-responsive smart polymers and image processing technologies improves the accuracy and efficiency of well pressure determination during drilling operations.

Potential Commercial Applications

This technology can be utilized by oil and gas companies to enhance drilling processes, reduce downtime, and improve overall operational efficiency.

Possible Prior Art

One possible prior art could be the use of traditional pressure sensors in drilling operations to monitor well pressure. However, the use of smart polymers and image processing technologies as described in the patent application represents a novel approach to this problem.

How does this technology compare to traditional methods of determining well pressure in terms of accuracy and efficiency?

The article does not provide a direct comparison between this technology and traditional methods of determining well pressure. Further research or testing may be needed to evaluate the effectiveness of this new approach.

What are the potential limitations or challenges of implementing this technology in real-world drilling operations?

The article does not address any potential limitations or challenges that may arise when implementing this technology in actual drilling operations. Factors such as cost, reliability, and compatibility with existing systems could be important considerations that need to be explored further.

Original Abstract Submitted

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.