CO2 I-BOTS FOR SEAL INTEGRITY MONITORING

Organization Name

Saudi Arabian Oil Company

Inventor(s)

Abdallah A. Alshehri of Dhahrah (SA)

Klemens Katterbauer of Dhahran (SA)

Abdulaziz S. Al-qasim of Dammam (SA)

CO2 I-BOTS FOR SEAL INTEGRITY MONITORING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18059786 titled 'CO2 I-BOTS FOR SEAL INTEGRITY MONITORING

Simplified Explanation

The patent application describes systems and methods for monitoring a hydrocarbon reservoir for escaped CO2 using COi-Bot sensors deployed downhole in an observation well. The sensors collect environmental and sensor data, which is used to train a machine learning algorithm to predict future data. The algorithm is then used to determine the optimal number of sensors to minimize power consumption and maximize coverage of the reservoir.

COi-Bot sensors deployed downhole in an observation well
Establishing communication among sensors, base station, and central processing location
Collecting environmental and sensor data
Training a machine learning algorithm to predict data
Determining optimal number of sensors to minimize power consumption and maximize coverage

Potential Applications

The technology can be applied in the oil and gas industry for monitoring CO2 leakage in hydrocarbon reservoirs, ensuring safety and environmental protection.

Problems Solved

This technology addresses the challenge of efficiently monitoring CO2 emissions in underground reservoirs, helping to prevent environmental damage and ensuring regulatory compliance.

Benefits

The benefits of this technology include improved safety, early detection of CO2 leaks, reduced environmental impact, and optimized resource management in hydrocarbon reservoirs.

Potential Commercial Applications

One potential commercial application of this technology could be in providing monitoring services to oil and gas companies to ensure compliance with environmental regulations and prevent costly leaks.

Possible Prior Art

One possible prior art could be the use of traditional monitoring methods such as manual inspections or fixed sensors, which may not be as efficient or cost-effective as the proposed COi-Bot sensor system.

=== What are the specific technical specifications of the COi-Bot sensors? The article does not provide detailed technical specifications of the COi-Bot sensors, such as their size, weight, power requirements, or communication protocols.

=== How does the machine learning algorithm handle data processing and analysis? The article does not explain in detail how the machine learning algorithm processes and analyzes the collected data to predict future environmental and sensor data.

Original Abstract Submitted

Systems and methods for monitoring a hydrocarbon reservoir for escaped COare disclosed. The methods include deploying a plurality of COi-Bot sensors downhole into an observation well located above a COinjection zone, establishing communication among the plurality of COi-Bot sensors, between the plurality of COi-Bot sensors and a base station, and between the base station and a central processing location. The methods also include collecting a plurality of environmental data and sensor data from the plurality of COi-Bot sensors, and training a machine learning algorithm to predict the plurality of environmental data and sensor data of the plurality of COi-Bot sensors. Furthermore, the methods include determining an optimized number of COi-Bot sensors that minimizes a quantity of power consumption and maximizes an area of coverage of the hydrocarbon reservoir by the plurality of COi-Bot sensors using the trained machine learning algorithm.