Co2 i-bots for seal integrity monitoring

A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

• deploying a plurality of CO2 i-Bot sensors downhole into an observation well located above a CO2 injection zone;
• establishing communication among the plurality of CO2 i-Bot sensors, between the plurality of CO2 i-Bot sensors and a base station, and between the base station and a central processing location;
• collecting a plurality of environmental data and sensor data from the plurality of CO2 i-Bot sensors;
• training a machine learning algorithm to predict the plurality of environmental data and sensor data of the plurality of CO2 i-Bot sensors;
• and
• determining an optimized number of CO2 i-Bot sensors that minimizes a quantity of power consumption and maximizes an area of coverage of a hydrocarbon reservoir by the plurality of CO2 i-Bot sensors using the trained machine learning algorithm.

A system optimizing an injection of CO2 down a borehole into a hydrocarbon reservoir and monitoring for escaped CO2, comprising:

• a plurality of CO2 i-Bot sensors configured to traverse the borehole and monitor CO2 in the hydrocarbon reservoir;
• a base station operatively connected to the plurality of CO2 i-Bot sensors;
• wherein the system is configured to establish communication among the plurality of CO2 i-Bot sensors, and between the plurality of CO2 i-Bot sensors and the base station;
• a control center having a computer processor operatively connected to the plurality of CO2 i-Bot sensors, the processor being configured to:
• collect a plurality of environmental data and sensor data from the plurality of CO2 i-Bot sensors;
• train a machine learning algorithm to predict the plurality of environmental data and sensor data of the plurality of CO2 i-Bot sensors using the collected environmental and sensor data;
• and
• determine an optimized number of CO2 i-Bot sensors that minimizes a quantity of power consumption and maximizes an area of coverage of the hydrocarbon reservoir by the plurality of CO2 i-Bot sensors using the trained machine learning algorithm.