Magnetic Sensors for Navigation of Untethered Downhole Robots

Abstract

Following the 4IR revolution, automation of oil and gas operations became a prime target. Various efforts have been put forward to create autonomous downhole tools which can increase the time and cost efficiency while reducing health and safety hazards. Navigation of the autonomous tools remains as one of the high barriers preventing these technologies from becoming available. This manuscript presents three new solutions we developed for our untethered autonomous logging tool to overcome this barrier. To increase the environmental self-awareness of downhole robots, we developed two technologies that will work together. The first technology is a low power miniaturized casing collar locator where a milimiter-size magnetometer chip and two 1-inch rod magnets are employed. The second technology is based on 1-D feature matching of residual magnetic fields generated by the steel casings. Here, two magnetometers are placed on the tool with a known separation along the direction of the motion. A correlation algorithm calculates the position and speed using the magnetic field logs. The low power miniaturized casing collar locator is placed on a wireline tool for the proof of concept demonstration. The tool was run in a water filled test well with 1450 feet depth. Decentralizers were used to keep the tool close to the casing wall. Clear peaks were observed at regular intervals. The detection depths were compared to a casing collar log run by a logging service company and one to one match was observed. The 1-D magnetic feature matching technology is demonstrated first by collecting residual magnetic field data from the same test well with a wireline tool. The collected signal was shifted in space and noise is added to mimic the difference with a second magnetometer. The matching algorithm was used to successfully find the shift between the two signals in time along the full log. This helped to estimate the speed of the tool which is used to calculate the position. Using information from the presented technologies, along with the data from other environmental sensors such as pressure and temperature will provide precise location that were not available before. The certainty will be improved by employing a Kalman filter that will integrate the sensor inputs. As in all autonomous vehicles, increasing the environmental self-awareness of autonomous downhole tools carries high importance for intelligent decision-making, successful and safe operation. Technologies fo surface applications, such as global positioning system, and radar may not be suitable for downhole environment. Therefore, new sensing technologies as we present here will accomplish these jobs for the robots operating below the surface.