Field Testing of an Automated System for Hole Cleaning and Wellbore Stability Monitoring and Management

Abstract This paper describes the results of a field test, conducted at a test rig, of a hole cleaning and wellbore stability monitoring system. This system encompasses a laser-based sensor and digital tools designed to process the collected data in real time. Additionally, this paper outlines the method for interpretating the processed data, including the combination of the estimated volume of cuttings recovered at the surface and the expected volume obtained with a transient cuttings transport model. The test was conducted while drilling three borehole sections at a test location in Germany. The preparation for the test included the installation of an auger system to divert the cuttings from three active shale shakers to the cuttings sensor. This sensor is equipped with a high-definition camera and a laser profile scanner, enabling the collection of both 2D images and point-cloud data of the cuttings stream. This data was then fed into a digital tool capable of estimating the solids’ volume and morphological characteristics, identifying cavings and their shape and size, and providing an evaluation of the wellbore condition. The test revealed that the system can be successfully deployed in the field with a simple rig-up procedure. Additionally, it showed that the digital tool and the underlying machine-learning techniques can accurately estimate the solids’ volume and their morphological characteristics. Furthermore, a minor discrepancy between the measured volume and the expected volume according to a transient cuttings transport model showed that the sensor can reliably be used to assess hole cleaning sufficiency in real time. During these tests, artificial cavings were introduced to evaluate the system's capability of cavings detection and characterization, the latter involving the shape of cavings which is related to the wellbore failure mechanism that created them. The results indicate that the system can be used to evaluate the in-depth nature of wellbore stability in real time. This, in turn, will allow for targeted corrective action to deal with stability-related operational problems. Finally, we also identified limitations of the current system, such as the generation of nonoptimal segmentation results when the cuttings’ size ranges from medium to coarse sand size (0.25–1 mm). Based on these limitations, we have defined a plan for future improvement, further described in detail in this paper. Previous field testing of the sensor only evaluated the sensor's readiness for deployment and the software's capability to reliably measure bulk volume—largely without correction for void space. The tests presented in this paper are more comprehensive. They evaluated the system's capabilities to measure the corrected solids’ volume, their morphological characteristics, and the integration of a transient cuttings model into the system for a holistic assessment of the well condition.