A Novel Technique for Determining Threshold Sand Rates from Acoustic Sand Detectors for Well Integrity Management

ABSTRACT The Acoustic Sand Detector (ASD) is one of the most widely used methods for detecting and estimating the amount of sand in the flow. ASDs capture the noise caused by the flow as well as the noise caused by sand hitting the pipe walls. The lowest sand rate at which detectors fail to differentiate flow or background noise from sand impact noise is known as threshold sand rates (TSR). In this work, based on several experiments in large scale multiphase flow loops and modeling, a unique method has been developed to determine TSR using the background noise that is generated by ASDs. This research has two parts: modeling and experimental work. A novel technique for predicting TSR has been developed using a significant amount of data collected over the years in large-scale test facilities with pipe sizes ranging from 50.8 mm to 101.6 mm. Data analysis and artificial Intelligence (AI) techniques are utilized to predict the TSR. For experimental data, firstly, ASDs were placed on two different locations: upstream and downstream of an elbow. Then, ASDs were positioned at two different orientations: 3 o'clock and 12 o'clock. Finally, two different elbow test sections were used: one with a 2D distance between two metallic elbows in series and other test sections where the first elbow was metallic, and the second elbow was non-metallic in series with a distance of 7D. The inner pipe diameter used was 76.2 mm and the sand size used was 150 μm. Both vertical and horizontal orientations were used. Experimental conditions were selected to cover annular, slug-churn, and dispersed-bubble air-water flows with superficial gas velocity ranging in-between 7 m/s to 35 m/s while superficial liquid velocity ranging in-between 0.09 m/s to 3.35 m/s. The effects of ASD location, sensor orientation and the effect of distance between two elbows on TSR were obtained. For ASD's location, it was observed that for the majority of cases, the ASD captures more sand noise when installed downstream of an elbow as compared to the upstream straight section. For ASD orientation, the 12 o'clock orientation is shown to be more effective as compared to the 3 o'clock orientation. Regarding the effect of test section configuration, the study of two elbows in series showed that when the distance between the two metallic elbows is 2D, the noise of sand impacting the second elbow interferes with the noise of sand impacting the first elbow. A new C values prediction model is developed and compared with AI TSR predictions. The results of this study can help operators select optimal ASD installation locations and allow them to predict TSR from background noise, which is critical for managing well integrity challenges due to sand production in oil and gas fields. To further validate this novel TSR determination technique, future work involves deploying ASDs in real-world oil and gas wells across different field environments. This will assess the performance of the method under actual operational conditions