Real-Time Sand Detection for Gas Wells Using AI Applications

Abstract

Abstract This paper proposes a novel approach for real-time sand detection in gas wells using an Autoencoder and a rule-based model ensemble. Accurate identification and early detection of sand in gas wells are crucial for effective sand management. However, the dynamic flow conditions and high-pressure environments of gas wells make sand detection challenging. The study aims to develop a methodology for sand production detection, considering the limitations of surface facilities and laboratory measurements. Methodology is based on essential features such as Acoustic Sand Detector (ASD) readings, Downhole Pressure, and Production Choke measurements. The methodology was applied for both top-side installed ASDs as well as subsea wells. To overcome the complexity of sand production patterns, we employ an Autoencoder, a deep learning technique capable of addressing the issue. In conjunction, we develop a rule-based model that leverages domain expertise to detect simpler sand patterns. The combination of these models forms a comprehensive and robust sand detection framework for real-time operations and monitoring. The proposed model demonstrates high accuracy in sand detection. After running the model on unsupervised data, we manually evaluated the results by inspecting AI estimated sand labels individually. Labels were assigned to classify them as true positives (captured sands) or false positives (false alarms). Accuracy and precision metrics were then calculated. For the top-side gas wells, the model achieves accuracy ranging from 98.5% to 100%. Meanwhile, for the subsea wells, the model achieves accuracy ranging from 89.4% to 100%. The performance of our approach demonstrates its effectiveness in detecting sand based on recordings both from surface and subsea gas wells. By accurately identifying sand presence, the proposed approach contributes to enhancing sand management strategies in the gas industry. The findings enable proactive maintenance, reduction in equipment damage, and optimization of production processes, ultimately enhancing operational efficiency in gas wells. The algorithm has been successfully deployed for a gas field located in the Caspian Basin. This paper introduces a comprehensive and robust sand detection framework that effectively addresses the complexity of sand production patterns in gas wells. By integrating the Autoencoder with a rule-based model that utilizes domain expertise, creating the framework that demonstrates high accuracy in detecting sand in both topside and subsea wells. This innovative methodology significantly enhances operational efficiency in sand management within the petroleum industry. The findings of this study make a valuable contribution to the literature, offering a novel approach to sand detection that promises to benefit the industry at large.