https://doi.org/10.65294/gpogc.2026.05
Real-time acoustic detection of sand production and its impact on oil and gas well productivity Sh. Ismayilov, I. Karimov
Sand production in oil and gas wells remains a
significant issue, inducing wellbore instability, equipment wear, and reduced
production performance. Reliable and early sand influx detection is critical in
order to maintain well productivity and enhance well lifetime, especially in
weakly consolidated and unconsolidated reservoirs. This work presents a
comprehensive analysis of real-time acoustic detection methods for sand
production and their impacts on well performance through numerical simulation
and experimentation. The study employs an integrated approach using
laboratory-scale simulation, field data analysis, and advanced signal
processing techniques-i.e., Fast Fourier Transform (FFT), wavelet analysis, and
machine learning-based classification. Performance metrics, such as Sand
Production Rate (SPR) and Well Productivity Index (PI), are utilized to
quantify the influence of sand influx and the efficiency of detection methods.
Simulation results show that real-time monitoring of acoustics makes it
possible to identify sand events early with detection efficiency of more than
92%. Expanding the integration of machine learning algorithms achieves further
signal discrimination and reduces false alarms and improves reliability.
Quantifying the impact of sand production and early detection on PI shows a
clear benefit in terms of preserved well productivity as well as reduced
maintenance frequency. This study provides practical guidelines for the
installation of real-time sand monitoring systems in oil and gas production that
focus on technical and economic advantages of acoustic methods. The findings
facilitate the development of effective sand management procedures for
sustainable hydrocarbon production.