Degree
Doctor of Philosophy (PhD)
Department
Petroleum Engineering
Document Type
Dissertation
Abstract
Sand production in oil and gas operations presents significant challenges, leading to equipment damage, reduced production efficiency, and costly well interventions. Conventional sand detection techniques, primarily surface-based acoustic sensors, fail to provide real-time insights into sand ingress and migration within the wellbore. This dissertation explores the use of distributed fiber optic sensing technology for monitoring sand transport in pipelines and wellbores.
This research is divided into two experimental phases: a surface flow loop and a wellbore setup. The surface flow loop experiment involves a 40-ft-long, 2-in. diameter PVC pipe to simulate sand transport in horizontal pipelines. Sand slurry, with varying flow rates at constant concentration, is injected into the pipeline, and distributed acoustic sensor (DAS) data is collected to identify sand ingress locations and migration patterns. Spectral and spectrogram analyses are carried out to isolate sand signatures from background noise, and a frequency band energy (FBE) analysis is used to visualize energy content in specific frequency ranges, enhancing data interpretation to estimate slurry flow parameters, such as sand phase velocity and slip velocity.
Wellbore experiments are conducted in a 5,163-ft deep well at LSU's Petroleum Engineering Research, Training, and Testing (PERTT) facility. The wellbore, prefilled with oil-based mud containing 10% solids, is monitored using a distributed temperature sensor (DTS), along with downhole and surface measurements, to detect solids migration during mud circulation. The DTS data provides temperature profiles along the wellbore, aiding in the identification of fluid migration.
Analytical and numerical models, including computational fluid dynamics (CFD) simulations, are employed and results are compared to the experimental data. Good agreement is observed between the critical settling velocity and slip velocity of sand particles estimated using the analytical models and CFD simulations with the DAS-based results for the flow loop experiments. Similarly, pressures and density predictions from the numerical simulation model closely follow the gauge measurements and observations from the DTS for the wellbore tests. The CFD simulations and numerical analysis provide detailed insights into slurry transport dynamics and the numerical models, validated and verified with experimental data. Validated and verified numerical models offer accurate simulations of a wide range of operational conditions, enabling reliable predictive analysis. By using these models, the need for costly and time-consuming experimental trials can be avoided. Thus, the integration of fiber optic sensor data with analytical and numerical models offers a robust framework for optimizing sand management strategies in oil and gas operations.
This study establishes a novel approach for solids monitoring using fiber optic sensors, enhancing the understanding of sand transport mechanisms and providing valuable insights for improving wellbore integrity and production efficiency.
Date
8-27-2024
Recommended Citation
Shetty, Rishikesh, "Detecting Sand Ingress and Migration Using Fiber Optic Distributed Acoustic Sensing Technology and Computational Fluid Dynamics During Oil and Gas Production" (2024). LSU Doctoral Dissertations. 6580.
https://repository.lsu.edu/gradschool_dissertations/6580
Committee Chair
Sharma, Jyotsna