Jianjun Zhu (Doctor of Philosophy in Petroleum Engineering)
Experiments, CFD Simulation and Modeling of ESP Performance Under Gassy Conditions
Directed by Dr. Hong-Quan (Holden) Zhang
232 pp., Chapter 5: Conclusions and Recommendations
(272 words)
Experimental measurements of pump boosting pressure under liquid and gas-liquid flow conditions are conducted on a 3-inch two-phase flow loop with a 14-stage radial-type electrical submersible pump (ESP). The stage-by-stage pressure increment with varying flow conditions is measured. Effects of intake pressure, gas volumetric fraction (GVF), rotational speeds, and surfactant presence on the ESP pressure increment are investigated. Two schemes of experimental testing are carried out to evaluate the pump behavior at different operational conditions, including surging tests (constant liquid flow rate) and mapping tests (constant gas flow rate). Experimental results reveal that the boosting pressure of ESP deteriorates with GVF increase. The gas tolerance of ESP improves significantly with surfactant injection, especially at higher intake GVFs.
Three-dimensional (3D) computational fluid dynamics (CFD) is used in this study to investigate multiphase flow behavior related to ESP boosting pressure. Compared with experimental measurements, the numerical prediction error for high-viscosity fluid flows is within ±15%. For gas-liquid flow, the numerically simulated ESP pressure increment is found to match experimental results well by incorporating a newly-developed bubble size model. The CFD simulation results of the in-situ gas void fraction (αG) are used to validate the newly developed model for predicting in-situ αG in a rotating ESP impeller
Based on the basic conservation equations for mass and momentum, a mechanistic model for predicting flow patterns and hydrodynamics in two-phase ESP flow is developed and validated with experimental results. The model-predicted ESP stage pressure increment agrees well with experimental measurements in both trends and values. The discrepancy in model predictions can be improved by improving the closure relationships, including bubble size model, and drag coefficient correlation.
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