Distributed Acoustic Sensing of Passive and Active Inflow Control Technology and Quantification of Flow Rates for Reservoir Surveillance

Abstract The application of distributed acoustic sensing (DAS) technology has continued to gain popularity and develop rapidly as a diagnostic tool within intelligent completions outfitted mature fields oil and gas wells, for the purpose of production and injection flow monitoring. Intelligent completion wells consist of subsurface valves placed at tailored depth increments which allows for the passage and restriction of flow in both a passive and active sense. The fluidic autonomous inflow control devices (AICDs) operate passively without moving parts or electronics and are designed to preferentially restrict unwanted fluids at breakthrough from the formation. Interval control valves (ICVs), in contrast, are controlled from the surface to adjust and expose flow ports which regulate flow. The following research involves a set of experiments about which a multiphase flow loop (oil, water, gas) was instrumented with a DAS system across an inflow test section that included both AICDs and ICVs. The experiments sampled realistic subsurface flow rates for both injection and production scenarios and sample fluid properties, where the composition was varied in two phase (100% oil to 100% water) and three phase mixtures (oil/water mix to 100% gas). The DAS data was acquired continuously with a temporal sampling frequency of 40 kHz which enabled an analysis of both the transient and steady state flow behavior. Electronic reference hydrophone and accelerometer data were also acquired to provide insights in supporting the DAS interpretation. Results from the single phase (100% water) injection flow configuration display a direct relationship between the acoustic response of the DAS acquired data at the ICV position along the flow loop with respect to choke settings and flow rates. Multiphase production flow quantification for both ICV and AICD configurations are significantly more complex than single phase injection flow given the range of fluid mixtures that may be present in field acquired data, however initial results from flow loop data are encouraging and require continued investigation. The complete dataset acquired at the flow loop provides a novel resource for the development of machine learning models which utilize DAS acoustic features, as well as other available data such as pressure gauge measurements, to estimate both flow rates and fluid composition. The pairing of these technologies will enable a realtime quantification of inflow rates per zone and provide operators with the necessary data to optimize reservoir management.