Horizontal-Steam-Injection-Flow Profiling Using Fiber Optics

Abstract

Summary Steam-conformance control in horizontal injectors is important for efficient reservoir-heat management in heavy-oil fields. Suboptimal conformance and nonuniform heating of the reservoir can substantially affect the economics of the field development and oil-production response and result in nonuniform steam breakthrough. To achieve the required control, it is essential to have an appropriate well-completion architecture and robust surveillance. Five fiber-optic systems, each with a unique steam-conformance-control-completion configuration, have been installed in two horizontal steam injectors to help mature steam-injection-flow profiling and conformance-control solutions. These fiber-optic systems have used custom-designed fiber-optic bundles of multimode and single-mode fibers for distributed-temperature sensing (DTS) and distributed-acoustic sensing (DAS), respectively. Fiber-optic systems were also installed in a steam-injection-test-flow loop. All the optical fibers successfully acquired data in the wells and flow loop, measuring temperature and acoustic energy. A portfolio of algorithms and signal-processing techniques was developed to interpret the DTS and DAS data for quantitative steam-injection-flow profiling. The heavily instrumented flow-loop environment was used to characterize DTS and DAS response in a design-of-experiment (DOE) matrix to improve the flow-profiling algorithms. These algorithms are dependent on independent physical principles derived from multiphase flow, thermal hydraulic models, acoustic effects, large-data-array processing, and combinations of these methods for both transient and steady-state steam flow. A high-confidence flow profile is computed using the convergence of the algorithms. The flow-profiling-algorithm results were further validated using 11 short-offset injector observation wells wells in the reservoir that confirmed steam movement near the injectors.

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

“…Multiphysics analysis and clustering optimization [89] 2019 Flow-loop experiment Applied on steam flow profiling experiment with high resolution DTS and DAS data.…”

“…Using the DTS trace, the identification of the annular-fluid interfaces (brine/gas, gas/flowing gas) was possible, as well as the depths of active lifting points. Shirdel et al [89] employed several signal processing algorithms, including DAS spectrogram, DTS and DAS waterfall analysis, and steady-state injection, to interpret the DTS and DAS data to provide a quantitative step-injection-flow profiling. It shows that those algorithms are tied together with an independent physical principle related to multiphase flow, acoustic effects, data array, and others.…”