Data Driven Virtual Flow Metering: Leveraging AI and Edge Computing for Continuous Multiphase Flow Measurement

Abstract

Abstract This paper describes the use of data-driven virtual flow metering (VFM) for continuous multiphase flow measurement, which has been developed and tested in an oil field well pilot in Austria. 12 ESP (Electric Submersible Pump) wells have been modelled and fine-tuned within the pilot. Hardware-based test separators were used to conduct quality control evaluations on the predicted production rates and calibrate the well models as required. For the practical deployment of VFM systems, we have addressed the need for optimized learning and scalability of the artificial intelligence (AI) models by means of what we call soft-sensing and will explain how to successfully deploy this technology on wells with artificial lift. Notably, the application of this software-based, soft-sensing VFM in combination with hardware-based multiphase flow measurement bears the potential to significantly reduce the CAPEX cost for future metering infrastructure investments and even reduce the OPEX of existing metering hardware by extending the duration of metering cycles. This makes data-driven VFM an economical option even for low-producing wells. Details of the well pilot project conducted with OMV in Austria will be provided. The use of soft-sensing VFMs via cloud computing for continuous multiphase flow measurement is a step toward the closed-loop, fully autonomous operation of oil fields.