Artificial Intelligence and Data Analytics for Virtual Flow Metering

Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.

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

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.

Heat flow metering in building practice - A critical field study for a large industrial building complex

Increasingly complex concepts for the heating and cooling supply of buildings require both intelligent and transparent operational management strategies. One way of sequencing and coordinating different generator components is to include information about heat flows on the consumption side. In addition to heat meters, modern pumps also provide heat flow detection. The present study compares the heat flow detection via heat meters and pumps for multiple hydraulic circuits in the operating phase of a large industrial demonstration object. In particular, the influence of typical errors in the installation of the temperature measurement and their elimination are quantified.

Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates

Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.

Development and Implementation of the Technology of a Mobile Gas-Lift Complex For Transferring Wells of the Block-Conductor of the Field Named After Yu. Korchagin For Artificial Lift

This paper is aimed at the mobile gas-lift unit installation workup to shift the wells of the conductor platform of the Yu. Korchagin field to mechanized extraction instead of constructing a gas lift pipeline. The paper presents all the stages of this technology implementation, from conceptual design, engineering calculations, to the economic feasibility study, implementation and operation of this unit. During the operation of the wells of the conductor platform at the Yu. Korchagin field, the following problem occurred: a gas-lift gas pipeline was not constructed from the offshore ice-resistant fixed platform to the conductor platform, as they wanted to shift the wells to the mechanized extraction method (artificial lift). An alternative option to provide gas-lift gas to the wells of the conductor platform is to install a mobile gas-lift unit directly on an unmanned platform. This mobile gas-lift unit will be a compact separator of a gas-liquid mixture from a donor well, and it will pipe a separated gas-lift gas supply system with control and flow metering sets into the production wells. This system enables a shift of the wells of an unmanned conductor platform to a compressor-less gas-lift operation and a remote regulation of production and control over the wells operation.