A Machine Learning-Based System for Self-Diagnosis Multiphase Flow Meters

2020 ◽  
Author(s):  
Tommaso Barbariol ◽  
Enrico Feltresi ◽  
Gian Antonio Susto
2019 ◽  
Vol 52 (11) ◽  
pp. 212-217 ◽  
Author(s):  
Tommaso Barbariol ◽  
Enrico Feltresi ◽  
Gian Antonio Susto

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3136
Author(s):  
Tommaso Barbariol ◽  
Enrico Feltresi ◽  
Gian Antonio Susto

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.


2021 ◽  
pp. 127-139
Author(s):  
E. A. Gromova ◽  
S. A. Zanochuev

The article highlights the relevance of reliable estimation of the composition and properties of reservoir gas during the development of gas condensate fields and the complexity of the task for reservoirs containing zones of varying condensate content. The authors have developed a methodology that allows monitoring the composition of gas condensate well streams of similar reservoirs. There are successful examples of the approach applied in Achimov gas condensate reservoirs at the Urengoy oil and gas condensate field. The proposed approach is based on the use of the so-called fluid factors, which are calculated on the basis of the known component compositions of various flows of the studied hydrocarbon system. The correlation between certain "fluid factors" and the properties of reservoir gas (usually determined by more labor-consuming methods) allows one to quickly obtain important information necessary to solve various development control tasks.


2021 ◽  
Author(s):  
Anton Gryzlov ◽  
Liliya Mironova ◽  
Sergey Safonov ◽  
Muhammad Arsalan

Abstract 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.


2020 ◽  
Author(s):  
Andrey Zozulya ◽  
Vladimir Baranov ◽  
Mikhail Miletski ◽  
Konstantin Rymarenko ◽  
Marat Nukhaev ◽  
...  

Summary Liquid hydrocarbon quantity optimization is among key technological indicators in the gas condensate fields development. To achieve it one needs to select and maintain optimal well-operating conditions. In this case, multiphase flow measurements are prioritized as an important optimization tool. The article presents a proven record of implementing the technology of instrumentalised virtual multiphase flow metering in the wells of the Vostochno-Makarovskoye gas condensate field to increase the efficiency of liquid hydrocarbon production. Virtual flow metering technologies that use modeling methods and adapt models to actual well-operating parameters aiming at determining well production rates are becoming increasingly popular. At that, the quality of the data at the model input does not often guarantee a qualitative determination of multiphase flow parameters. This article presents a track record of building a virtual multiphase flow meter based on single-phase streamer flow meters mounted on gas wells. Venturi flow meters were used. A series of well tests were conducted in various modes. To configure the streamer model, additional tuning studies were conducted on the separator. While testing the wells, the results of constructing a streamer model were verified by nodal analysis.


Author(s):  
Bruno Pinguet ◽  
Paul Guieze ◽  
Dave MacWilliam ◽  
Brad Martin

Representative reservoir fluid sampling and characterization has become increasingly important over the years. With exploration, appraisal and development activities moving into marginal fields and more challenging environments, accurate fluid characterization becomes more critical. This can be said for the formation tester, DST and multiphase sampling and fluid characterization environments with the most challenging area in recent years arguably being the multiphase environment. Multiphase flow meters have been accepted for several years now by the industry. Their use in permanent or well testing applications has been growing rapidly. In many cases, multiphase flow meters have replaced the separator for flow rate evaluation, but some fundamental needs from the client were not addressed properly, such as the ability to collect representative samples for phase-behavior characterization. Moreover, metering accuracies has been questionable in many cases (at very high GVF or in wet gas conditions, high pressure or /and high temperature).This paper focus on the Multiphase Active Sampling Device Service (MASS), a fluid sampling and analysis service that can be provided with the Vx multiphase metering technology with the objective of collecting representative samples, isolating and analyzing each fluid phase, and providing data from the analysis to input to the Vx acquisition software data to obtain more accurate flow rates. The collection of phase representative samples also opens the opportunity for a full recombination PVT study to be performed using the improved recombination ratio at line conditions from the multiphase flow meter. This dedicated multiphase fluid sampling and analysis system, combined with Vx technology provides flow rate better and fluid property than to a conventional test separator system.


2019 ◽  
Author(s):  
Anton Skopich ◽  
Edward Neubauer ◽  
John Clarke ◽  
Chingiz Bopiyev

2014 ◽  
Author(s):  
Ahcene Nasri ◽  
Abdulaziz Al-Anizi ◽  
Meshal A. Al-Amri ◽  
Faisal T. Al-Khelaiwi ◽  
Ammal Al-Anazi

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