L. E. Vedoato, G. G. Lacourt, G. F. M. Santos, R. Ramos, L. O. V. Pereira
Nov 2025 · Conference Paper
In this study, a data-science-based methodology is presented for correlating Multiphase Flow Meter (MPFM) measurements with separator vessel data to enhance accuracy in quantifying oil, water, and gas-flow rates in production lines. Accurate flow rate quantification is essential for reservoir optimization, operational safety, and maximizing economic return. Although MPFMs provide real-time readings, their accuracy remains lower compared to single-phase flowmeters downstream of the separator vessel. Data from twelve wells in a Brazilian offshore field were analyzed, following a workflow comprising data collection and pre-processing, correlation analysis, and validation of correlation results. An average delay of 85.94 seconds was identified between gas-flow rate signals; compensating for this shift aligned the time series and eliminated phase oscillations. Oil and water data did not meet minimum quality criteria and were thus excluded from the analysis. The proposed methodology improves MPFM reliability and can ultimately feed machine-learning models for real-time dynamic correction, reducing reliance on physical separators on offshore platforms.
Conference: 28th International Congress of Mechanical Engineering
DOI: 10.26678/ABCM.COBEM2025.COB2025-2649
Gustavo Grecco Lacourt, Luiz Eduardo Vedoato Almeida Everton, Rogerio Ramos, Guilherme Fabiano Mendonca dos Santos, Luiz Octavio Vieira Pereira
Nov 2025 · Conference Paper
Multiphase flow refers to the simultaneous flow of two or more immiscible fluids with distinct physical properties through a pipeline. In the oil and gas industry, these flows typically comprise oil, gas, and water, which are separated on production platforms by separator vessels to allow accurate individual flow rate measurements. However, due to the size and weight of such vessels, there is growing interest in using multiphase flow meters (MPFM) as alternatives for field testing. This paper applies a calibration factor methodology to validate MPFM measurements using reference data from single-phase flow meters positioned downstream of the test separator. The calibration factor is calculated as the ratio between the cumulative volume measured by the single-phase meters and that measured by the MPFM. Field data from 27 tests conducted in two campaigns across seven wells on a Brazilian offshore platform were analyzed using a Python-based adaptation on Gustavsen's algorithm. The results showed that while the MPFM tends to estimate the total liquid flow accurately, significant deviations occur when measuring individual phases, especially oil. Calibration factors were analyzed over time and across wells, with convergence and percentage error criteria used to assess measurement reliability. A Kruskal-Wallis statistical test revealed that well-specific properties influence MPFM accuracy, suggesting the need for calibration protocols by well rather than by device. In a second campaign, adjustments in sensor acquisition frequency significantly improved calibration factor accuracy, demonstrating that MPFM performance can be enhanced through technical refinements. Additionally, a complementary 50/50 calibration and validation approach was implemented, in which half of each dataset was used to estimate the calibration factor and the remaining half to evaluate its performance. The Mean Absolute Percentage Deviation (MAPD) confirmed that calibration effectively reduced deviations, with oil MAPD decreasing from 58.36% to 8.74%, water from 10.46% to 1.69%, and gas from 29.36% to 24.92%.
Conference: 28th International Congress of Mechanical Engineering
DOI: 10.26678/ABCM.COBEM2025.COB2025-1042
L. E. Vedoato, G. Lacourt, G. F. M. Santos, R. Ramos, L. O. V. Pereira
Apr 2025 · Conference Paper
Accurate measurement of multiphase flow rates, comprising oil, water, and natural gas, is essential for efficient petroleum production management. This data is crucial in optimizing reservoir performance, ensuring operational safety, and maximizing economic returns. Multiphase flow meters (MPFMs) emerge as promising equipment for the industry's future due to their ability to provide real-time measurements of the combined rates of various phases. However, despite technological advancements, MPFMs have not yet achieved full reliability, presenting challenges related to the precision and consistency of measurements. Currently, three-phase separators are considered reference equipment for multiphase flow measurement, as they allow physical separation of the phases and individual measurement of oil, water, and gas with greater accuracy. In this context, it becomes fundamental to develop methodologies that enable the approximation and correlation of data obtained by MPFMs with the reference data from three-phase separators. Such an approach aims to validate and calibrate MPFMs, enhancing their reliability and promoting their integration into industrial workflows. This study proposes a methodology based on data science and machine learning to correlate multiphase flow measurements from MPFMs with reference data from three-phase separators. By establishing a reliable correlation between these systems, the goal is to improve confidence in MPFM measurements, facilitating their adoption as precise and efficient tools in monitoring and controlling processes in the petroleum industry.
Conference: 8th Multiphase Flow Journeys
DOI: 10.26678/ABCM.JEM2025.JEM25-0014
G. Lacourt, L. E. Vedoato, R. Ramos, G. F. M. Santos, L. O. V. Pereira
Apr 2025 · Conference Paper
In the oil and gas industry, production from wells is characterized by a multiphase flow formed by oil, water, and gas. What is produced in each well is transported individually to the processing plant, where the components are separated for their final destinations. The advance of offshore production in increasingly extreme subsea conditions requires greater control and management of production for economic, fiscal, and operational safety reasons. One of the regulatory requirements is the need to measure appropriation, i.e., to allocate the production of each field, well, and reservoir periodically. Test separators are often used for this, as they can separate the fluids, making it possible to measure each phase individually using single-phase meters. However, test separators are heavy and bulky pieces of equipment, which are scarce resources on oil platforms. This has led to the need to use multiphase meters, which can replace test separators when testing producing wells with the aim of measuring for appropriation. The aim of the multiphase meter is to determine the flow of each component present in the flow individually, without the need for separation. Any multiphase flow meter requires testing, calibration, and adjustment, which can be carried out in different locations, such as the vendor's own factory, test facilities, or even in situ. These tests are of the utmost importance to ensure that the system performs all its functions satisfactorily. On the oil platform, it is very common to evaluate the performance of the multiphase meter by comparing it with measurements obtained by single-phase meters, which are considered to be more accurate and reliable, after separation by the test separator. To carry out this type of procedure, it is necessary to use tools based on data science, such as programming languages and statistical analysis, to improve the field performance of multiphase meters. In this study, a calibration factor was applied to the flow data obtained on an oil platform using multiphase flow meters and single-phase meters installed after the test separator vessel. For this purpose, flow measurement data from seven oil wells on different days was used, totaling 24 tests.
Conference: 8th Multiphase Flow Journeys
DOI: 10.26678/ABCM.JEM2025.JEM25-0018