A Unique Software Tool for Estimating Subsea Measurement System Uncertainty

Accurate oil, water and gas flow rate measurements from individual wells are essential in order to properly manage both the wells and reservoirs, as well as to obtain reliable data for production performance monitoring, optimization, allocation and simulation models1, 2, 3, 4, 5. Over the past three decades, there has been great advancement in the multiphase metering technology. These advancements have resulted in a significant improvement in measurement accuracy as well as in reducing the physical size of these meters which is a crucial element in limited-space environments as in offshore platforms. The intent of this paper is to present results of a compact Multiphase Flow Meter (MPFM) " PhaseWatcher?? based on information gathered from Saudi Arabia, where more than one hundred meters have been installed so far in several fields covering wide ranges of fluid properties and operating conditions. Moreover, the paper will discuss in detail the effect of crude sourness (H2S concentration) and pressure on the meter's measurement accuracy. Multiphase Flow Meter (MPFM) Design The PhaseWatcher is designed to measure the total volumetric flow rates of oil, water and gas at line conditions. These flow rates are converted to standard onditions via a pressure-volume-temperature (PVT) package, which can be either the default Black Oil Model or a specific model derived using a client supplied PVT data. The meter comprises of five basic components, Fig. 1:A Venturi meter with pressure transducers across it to measure the differential pressure between the inlet and throat of the Venturi.A nuclear dual energy fraction meter consisting of a Smart Detector and Barium Source, measures the count range of photons transmitted from the source to the detector at two different photon energy levels (the high energy level is used to measure the density of the mixture, while the low energy level is used to calculate the water-oil ratio).Process fluid temperature transducers, which measure the fluid temperature upstream of the Venturi section and the ambient temperature.A Blind Tee Static Mixer, used to homogenize the flow stream ahead of the Venturi meter.A process flow computer used to manipulate the measured data.

The need for multiphase flow measurement in the oil and gas production and petrochemical industries has been significantly increased over the last few years.Reliable measurements of the multiphase flow parameters such as void fraction, phase concentration, phase velocity and flow pattern identification are important for accurate modelling and/or in the operation of multiphase systems.Although many multiphase flow meters were recently developed, challenges in measuring multiphase flow components remain unresolved.Therefore, extensive research efforts were spent in designing accurate multiphase flow meters and several meters are currently under development worldwide.However, due to the complexity of the multiphase flow mixture and in some cases when three or more phases co-exist, it is impossible to adopt only one technique to develop a multiphase flow meter.Consequently, the integration of multiple sensors, based on several measurement techniques, found to be the optimum solution for accurate multiphase flow metering.The main objective of this study is to evaluate existing multiphase flow measurement techniques for the potential integration in a multiphase flow metering device.

The difficulties associated with producing heavy oil have discouraged operators from developing the Atlanta field in the Santos basin offshore Brazil, despite more than 1.5 billion bbl of oil in place. The water depth in the area is more than 1,500 m, which makes operations even more challenging. Because Brazil has set aggressive production targets for the next decade, new interest has been given to the field, and a consortium of E&P operators began to reassess the field. Well deliverability and test data are crucial to field development planning. Well test operations were planned with a multienergy gamma ray and venturi multiphase flow meter used as contingency. During the well test operations, the foaming propensity of the produced oil made it almost impossible to distinguish an interface between liquid and gas in the separator or tank, rendering the standard equipment useless. Only in the second test were some tank measurements available. The measurement principle used in this multiphase flow meter, however, is not sensitive to foam or emulsion because they respond to the atomic-level composition of the different components of the mixture independently of their arrangement, providing accurate flow rate measurements. We detail the behavior of the multiphase flow meter during the well tests in the Atlanta field and the challenges caused by foaming oil. In the case of standard oils, the measurements are not sensitive to the viscosity value. However, in the case of high viscosities which lead to low Reynolds numbers, the viscosity value can drastically affect the results. In this case, some solvents were injected during the well test to reduce the liquid viscosity, and we present the consequences on the multiphase flow meter. We also compare the multiphase flow meter results to the gas/oil ratio measured in the laboratory on downhole samples from the same reservoir, to the water cut measured manually during the operations and to the tank measurements. Finally, we present some recommendations on the use of multiphase flow meters for heavy oil well tests.

Objectives/Scope: During the period of two years the difference between sum of daily oil flow rate measurements of each oil production well using multiphase flow meter (MPFM) and cumulative daily oil production rate measured by custody transfer meter increased overall by 5%. For some wells inaccuracy of MPFM liquid rate measurement could reach 30-50%. The main goal of this research was to improve the accuracy of multiphase flow meter production rate measurements. Methods, Procedures, Process: More than 80 oil production wells were involved in the research, more than 100 flow rate tests were carried out. Machine learning methods such as supervised learning algorithms (linear and nonlinear regressions, method of gradient descent, finite differences algorithm, etc.) have been applied coupled with Integrated production modelling tools such as PROSPER and OpenServer in order to develop a function representing correlation between MPFM parameters and flow rate error. Results, Observations, Conclusions: The difference between cumulative daily oil production rate measured by custody transfer meter and multiphase flow meters decreased to 0.5%. The solution has been officially applied at the oil field and saved USD 500K to the Company. The reliability of the function was then proved by the vendor of MPFMs. Novel/Additive Information: For the first time machine learning algorithms coupled with Integrated Production modelling tools have been used to improve the accuracy of multiphase flow meter production rate measurements.

Accurate metering of flow rates and prediction of water breakthrough are important issues in offshore oil production. The multiphase flow rates can be found for instance from frequent well testing, from multiphase flow meters, or from software simulations. Multiphase flow meters are very expensive and so are single-well tests, especially in cases with long tiebacks. On the contrary, software simulations are cheap, they can be made accurate, and simulations can usually be based on existing sensors. Also, software is easy to install, operate, and maintain compared to hardware multiphase meters. In this article we describe the main components needed in a flexible software system for flow metering that can handle sparsely instrumented production facilities. Several sources of implicit information are pointed out, and a tuning strategy that avoids single-well tests is described. We also present examples that show the power of advanced simulations in situations that are not well handled by multiphase flow meters and standard software because of very low flow rates. What makes the situation in the examples even harder is that: There is no available information about the choke, the bottom hole sensors fail, and the temperature measurements are influenced by the seawater temperature. Despite this, good results are obtained. Introduction The offshore oil industry is facing a market where the number of brown fields is growing, and where more and more of the green fields are marginal fields. In addition, many of the new findings are on deep water. These facts all point in the direction of more production networks with long tiebacks, a stronger need for limiting the number of expensive well tests, and a push towards minimizing OPEX in general. The need for reliable monitoring of the production from each well in a production network is therefore continuously increasing. Furthermore, reservoirs may have more than one owner, whereas the various owners may share the same infrastructure and production facilities. In such cases reliable production monitoring is of course highly demanded. A software system for flow rate estimation may satisfy the above needs. Software is relatively cheap, and both installation and usage can be made very easy. Furthermore, software is easily maintained and supervised from remote, whereas hardware, such as a multiphase flow meter, usually needs to be maintained on the site. This is an important issue since many larger green fields are far away from where many of the oil companies' expertise traditionally is situated. Remote access can easily be obtained using standard web browsers and internet technology. However, if the instrumentation is sparse or inaccurate most software systems will not give reliable rate estimates. In this article we will look at how to deal with such problems. We will present the main building blocks for a flexible software system that can handle almost any production network configuration. We also show how inaccurate measurements and what may look like useless information may contribute to good rate estimates.

Proper testing and measuring of a gas-condensate well is essential to ascertain the state of the hydrocarbon system at reservoir conditions and to plan the best production and recovery program for the reservoir. At surface, the volumetric measurement of three phase flow of a wet gas well has been considered challenging. Existing conventional metering devices and ordinary multiphase flow meters (MPFMs) are usually incapable of providing accurate measurement of flow rates in a dominating high gas volume fraction. Portable separators equipped with liquid gauge tanks are still considered accurate, but are bulky and expensive. This paper discusses the results obtained from tests conducted using multiphase flow meters in wet gas-condensate producers at a wide range of flowing conditions. The meters were installed in conjunction with a regular three-phase test separator, and the objective of the tests was to measure meter accuracy in comparison with the data obtained from the conventional test separator. Obtaining accurate three-phase data in wet gas producers poses a significant challenge for the majority of conventional multiphase flow meters, because of the high-gas volume fraction (95 % and above) nature of the wells. The paper also discusses some lessons learned, challenges and uncertainties associated with accurate flow measurement of a wet gas well encountered throughout the testing, as well as other important observations. The tests were conducted by Saudi Aramco over a four-month period using two MPFMs (A and B) in five different wells, spread out throughout the Ghawar field.The tests were conducted in wells producing at rates ranging from 10 to 40 MMSCFD, and condensate-gas ratios ranging from 30 to 320 Bbls/MMSCFD. Three tests were conducted in each gas producer, totaling 15 tests. The testing concluded that both meters require technological upgrading to meet the acceptance criteria in measuring the three phase flow with acceptable accuracy.

Historically well production flow surveillance has been attracting attention from reservoir engineers. The accurate estimation of individual well contribution, Has profound impact on reservoir management. Often production allocation suffers from multiple issues such as quality of available production data and production rate estimation in case of inaccurate and uneconomical measurements. These issues can be mitigated if the well has accurate, calibrated Multiphase Flow Meters (MPFMs) and Virtual Flow Meters (VFMs). Reliability of digital meters is primarily dependent on regular well testing to keep the model updated and tuned to latest reservoir characteristics. As an industry wide common practice, well tests are performed in a timely manner to ensure to allocate production accurately to each well. But performing solo well tests for subsea wells is not trivial as it may seem. Differed production due to downtime associated with flowing subsea wells to a test separator and inability of mature wells to flow to the test separator result in additional challenges. Under such situations, commingled well tests are performed to estimate well rates. Individual well rates can be estimated using test by difference (TBD) method. The apparent simplicity of the TBD method often results in incorrect estimation of allocation rates as the test conditions and production conditions differs significantly. As a result, the credibility of the forecast and the ability to optimize production are compromised. In this paper a novel application called Commingled Well Test Analysis (CWTA) is reviewed which addresses these aforementioned issues. This integrated application comprises of data loading interface, data visualization interface, core optimization engine and an interactive user interface. To demonstrate the capability, three wells from a deep water conventional wells are analyzed using commingled well tests and well performance parameters (PI, WCUT and GOR) are obtained. Based on the tuned parameters, well rates are predicted and compared against rates obtained from sales meter and MPFM meters. As per the comparison, well allocation rates are validated and health of MPFM meters are assessed. As an additional benefit, proper designing of commingled well tests enable in 25-30% time savings of well testing.

This paper introduces the reasoning, methods, and technology behind using a Remotely Operated Vehicle (ROV) to obtain a sample of production fluids from offshore sub-sea installations. Subsea sampling in this context involves physically delivering equipment to sub-sea installations, connecting to said installations, obtaining a measure of the product in question, and returning said measure of fluid to the surface in a specified condition. Methods and technologies relating to the transport and laboratory analysis of the product once delivered to the surface are examined briefly, but not in great detail. An explanation of why the offshore oil and gas industry is increasingly interested in subsea sampling, and the value it brings to operating companies is presented. What is Subsea Sampling? Subsea Sampling is the act of retrieving a measure of production fluid from a subsea installation such as a production tree, manifold, or other junction for the purpose of testing and recording information about said fluid. The quantity and type of fluid that is retrieved depends on the information that is required from the sample. The information retrieved from samples includes but is not limited to chemical composition, pressure volume temperature (PvT) analysis, salinity, and density. The values obtained for the different attributes are used to calibrate subsea multiphase flow meters and manage reservoir production and intervention schedules. Obtaining samples from installations in deep water, 5000 feet or more necessitates the use of remote operated vehicles (ROVs) to carry and manipulate the sample gathering and transport equipment, or the use of down wire tooling packages. The focus of this paper is the former. Why is Subsea Sampling Performed? Offshore oil and gas producers perform, or plan to perform subsea sampling for two reasons. The first reason is to support the use of subsea multiphase flow meters. The second reason is gathering data to support reservoir management efforts.

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.

Multiphase flow metering is a challenging task because of the complexity of multiphase flow. In this paper, nonintrusive multiphase flow metering techniques, including machine learning (ML) / artificial intelligence models for the identification of flow regimes and estimation of flow parameters of a two-phase flow in a horizontal pipe are proposed that use data from Electrical Capacitance Tomography (ECT) and conventional measurements such as differential pressure in the pipe. The flow regimes are classified into five types, namely plug, slug, annular, wavy and stratified. Two-phase air/water flow experimental data from ECT are collected by running extensive experiments using the horizontal section of the multiphase flow rig at the University of South-Eastern Norway (USN). Exploratory data analysis (EDA) is performed on these data to extract features for use in classification and regression algorithms. Time series of normalized capacitance data from ECT sensors are used to classify flow regimes and identify flow parameters. ML techniques of Artificial Neural Network, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are used to classify flow regimes by using features extracted from ECT data. The cross-correlation technique is used to estimate flow velocity using data from a twinplane ECT module. ML regression techniques are used to estimate phase fractions. Fusing data from differential pressure sensors enhances the flow regime classification. An overall system performance is given with suggestions for designing dedicated control algorithms for actuators used in multiphase flow control.

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.

Multiphase flow metering is very important in the oilfield. We construct four new liquid metering models (Falling Model I, Falling Model II, Open Channel Model and Element Resistance Model) and a gas metering model based on the online multiphase flow metering equipment without separation. The experimental study was carried on indoors at the multiphase flow metering equipment, and the model coefficients can be figured out by the method of date classification. We can figure out the error of each phase, and evaluate all the metering models. Through errors analysis, the average error of the liquid metering models can be within 10%. The error of gas metering model can be controlled within 20%. The experiment results proved that these metering models can be used in the multiphase metering with different viscosity. The metering precision is rather high in the wide range of gas phase and liquid phase flow rate. And it fits in with outdoor application.

This paper presents a performance evaluation of multiphase flow models when using these models on Virtual Flow Meters (VFM). Recently, VFM simulators have been identified as a technology capable of provide metering backup, monitoring for abnormalities of physical multiphase flow meters and also for production well allocation. Nevertheless, there are just a few studies found in the literature analysing the accuracy of different VFM models applied to multiphase flow metering. In the oil and gas industry it is essential to accurately measure the amount of oil, gas and water being produced by individual wells. Prior to the 1980s, single-phase measurements alone were sufficient to meet the industry standards. However, with the advent of deepwater exploration and production, multiple wells comingling to a single production separator is a common practice in petroleum production for offshore systems. Therefore, the use of multiphase flow meters to account for the production of oil, gas and water from individual wells became evident. This investigation uses field data for more than one well to evaluate the performance VFM simulators. The results obtained from this study shows that VFM models can provide accurate predictions for multiphase flow metering. The simulators evaluated in this study could predict flow rates of oil, gas and water within acceptable levels of uncertainty, with an average absolute error of 20%.

Reservoir and production engineers rely heavily on well production rates to optimize well activities such as ensuring optimum reservoir monitoring. Individual gas well rates are not readily available, rather, they can be estimated thru multi-phase flow meter (MPFM) and well test analysis. These methods are associated with certain limitations such as high cost, high uncertainty, and technically elaborate calculations. Consequently, empirical and numerical calculations are employed with well test data to calculate daily rates. These practices lead to inaccurate gas rate estimations. A model with an ability to provide accurate estimates of gas rates for a gas reservoir can serve as a handy tool for the subsurface engineers in addressing well and reservoir optimization strategies. This work presents artificial intelligence models to estimate gas rates in a gas field containing ten wells. The aim is to develop a correlation that is simple and easy to incorporate yet providing robust answers on a global scale. Multiple machine learning tools are employed. These include; Artificial Neural Network (ANN), Functional Network (FN), and Adaptive Neuro Fuzzy Inference System (ANFIS). Production data from a dry gas field X was used for the model development. Data cleaning and data reduction steps were carried out to ensure the input parameters for the proposed model are physically relevant and accurate. Missing these steps would result in the development of an erroneous correlation, i.e., garbage -in garbage-out (GIGO). This led to finalization of certain basic well-head parameters which are available at any typical well and had direct impact on the output production rate. The target parameter for model training is the gas rate. A rigorous comparison between the investigated artificial intelligence models was conducted by calculating average absolute percentage error (AAPE) and coefficient of determination. The comparative analysis shows that the intelligent model is able to predict the gas rate in condensate wells with accuracy in excess of 90%. Examples of such large accuracy has not been reported previously. ANN performs a step ahead as compared to the various intelligent algorithms used in this study. This paper sheds light on the potential of the Industrial Revolution 4.0 for the Pakistani Oil and Gas Sector. Data-driven artificial intelligent models are capable of validating the well test and multiphase flow meter results. In addition, it can prove to be a vital tool in an engineer's tool-kit to reduce uncertainties in gas rate measurements.

A measuring system – a multiphase flow meter is installed in oil wells. It consists of a set of sensors and a computer. The flow meter registers the following set of primary data. Oil is a very useful mineral. More than four billion tons, or about five cubic kilometers, of oil is produced annually in the world. Oil production is a complex of production and technological processes aimed at extracting crude oil from the subsurface to the earth's surface. This is a complex production process that includes geological exploration, drilling and construction of wells, their repair, purification of extracted oil from water, sulfur, paraffin, and much more.