Multiphase Flow Metering Research Articles

Applications of Artificial Intelligence to Predict Oil Rate for High Gas-Oil Ratio and Water-Cut Wells.

Measuring oil production rates of individual wells is important to evaluate a well’s performance. Multiphase flow meters (MPFMs) and test separators have been used to estimate well production rates. Due to economic and technical issues with MPFMs, especially for high gas–oil ratio (GOR) reservoirs, the use of a choke formula for estimating well production rate is still popular. The objective of this study is to implement different artificial intelligence (AI) techniques to predict the oil rate through wellhead chokes. Support-vector machine (SVM) and random forests (RF) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (548 wells) was obtained from oil fields in the Middle East. GOR varied from 1000 to 9351 scf/stb, and WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. Hence, two cases were studied using each AI model. Seventy percent of the data was used to train both RF and SVM models, while 30% of the data was used to test and validate these models. The developed RF and SVM models were then compared against the previous empirical formulas. The RF model in both critical and subcritical flow conditions was able to perfectly match the actual oil rates. SVM was able to predict the general trend for the oil rates but missed some of the sharp changes in the oil rate trend. The average absolute percent error (AAPE) values in the subcritical flow for SVM and RF were 1.7 and 0.7%, respectively, while in the critical flow, the AAPE values were 1.4 and 0.75% for SVM and RF models, respectively. SVM and RF models outperform the published formulas by 34%. The results from this study will help to estimate the real-time oil and gas rates based on the available data from wellhead chokes without the need for field intervention.

Development of a deep rectifier neural network for fluid volume fraction prediction in multiphase flows by gamma-ray densitometry

This paper presents a novel methodology for volume fraction predictions in multiphase flow meters in offshore petroleum industries using gamma-ray densitometry. The algorithm that interprets spectra recorded at detectors is based on the new architectures of deep neural networks (deep learning). In this study, an 8-layer deep rectifier neural network (DRNN) has been used, running on a GPU-based parallel framework in order to increase the measuring precision of volumetric percentage of gas-oil-water system. The adaptive learning rate optimization algorithm (Adam) was used for DRNN training. All spectra recorded at detectors were normalized and used as the inputs of the DRNN. The detection system uses two NaI(Tl) detectors and appropriate narrow beam geometry, comprised of a (59.54 and 662 keV) dual-energy gamma-ray source and a dual-modality in order measure transmitted and scattered beams. The theoretical models consider a static oil-water-gas annular flow regime that were developed using MCNP6 code, which was used to provide training, validation and test data for the DRNN. The response-function of a real NaI(Tl) detector is also considered on the mathematical model. The results showed the root mean square error below than 0.8 for the fluids investigated in this study.

Multiphase Flow Regime Characterization and Liquid Flow Measurement Using Low-Field Magnetic Resonance Imaging.

Multiphase flow metering with operationally robust, low-cost real-time systems that provide accuracy across a broad range of produced volumes and fluid properties, is a requirement across a range of process industries, particularly those concerning petroleum. Especially the wide variety of multiphase flow profiles that can be encountered in the field provides challenges in terms of metering accuracy. Recently, low-field magnetic resonance (MR) measurement technology has been introduced as a feasible solution for the petroleum industry. In this work, we study two phase air-water horizontal flows using MR technology. We show that low-field MR technology applied to multiphase flow has the capability to measure the instantaneous liquid holdup and liquid flow velocity using a constant gradient low flip angle CPMG (LFA-CPMG) pulse sequence. LFA-CPMG allows representative sampling of the correlations between liquid holdup and liquid flow velocity, which allows multiphase flow profiles to be characterized. Flow measurements based on this method allow liquid flow rate determination with an accuracy that is independent of the multiphase flow profile observed in horizontal pipe flow for a wide dynamic range in terms of the average gas and liquid flow rates.

Experimental investigation of gas-liquid flow in a vertical venturi installed downstream of a horizontal blind tee flow conditioner and the flow regime transition

A venturi device is commonly used as an integral part of a multiphase flowmeter (MPFM) in real-time oil-gas production monitoring. Partial flow mixing is required by installing the venturi device vertically downstream of a blind tee pipework that conditions the incoming horizontal gas-liquid flow (for an accurate determination of individual phase fraction and flow rate). To study the flow-mixing effect of the blind tee, high-speed video flow visualization of gas-liquid flows has been performed at blind tee and venturi sections by using a purpose-built transparent test rig over a wide range of superficial liquid velocities (0.3–2.4 m/s) and gas volume fractions (10–95%). There is little ‘homogenization’ effect of the blind tee on the incoming intermittent horizontal flow regimes across the tested flow conditions, with the flow remaining intermittent but becoming more axis-symmetric and predictable in the venturi measurement section. A horizontal (blind tee) to vertical (venturi) flow-pattern transition map is proposed based on gas and liquid mass fluxes (weighted by the Baker parameters). Flow patterns can be identified from the mean and variance of a fast electrical capacitance holdup measured at the venturi throat.

Dispersed-phase velocities for gas-liquid vertical slug and dispersed-bubbles flows using an ultrasonic cross-correlation technique

There is a consensus in the oil industry that multiphase flow meters (MFMs) can bring various benefits to the oil industry. MFMs would reduce the minimum space and weight requirements of the platforms as they would eliminate the separating tanks and the interconnection pipes. One of the important variables to MFMs is the dispersed-phase velocity that is a closure relationship to mechanistic models. In this context, this paper studied the use of the ultrasonic cross-correlation technique based on the energies of the reflected pulses to estimate the dispersed-phase velocities for the dispersed-bubbles and the slug flow patterns. The experiments were done in a vertical two-phase liquid-gas flow pipe, with superficial liquid and gas velocities ranging from 0.5 to 3.9m/s and 0.1 to 3.7m/s, respectively. Also, the results were compared with image analysis and mechanistic models. For dispersed-bubbles flows, the relative deviation from the mixture velocities was less than 20%. For slug flows, the relative deviations from the images were less than 15%.

A Novel Multi-Sensor Fusion Algorithm Based on Uncertainty Analysis.

During the research and development of multiphase flowmeters, errors are often used to evaluate the advantages and disadvantages of different devices and algorithms, whilst an in-depth uncertainty analysis is seldom carried out. However, limited information is sometimes revealed from the errors, especially when the test data are scant, and this makes an in-depth comparison of different algorithms impossible. In response to this problem, three combinations of sensing methods are implemented, which are the “capacitance and cross-correlation”, the “cross-correlation and differential pressure” and the “differential pressure and capacitance” respectively. The analytical expressions of the gas/liquid flowrate and the associated standard uncertainty have been derived, and Monte Carlo simulations are carried out to determine the desired probability density function. The results obtained through these two approaches are basically the same. Thereafter, the sources of uncertainty for each combination are traced and their respective variations with flowrates are analyzed. Further, the relationship between errors and uncertainty is studied, which demonstrates that the two uncertainty analysis approaches can be a powerful tool for error prediction. Finally, a novel multi-sensor fusion algorithm based on the uncertainty analysis is proposed. This algorithm can minimize the standard uncertainty over the whole flowrate range and thus reduces the measurement error.

Flow Measurement Optimization Using Surface Measurements and Downhole Sound Speed Measurements from Local or Distributed Acoustic Sensors

Summary The litmus test for downhole multiphase flowmeters is to compare the measured phase flow rates with the rates from a test separator or other surface measurement systems. In most cases, the composition of the measurand is required for flowmeters. This is typically obtained from bottomhole fluid samples. Extracting and analyzing fluid samples is an expensive process mostly done at the initial stages of field development. In some cases, the composition may be old or unavailable, leading to subpar flowmeter performance compared to surface systems. In this work, it is shown that when the data from a surface system such as a test separator are used in conjunction with the mixture sound speed measured downhole, it is possible to optimize a downhole multiphase flowmeter system without obtaining new fluid samples. The optimization process is independent of the downhole measurement device because the required flow-velocity and sound-speed measurements may be obtained from separate devices. For example, the fluid bulk velocity and mixture sound speed can be measured by a local measurement device and a distributed acoustic sensing (DAS) system, respectively. The main challenge in a flow-velocity/sound-speed measurement system is determining individual phase sound speeds so that the mixture phase fraction can be correctly determined using Wood’s mixture sound speed model. The phase fraction from the separator tests can be used as the target value to optimize the performance of the system. The system has two operation modes. In optimization mode, the individual phase sound speeds are calculated backward using the predicted phase fractions from surface measurements. Pressure and temperature variations at measurement locations, as well as pipe compliance effects, are accounted for during the process. After the adjustment of individual phase sound speeds, steady-state operation mode takes over, and a forward calculation is implemented using the same model. The final phase fraction agrees well with the actual value and can be improved further with an iterative approach. This novel method is demonstrated in a North Sea case history. A downhole optical flowmeter in a North Sea field measured mixture velocity and sound speed. Well-test results indicated that water cut from the flowmeter was underreported and phase flow rates did not match test-separator rates. Instead of halting production and going through a fluid sample analysis cycle, the test-separator water cut was used as the target value to optimize oil phase sound speed using Wood’s model in the optimization mode. The difference between the initial and optimized oil sound speeds was extrapolated to other pressure and temperature conditions, and steady-state operation mode showed that separator tests and flowmeter measurements closely matched. Subsequent flowmeter and test-separator data confirmed excellent agreement. Using surface measurements and downhole mixture sound speed to optimize phase flow rates is a novel method that has not been previously demonstrated. This method is independent of device type, is broadly applicable, and improves the understanding of multiphase flow measurement.

Nuclear Magnetic Resonance Multiphase Flowmeters: Current Status and Future Prospects

SummaryMultiphase flowmetering is a requirement across a range of process industries, particularly those that pertain to oil and gas. Generally, both the composition and individual phase velocities are required; this results in a complex measurement task made more acute by the prevalence of turbulent flow and a variety of flow regimes. In the current review, the main technical options to meet this metrology are outlined and used to provide context for the main focus on the use of nuclear magnetic resonance (NMR) technology for multiphase flowmetering. Relevant fundamentals of NMR are detailed as is their exploitation to quantify flow composition and individual phase velocities for multiphase flow. The review then proceeds to detail three NMR multiphase flowmeter (MPFM) apparatus and concludes with a consideration of future challenges and prospects for the technology.

Conductance Sensor-Based Flowrate Estimation of Horizontal Gas-Water Slug Flow From Interfacial Wave Statistics

As more and more oil fields enter the high water-cut stage, it is of great significance to develop low-cost multiphase flow meters (MPFMs) for high water-cut flows. Based on characteristic parameters of the interfacial wave, this article presents a cost-effective method for flowrate measurement of gas-water slug flow in the horizontal pipeline, which has a great potential to be developed as a key technique for gas-oil-water flow with high water cut. A series of experiments have been carried out on our laboratory flow loop in which the pipeline inner diameter is 50 mm. The superficial velocities of gas and water range from 0.97 to 5.73 m/s and 0.36 to 0.95 m/s respectively. The conductance sensors are used to extract the characteristic parameters of the interfacial wave including the average liquid film thickness and the wave velocity. The system of equations describing the relationships between the flowrates of different phases and the two characteristic parameters is established. By solving the system of equations, the flowrates of gas and water can be estimated. The experimental results show that the proposed method can give predictions for gas flowrate and water flowrate with the mean absolute percentage error (MAPE) in 2.95% and 8.95% respectively, which is promising for developing a cost-effective MPFM for high water-cut flows.

Investigation on different scenarios of two-phase flow measurement using Orifice and Coriolis flow meters: Experimental and modeling approaches

In this study, various methods for multiphase flow metering were introduced. These techniques include measurement of gas-liquid average density by a Coriolis flow meter and determining the gas volume fraction by discharge coefficient of an orifice plate. Application of Orifice plate and Coriolis flow meters were investigated for gas-liquid flow metering. The principle of using these instruments as single-phase flow meters, for measurement of the two-phase flow was not focused in previous studies. In the second part, gas volume fraction was predicted by a soft sensor. Variation of Coriolis meter factor with gas volume fraction and Reynolds number was studied. Moreover, changes of pressure drop and discharge coefficient of Orifice with Reynolds number was investigated. On the other hand, 250 data sets were collected to develop a soft sensor using 2-layer and 3-layer neural networks optimized by genetic algorithm and Least Square Support Vector Machine. Increasing Reynolds number led to decrease in Coriolis meter factor. Among various computational intelligence methods, 3-layer neural network was the best, with mean square error and mean absolute percent error of 0.91% and 3%, respectively and computation time of 0.0001 s.

Анализ результатов промысловых испытаний и опытной эксплуатации многофазных расходомеров “УЛЬТРАФЛОУ” на промыслах ООО “ЛУКОЙЛ-ПЕРМЬ”

Анализ результатов промысловых испытаний и опытной эксплуатации многофазных расходомеров “УЛЬТРАФЛОУ” на промыслах ООО “ЛУКОЙЛ-ПЕРМЬ”

Linearization Point and Frequency Selection for Complex-Valued Electrical Capacitance Tomography

Benefiting from the ability to image the permittivity distribution of dielectric materials, electrical capacitance tomography (ECT) has been applied for multiphase flow metering for decades as a contactless method. However, the water-continuous flow brings challenges for ECT since the conductivity in water makes ECT fail to reconstruct the distribution. Therefore, complex-valued ECT (CV-ECT) is introduced to image both permittivity and conductivity distribution based on complex-valued capacitance measurements using the same sensor head of ECT. Different from conventional ECT, the investigation of excitation frequency and linearization point selection is vital for CV-ECT, as the conductivity information is coupled with permittivity and frequency. An eight-electrode CV-ECT system was set up to obtain measurements both in simulations and experiments. The measurements on different phantoms over different excitation frequencies were conducted, and the images were reconstructed to elaborate the selection of the linearization point and excitation frequency range.

Defining the flow rates in horizontal two-phase water-gas flows and operation features of a conical narrowing device in a multiphase flowmeter with a gamma-densitometer

Defining the flow rates in horizontal two-phase water-gas flows and operation features of a conical narrowing device in a multiphase flowmeter with a gamma-densitometer

Health, Safety, and Environment Operational Excellence through Full Utilization of Intelligent Field

Summary Working in the oil industry comes with unique challenges and risks, and so extra precautions and safety measures coupled with strict environmental compliance must be applied. Contrary to the common belief that strict safety enforcement could hinder smooth operations, the deployment of new technologies and enhanced solutions of processes has enabled operational excellence (OE) and improved safety performance. In this paper, we demonstrate health, safety, and environment performance improvement through implementing two main initiatives: The first category has initiatives that require less intervention or personnel; for example, the deployment of cableless pressure sensors or permanent monitoring systems in key wells to ensure continuous real-time pressure data to monitor reservoir pressure. The second category has initiatives that mitigate traditional health, safety, and environment risks; for example, through use of multiphase flow meters (MPFMs) to collect accurate and continuous flow measurements instead of traditional well testing. Optimizing operations costs while maintaining a high-level of safety is achieved through a dedicated team working in a state-of-the-art Production Operations Surveillance Hub (POSH), which enables the monitoring of wells in real time, making production optimization decisions, and ensuring a high level of well integrity via close monitoring of wells and assets.

Statistical characterization of horizontal slug flow using snapshot proper orthogonal decomposition

Slug flow is an intermittent and complex gas-liquid flow pattern, that can cause severe problems in industrial operations and lead to large uncertainties in multiphase flow metering. These problems are primarily caused by the liquid slugs of the flow. In this context, different cases of slug flow in horizontal pipes are investigated to find a statistical characterization of the slugs in time and space. To achieve this, snapshot proper orthogonal decomposition (snapshot POD) with an additional mode coupling algorithm is employed, which provides an energy-ranked mode basis of the underlying coherent structures. It is shown that the characterization for the considered slug flows can be derived from the temporal and spatial information of the dominant mode pair. In that regard, the slug frequencies, the averaged slug body length and an energy representation of the slugs are obtained from this method. Furthermore, the accuracy of this characterization is shown and the influence of the size of the observed pipe section on its reliability is investigated.

Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter

Multiphase flowmeters have an important role to play in the industry and any attempts that lead to improvements in this field are of great interest. In the current study, group method of data handling (GMDH) technique was applied in order to increase measuring precision of a simple photon attenuation based two-phase flowmeter that has the ability to estimate the gas volumetric percentage in a two-phase flow without any dependency to flow regime pattern. The simple photon attenuation based system is comprised of a cobalt-60 radioisotope and only one 25.4 mm × 25.4 mm sodium iodide crystal detector. Four extracted features from recorded photon spectrum in sodium iodide crystal detector were used as the inputs of GMDH neural network. Equations related to the combination of the features and the error rate of each approximation is also reported in this paper. Applying the mentioned technique, the gas volumetric percentage in an oil-gas two phase flow was determined with the root mean square error of less than 2.71 without any dependency to the flow pattern. The obtained measuring precision in this study is at least 2.1 times better than reported in previous studies.

On the life and habits of gas-core slugs: characterisation of an intermittent horizontal two-phase flow

Slugs are intermittent structures observed in horizontal co-current gas-liquid two-phase flow. Published data examining horizontal flow indicate the existence of intermittent sub-flow patterns. There is limited work performed on the study of this sub-categorisation. This paper presents a method, exploiting non-intrusive Electrical Capacitance Tomography (ECT) measurements, to assess and to characterise the spatial and temporal features of horizontal gas-oil flow. A logic-based algorithm is proposed to automatically identify and classify flow regimes. This algorithm uses the spatial distribution of the phases, based on measurements of the intrinsic dielectric properties of the fluids. The measurements were performed at the NEL UK national standard multiphase flow loop using a high-speed dual-plane ECT sensor array. The ECT measurements identified intermittent liquid structures with a sustained gas-core in the central region of the pipe cross-section with outer features analogous to those of liquid slugs. Assessment of the frequency and time span of the periodic structures showed that gas-core slugs are, typically, shorter than full slug structures but are more frequent for the same given time period. For both flow structures, the mixture velocity was a determinant factor, with gas-core slugs dominating the flow at higher velocities. The experimental work led to the modification of existing flow pattern transition models. The modified flow regime maps suggested in this study aim to widen the applicability of the results by enabling the prediction of gas-core slug flow for similar experimental and operating conditions. The present work comprises the initial steps towards automatic flow regime identification in multiphase flow metering. Automatic flow regime recognition has the potential to lead to better process control and improved accuracy in production allocation.

Multiphase Flow Meters Targeting Oil & Gas Industries

In oil fields, multiphase flow meters (MPFMs) yield important data for proper reservoir management to maximize the oil-gas production throughput. MPFMs are also required in downstream industries such as refineries to monitor the quality of the end product and in power generation to estimate the amount of wet steam, which if its flow rate is high may damage the blade’s turbines. For decades, radioactive γ-ray-based MPFM remains the most widely used and reliable device, especially for a high gas void fraction (GVF), which is however not safe and hazardous. This has prompted researchers to suggest other alternative technologies, among which some were successfully assessed either in multiphase flow loops or even in oil-gas fields. Most of these technologies still can’t operate for all flow conditions and are relatively expensive hindering their wide industrial deployment. This review paper presents the most recent findings in multiphase flow metering and the main research challenges encountered in each of them. Important and significant research works are cited in an attempt to provide a reasonable cross-section of various techniques. The paper can be useful for either a fresh researcher in the area or for a skilled R&D engineer involved in the design of a MPFM.

Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

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.

An ultrasonic-based multiphase flow composition meter

Abstract Accurate multiphase flow metering using non radio-active sensors is highly required in oil fields. This paper suggests a new concept of multiphase flow metering of flow composition with experimental validation in a large lab-scale multiphase flow loop. The device uses an online flow conditioner which comprises a swirl cage to accelerate the liquid-gas centrifugal forces and generate an annular flow with the liquid phase as outer phase. This recommends the usage of liquid-type clamp-on ultrasonic array probe downstream the separator to measure simultaneously and in real-time both the gas-void fraction (GVF) and the water-cut within the liquid phase. The ultrasonic array features high resonance frequency (i.e. 5 MHz in this paper) and small pitch distance (i.e. 0.5 mm pitch between sensors elements), which helps improving the measurement accuracy. Extensive experiments were conducted using an in-house built multiphase flow loop. The corresponding results indicate that for water-cut ranging from 0 to 95% and GVF ranging from 10 to 60% the meter achieves a maximum percentage error of 2% and 3.5% for water-cut and GVF measurement respectively. Exploring further characteristics of ultrasonic waves such as the amplitude of the received signal can help improving the accuracy even further for higher GVF, which however can’t reach 100% since the probe requires a liquid film to operate.