Multiphase Flow Rate Research Articles

An Effective Method for Reproducing the Flow of a Gas-Liquid Mixture Using a Flow Standard

The appearance on the market of measuring equipment of multiphase flowmeters used in the oil and gas industry to measure the amount of extracted hydrocarbons directly at wells without preliminary separation and performing the function of operational accounting raised the question of their metrological support in accordance with the requirements of regulatory documents of the sphere of state regulation in this field of measurements. His decision was to create a reference base, the basis of which was the State primary special standard for units of mass flow of gas-liquid mixtures GET 195-2011 and a verification scheme for measuring multiphase flow rates, regulated by GOST 8.637-2013. When transmitting units of volume and mass flow of a multiphase mixture with the required accuracy, the main problem is the difficulty of reproducing and maintaining stability of several flow parameters at once – mass flow and content of liquid components (water and oil simulator) and volume flow of the gas phase. It should also be borne in mind that test programs for the purpose of approving the type of multiphase flow measuring instruments, as well as verification and calibration methods, provide for changing the modes of reproduction of a gas-liquid mixture, covering the measurement ranges of multiphase flowmeters. Such standards used in practice represent a very complex measuring system consisting of storage tanks for components, supply pumping and compressor units, control equipment, mixers and separators, measuring instruments for the physical parameters of multiphase flow components and an automated control system. Given the high cost of multiphase standards, when creating them, effective methods and methods should be used to reproduce the flow of a multiphase mixture with specified parameters, as well as to make a fairly rapid transition to the next mode provided by the program or methodology, which will reduce the time and production costs for the procedure of transferring units of measurement from the standard to the working measuring instrument. This article describes a method for reproducing the flow rate of a gas-liquid mixture according to the original technological scheme introduced during the development of the 1st category standard. Constructive solutions are presented that increase the efficiency of the liquid component separation apparatus to increase the dosing accuracy, as well as expand the range of reproduction of the flow rate and parameters of the multiphase flow, ensuring the consistency of the composition of the liquid mixture at a given regime. An original rational method of compressing and maintaining the pressure of the gas phase is described, which allows you to quickly switch to the next mode, confirmed by the results of experimental studies of the standard.

Deep learning-assisted dual-modal tomography for phase flow rate estimation in two-phase oil-water flow systems

Accurately estimating phase flow rates in multiphase systems is crucial for many industries, where precise measurements are essential for operational efficiency and safety. Addressing this issue, this paper introduces an approach that employs deep learning-assisted dual-modal electromagnetic flow tomography (EMFT) and electrical tomography (ET) to predict both oil and water flow rates in two-phase oil-water flows. To facilitate the generation of the data, we first simulate diverse flow conditions using COMSOL Multiphysics software and the convection–diffusion equation, aiming to create a realistic representation of two-phase oil-water flows. The dual-modal system measurement data, generated from these simulations and simulated by using a dense finite element mesh, provide reliable inputs for the deep learning model. Moreover, this study also integrates experimental data into both the training and testing phases, improving the ability of the proposed approach to estimate flow rates accurately in practical investigations. The results from laboratory experiments demonstrate the potential of the deep learning-assisted dual-modal ET and EMFT approach in effectively resolving the challenges of estimating flow rates in two-phase oil-water flow systems. By combining the deep learning capabilities with dual-modal tomography, this study offers valuable insights for future applications and represents a significant step forward in the field of multiphase flow rate estimation.

INCREASING THE GASLIFT EFFECTIVENESS BY ADJUSTING MULTIPHASE FLOW HYDRAULIC CHARACTERISTIC

Real media that are encountered in hydrocarbon production industry are normally represented by systems, comprised of at least two phases, of which one is dispersed and distributed in another. As it is known, production, gathering, preparation and transport of oil and natural gas are based on multiphase technology, comprised of lifting the hydrocarbons from the formation to the surface, transport to the separation facilities, separation and intrafield transport of multiphase mixtures, consisting of oil, natural gas, formation water and mechanical particles. This article strives to determine the optimal work regimes of production of gaslift systems based on the relations between production rates and gas pressure in cylindrical flows. Knowing the parameters of the well borehole as well as the parameters of the produced fluid and gas injection rates it is possible to determine the optimal flow regime as a derivative of a function of pressure losses versus production rates (ΔP/Q). This function is called the flow hydraulic characteristic, and it is this function, by optimizing which an optimal flow regime of the gaslift operated well can be achieved. Estimation the hydraulic characteristic relies heavily on calculation of pressure losses of the gaslift producing well. These losses consist of two independent factors: gravitational and frictional. Frictional pressure losses have linear dependence versus the flow rate, while gravitational losses exhibit an inverse, hyperbolic, correlation to changes in flow rate. To get the full picture of the pressure loss both aspects must be accurately calculated from the given field data. In order to do so, it is important to estimate real gas content of the multiphase flow, which is a complex function of direction and velocity of the multiphase flow, gas and liquid flow rates, gas slippage, etc. Once the pressure losses have been calculated it is comparatively simple to optimise the gas flow rate to adjust the hydraulic characteristic, guiding the gaslift production process towards optimal production rates. Keywords: Gaslift, multiphase flow, pressure losses, joint transport, gas content.

Multiphase flow rate prediction using chained multi-output regression models

Virtual flow meters (VFM) are emerging as an attractive and cost-effective alternative to traditional multiphase flow meters to meet monitoring demands, reduce operational costs, and improve oil recovery efficiency. However, no previous studies have accounted for the correlations between the oil, water, and gas flow rates when developing machine learning models. This study proposes a chained regression model for multiphase flow rate prediction to account for such relationships. Real-field data consists of 375 data points for sensory measurements, including pressure, temperature, and choke opening levels, and 42 data points for oil, water, and gas flow rates that were measured downstream of the wellhead, which was acquired over one month. Two robust algorithms, Support Vector Machine (SVM) and Gaussian Process (GP), were employed to develop the chained regression model. The evaluation metrics such as mean absolute percentage error (MAPE) for all the models were estimated using a repeated hold-out approach of cross-validation. The response variables, i.e., the three flow rates, were moderate to strongly correlated. The results showed that the GP-based chain regression model was significantly better than the direct model using the GP algorithm for oil (MAPE: 2.07% vs. 2.27%) and gas (MAPE: 2.5% vs. 2.65%) flow rate prediction (p < 0.01). Overall, the chained model is generally superior to the direct model for flow rate prediction, which was supported by the ranking scores, consistently outperforming the latter in both SVM (79 vs. 87) and GP (64 vs. 70) based approaches. The sensitivity analysis showed that the GP-based chained model accurately predicted oil, water, and gas flow rates within 39.45 m3/day, 14.69 m3/day, and 5.63 m3/day, respectively, of the actual values for approximately 92% of the data points. This study’s findings can be instrumental in designing and developing practical and accurate VFM for multiphase flow rate prediction.

A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes

Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow rates. Several correlations have been suggested to model the multiphase flow of oil and gas via surface chokes. However, substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity, anisotropism, variance in reservoir fluid characteristics at diverse subsurface depths, which introduces complexity in production data. Therefore, the estimation of daily oil and gas production rates is still challenging for the petroleum industry. Recently, hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum domain. This paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke (viz. stacked generalization and voting architectures), followed by an assessment of the impact of input production control variables. Otherwise, machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North Sea. Feature engineering has been properly applied to select the most suitable contributing control variables for daily production rate forecasting. This study provides a chronological explanation of the data analytics required for the interpretation of production data. The test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting.

Regime independent flow rate prediction in a gas-liquid two-phase facility based on gamma ray technique and one detector using multi-feature extraction

One of the key challenges in petroleum related industries is how to precisely measure the flow rates of individual phases in a pipeline. To address this challenge, in the present study, an automated two-phase test loop capable of generating different flow patterns in horizontal pathway is used to perform experiments on the flow rates. The measuring package set-up consists of a Cs-137 radiation source with photon energy of 662 keV and one NaI (Tl) scintillation detector to register transmission counts. Multi-layer perceptron (MLP) is the selected processing element. Distinguished property of this paper is considering a feature vector with diverse-nature elements as input and the flow rate values of water and air as the target elements. Several combinations of features were investigated to determine the feature vector which shows the best quality to predict the flow rates. Moreover, two structures of MLP with different scenarios of hidden layers were utilized for examining every feature vector set. Numerous experiments were performed to collect adequate data to train and test the ANN in a wide range of air and water flow rates. The results indicate that the proposed model achieved MAE of less than 1 and 5.9 L/min and MRE% of 1.09% and 1.45% to predict water and air flow rates, respectively. The results show that the presented ANN model outperforms existing methods on multiphase flow rates measurements. Therefore, the proposed feature extraction method is applicable to estimate phase flow rates in a two-phase flow for industrial goals.

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms.

This study presents a novel data-driven approach for calculating multiphase flow rates in electrical submersible pumped wells. Traditional methods for estimating flow rates at test separators fail to identify production trends and require additional costs for maintenance. In response, virtual flow metering (VFM) has emerged as an attractive research area in the oil and gas industry. This study introduces a robust workflow utilizing symbolic regression, extreme gradient boosted trees, and a deep learning model that includes a pipeline of convolutional neural network (CNN) layers and long short-term memory algorithm (LSTM) layers to predict liquid rate and water cut in real time based on pump sensors' data. The novelty of this approach lies in offering a cost-effective and accurate alternative to the usage of multiphase physical flow meters and production testing. Additionally, the study provides insights into the potential of data-driven methods for VFM in electrical submersible pumped wells, highlighting the effectiveness of the proposed approach. Overall, this study contributes to the field by introducing a new, data-driven method for accurately predicting multiphase flow rates in real time, thereby providing a valuable tool for monitoring and optimizing production processes in the oil and gas industry.

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Accurate multiphase flow measurement is vital in monitoring and optimizing various production processes. Deep learning has as of late arose as a promising approach for assessing multiphase flowrate dependent on various customary flow meters. In this paper, we propose a multi-modal sensor and Temporal Convolution Network (TCN) based method to predict the volumetric flowrate of oil/gas two-phase flows. The volumetric flowrates of the liquid and gas phase vary from 0.96 - 6.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{{3}}$ </tex-math></inline-formula> /h and 5.5 - 121.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{{3}}$ </tex-math></inline-formula> /h, respectively. The multi-modal sequential sensing data are simultaneously collected from a Venturi tube and a dual-plane Electrical Capacitance Tomography (ECT) sensor in a pilot-scale multiphase phase flow facility. The reference data are derived from the single-phase flowmeters. Z-score and First-Difference (FD) data pre-processing methods are employed to manipulate the collected instantaneous time series multi-modal sensing data. The pre-processed data are utilized for training the TCN model. Experimental results reveal that the TCN model can effectively predict the multiphase flowrate based on the multi-modal sensing data. The results provide guidance on data pre-processing methods for multiphase flowrate estimation and demonstrate the effectiveness of combining multi-modal sensors and TCN for multiphase flowrate prediction under complex flow conditions.

Modeling subcritical multi-phase flow through surface chokes with new production parameters

The produced hydrocarbons from underground reservoirs must eventually pass through surface chokes installed to control the surface flow rate at an optimum value, which should regularly be checked against the recommendations of the production engineers to prevent problems such as water coning. Accurate prediction of the surface flow rate is, therefore, crucial as it will lead to fulfilling the development plan goals of the reservoir and production optimization. In this regard, many correlations have been developed to predict the flow rate through surface choke and most of them being developed from only one dataset gathered from a single reservoir, hence with limited prediction capability and high error. Furthermore, these correlations predict the oil flow rate only as a function of wellhead pressure, gas-oil ratio, and choke size. In this study, two machine learning techniques are used to develop models for better prediction of the multi-phase flow rate for the oil wells using two new parameters of basic sediment and water (BS&W) and fluid temperature which were overlooked previously. A total of 182 production tests were utilized in developing these models which are covering a wide range of data. Graphical and statistical approaches are utilized to compare the forecasted values against the field data. Furthermore, absolute error is used as a statistical approach to assess the developed models based on machine learning in comparison to conventional correlations available in the published literature. The findings illustrate that an acceptable relation exists between the field data and predicted values with coefficients of determination equal to 0.9840 and 0.9706 for artificial neural network (ANN) and least squares support vector machine coupled simulated annealing (LSSVM-CSA), respectively, based on total datapoints. The results from this study will greatly assist petroleum engineers to have particular estimations of liquid flow rates from wellhead chokes.

Multiphase Flow Analysis Using Piezoelectric Leaf-Cell Sensor Array

The composition of the multiphase flow rates from hydrocarbon reservoirs has direct impact on the programs to optimize hydrocarbon production and for the identification of problems with production. However, the accurate measurement of the flow rates of oil, brine, and gas in three-phase regimes remains an intractable problem for current state-of-the-art sensor technologies. Here, we describe a piezoelectric acoustic leaf-cell sensor array providing an in situ real-time measurement of the distribution in three-phase flow fluid density over the wellbore cross section. The sensor array measurements are evaluated from experimental three-phase flow loop test data under a range of flow regimes comprised of 0–95% gas volume fraction and water/liquid volume ratio between 0 and 100%. The experimental data include three-phase flow transitions well into the nonlinear subsonic gas velocity region predicted by the Wood equation and show an average absolute error in three-phase fluid density prediction less than 0.05 g/cc over the entire test regimen.

Proxy Capacitance-Resistance Modeling for Well Production Forecasts in Case of Well Treatments

Summary We disclose a new-age field-scale production forecast model that handles complex treatment of wellbores during their life cycle. Predictive production models have been an object of increased interest and research for a long time due to the need for a fast tool for forecasting production rates or choosing an optimal field development scheme. The existing approaches based on the material balance equation have several limitations and are not very applicable for real objects. Full-scale reservoir modeling is relatively slow and requires large computing resources. In this paper, we propose a proxy model based on advanced capacitance-resistance approach. The model predicts multiphase flow rates based on the available historical data of field production and information about well treatments. In addition, it provides preferable transmissibility trends, the presence of sealed or leaking faults, and the degree of dissipation between injector-producer well pairs. The advanced feature of the model is time-dependent weight coefficients, which have not been studied previously. They help in accounting the shut-in and workover periods and can be found during the optimization procedure simultaneously. Another feature is fast calculations due to a vectorized form of the model and application of modern optimization techniques. All these options allow modeling real oil fields with a large number of wells and a complex system of production control.

Workflow to predict wellhead choke performance during multiphase flow using machine learning

Multiphase flow metering in oil and gas production wells is essential for monitoring the production performance from oil and gas reservoirs. Accurate measurements of multiphase flow rate that passes through the chock at the wellhead assembly is highly important as it provides a crucial information to estimate production to asses and optimize the reservoir performance. The current practice is to conduct production rate tests monthly for the entire wells that are connected through the same manifold. Another way to estimate the flow rate at the surface is using any of the several correlations to calculate the flow rate using production and wellhead chock data. The high uncertainty in production rate predictions is very well expected and the main source of this uncertainty is the reliance on sporadic well test data and empirical multiphase flow correlations. The objective of this study is to develop a machine learning model that outperforms the industry's widely used correlations in predicting the flow rate of critical and subcritical multiphase flow through the wellhead choke. This objective is achieved by developing a detailed workflow to generate a choke performance prediction model. Validating the accuracy and reliability of the developed model is done using actual data from more than 4000 production rate tests from different fields to investigate the model's accuracy. Testing the developed models showed that it provides a higher accuracy than the several correlations it was compared against in critical and subcritical flow. The average accuracy (correlation coefficient) of prediction was around 0.92 for critical and subcritical flow models. The developed models using machine learning provided a reliable method to predict flow rate through the wellhead chock by utilizing readily available surface data.

Simulation and experimental investigation on gas-liquid two-phase flow separation behaviors in multitube T-Junction separator

A specially designed separator for gas-liquid two-phase flow separation and measurement is proposed. The flow characteristics and working scope are studied under different gas/liquid superficial velocities and different flow patterns through FLUENT numerical simulation and experimental research. The working scope of the separator is related to both the gas and liquid superficial velocity. The separator work well under the when the gas superficial velocity ranges from 0.65 to 21 m/s, and the liquid superficial velocity ranges from 0.01 to 0.31 m/s. When the actual working condition is beyond this range, the performance is not so outstanding in case of partial slug flow and annular. Under the working range of the separator, the measurement error of gas and liquid mass flow rates is less than ±2.5%. The special structure provides a buffer space for liquid slug, which shows good shock resistance capacity under high liquid superficial velocity. The investigation offers a valuable guidance for multiphase flow rates measurement.

Intelligent Production Monitoring with Continuous Deep Learning Models

Summary Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flowmeters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines the time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared with other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network (RNN). In addition, the framework of virtual flowmetering (VFM) is analyzed from the point of multiphase measurement technology. In this work, the application of Latent ODEs for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario in which the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator. The results demonstrate the satisfactory performance of the continuous deep learning models in comparison with other machine learning methods in terms of accuracy, where the normalized root-mean-squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While Latent ODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time series, which makes it especially attractive to forecast values of multiphase rates.

When is gray-box modeling advantageous for virtual flow metering?

Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predictions. However, the superiority of gray-box models is yet to be demonstrated in the literature. This article examines scenarios where a gray-box model is expected to outperform physics-based and data-driven models. The experiments are conducted with synthetic data where properties of the underlying data generating process are controlled. The results show that a gray-box model yields increased prediction accuracy over a physics-based model in the presence of process-model mismatch, and improvements over a data-driven model when the amount of available data is small. On the other hand, gray-box and data-driven models are similarly influenced by noisy measurements. Lastly, the results indicate that a gray-box approach may be advantageous in nonstationary process conditions. Unfortunately, model selection prior to training is challenging, and overhead on gray-box model development and testing is unavoidable.

The Multiphase Flow Rate Measurement Model of Non-Full-Pipe Based on Fluid Mechanics Coupled with Iscn-Lstm Network

The Multiphase Flow Rate Measurement Model of Non-Full-Pipe Based on Fluid Mechanics Coupled with Iscn-Lstm Network

Multiphase flowrate measurement with time series sensing data and sequential model

Accurate multiphase flowrate measurement is challenging but vital in the energy industry to monitor the production process. Machine learning has recently emerged as a promising method for estimating multiphase flowrates based on different conventional flow meters. In this paper, we propose a Convolutional Neural Network (CNN)-Long-Short Term Memory (LSTM) model and a Temporal Convolutional Network (TCN) model to estimate the volumetric liquid flowrate of oil/gas/water three-phase flow based on the Venturi tube. The volumetric flowrates of the liquid and gas phase vary from 0.1–10 m3/h and 7.6137–86.7506 m3/h, respectively. We collected time series sensing data from a Venturi tube installed in a pilot-scale multiphase flow facility and utilized single-phase flowmeters to acquire reference data before mixing. Experimental results suggest that the proposed CNN-LSTM and TCN models can effectively deal with the time series sensing data from the Venturi tube and achieve a good accuracy of multiphase flowrate estimation under different flow conditions. TCN achieves a better accuracy for both liquid and phase flowrate estimation than CNN-LSTM. The results indicate the possibility of leveraging conventional flow meters for multiphase flowrate estimation under various flow conditions.

Multi-task learning for virtual flow metering

Virtual flow metering (VFM) is a cost-effective and non-intrusive technology for inferring multiphase flow rates in petroleum assets. Inferences about flow rates are fundamental to decision support systems that operators extensively rely on. Data-driven VFM, where mechanistic models are replaced with machine learning models, has recently gained attention due to its promise of lower maintenance costs. While excellent performances in small sample studies have been reported in the literature, there is still considerable doubt about the robustness of data-driven VFM. In this paper, we propose a new multi-task learning (MTL) architecture for data-driven VFM. Our method differs from previous methods in that it enables learning across oil and gas wells. We study the method by modeling 55 wells from four petroleum assets and compare the results with two single-task baseline models. Our findings show that MTL improves robustness over single-task methods, without sacrificing performance. MTL yields a 25%–50% error reduction on average for the assets where single-task architectures are struggling.

Application of deep learning methods to estimate multiphase flow rate in producing wells using surface measurements

In the past, the problem of predicting the multiphase flow rate in producing wells has been tackled using the Gilbert correlation. The Gilbert correlation offers a quick solution and requires quantities that are readily available at the surface, making it a staple among solutions in many publications. However, the correlation is limited in applicability to local data, and in some cases, multiple parameter settings are required when flow conditions change. To address these points, we introduced a systematic comparison of applying Gilbert correlation and Deep Learning (DL) algorithms to the problem of liquid flow prediction. We determined that some of the issues, specifically the need for multiple models, can be alleviated using DL methods. In this work, available wellhead measurements that can be collected at surface were used in modeling their relationship to well production rate. In addition, features were extracted automatically using autoencoders to generate additional inputs. The algorithms used for forecasting were designed using standard and customized DL architectures. The results of both DL and existing solutions from the literature were compared systematically to determine the usefulness of the DL methodology developed. The study developed a novel methodology for forecasting well liquid and multiphase restricted flow rate using wellhead surface measurements and demonstrated the application to real field data. The method was found to be an efficient utilization of data that are readily available and do not require well intervention to measure.

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (QL) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.