Development of an accurate method to prognosticate choke flow coefficients for natural gas flow through nozzle and orifice type chokes

One of the main components in oil and gas production system is choke valve. The choke valve role is maintaining sufficient back pressure to prevent water gas coning and formation damage and also stabilizing fluid flow to reach the optimum production scenario. Chokes can be employed either on surface or subsurface to control the fluid flow characteristics to the downstream processing facilities such as flow rate, pressure, and velocity. Malfunction of choke may results in severe damages in safety, facilities, and environment.In this study, a rigorous method based on artificial intelligence is developed to predict the choke flow coefficient for subsonic natural gas flow through nozzle and orifice type chokes. Reynolds number and ratio of choke diameter to pipe diameter was utilized as input parameters. The method used in this study is radial basis function neural network coupled with genetic algorithm. The results showed great agreement with experimental data. In addition, the proposed method was compared with classic correlations. This comparison demonstrated the robustness and superiority of the GA-RBF model.     

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 (Q_L) 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.     

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.     

Experimental and numerical investigation on the performance of small-sized cavitating venturis

Cavitating venturis (CVs) are simple devices which can be used in different industrial applications to passively control the flow rate of fluids. In this research the operation of small-sized CVs is characterized and their capabilities in regulating the mass flow rate were experimentally and numerically investigated. The effect of upstream and downstream pressures, as well as geometrical parameters such as the throat diameter, throat length, and diffuser angle on the mass flow rate and critical pressure ratio were studied. For experimental data acquisition, three CVs with throat diameters of 0.7, 1 and 1.5 mm were manufactured and tested. The fabricated CVs were tested at different upstream and downstream pressures in order to measure their output mass flow rate and to obtain their characteristic curves. The flow inside the CVs was also simulated by computational fluid dynamics. The numerical results showed agreement with the experimental data by a maximum deviation of 5–10% and confirmed that the numerical approach can be used to predict the critical pressure ratio and mass flow rate at cavitaing condition. It is found that despite the small size of venturis, they are capable of controlling the mass flow rate and exhibit the normal characteristics. By decreasing the throat diameter, their cavitating mode became more limited. Results also show that increasing the diffuser angle and throat length leads to a decrease in critical pressure ratio.     

Flow measurement and instrumentation flow control characteristics of throttling venturi valve with adjustable area

In this study, a throttling venturi valve with adjustable area was designed to control the thrust of a monopropellant thruster using hydrogen peroxide. The flow rate control characteristics of the throttling venturi valve were investigated based on the pintle stroke and upstream pressure of the venturi. Three kinds of experiments were conducted: pressure and flow rate measurement according to pintle stroke and venturi upstream pressure, determination of critical pressure ratios under various conditions, flow rate control performance through open-loop control and feedback control of an actuator. The pressures were measured at the upstream, throat, and downstream of the venturi. It was observed that the flow rate changed in proportion to the stroke and upstream pressure. Below a stroke of 10 mm, the critical pressure ratio gradually decreased as the stroke and upstream decreased. However, above a stroke of 10 mm, the critical pressure ratio converged to a value between 0.7 and 0.8 regardless of the upstream pressure. The results of automatic flow rate control tests using open-loop control and feedback control showed that the measured flow rate satisfactorily followed the target flow rate profile.     

Predicting oil flow rate through orifice plate with robust machine learning algorithms

Measuring fluid flow rate passing through pipelines is a basic strategy for developing the infrastructure of fluid-dependent industries. It is a challenging issue for trade, transportation, and reservoir management purposes. Predicting the flow rate of fluid is also regarded as one of the crucial steps for the development of oil fields. In this study, a novel deep machine learning model, convolutional neural network (CNN), was developed to predict oil flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (T_f), upstream pressure (Pu), root differential pressure (√ΔP), percentage of base sediment and water (BS&W%), oil specific gravity (SG), kinematic viscosity (ν), and beta ratio (β, the ratio of pipe diameter to orifice diameter). Due to the absence of accurate and credible methods for determining Qo, deep learning can be a useful alternative to traditional machine learning methods. Justifying the promising performance of the developed CNN model over conventional machine learning models, three different machine learning algorithms, including radial basis function (RBF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were also developed and their prediction performance was compared with that of the CNN model. A sensitivity analysis was also performed on the influence degree of each input variable on the output variable (Qo). The study outcomes indicate that the CNN model provided the highest Qo prediction accuracy among all the four models developed by presenting a root mean squared error (RMSE) of 0.0341 m3/s and a coefficient of determination (R²) of 0.9999, when applied to the dataset of 3303 data records compiled from oil fields around Iran. The Spearman correlation coefficient analysis results display that √ΔP, Pu, and Tf were the most influential variables on the oil flow rate in respect of the large dataset evaluated.     

Prediction of oil rates using Machine Learning for high gas oil ratio and water cut reservoirs

Allocated well oil rates are essential well performance evaluation. Flow meters are not reliable at high gas-oil ratio (GOR) and high water-cut (WC). Most of the available formulas are based on Gilbert-type formulas with neglecting the differential pressure across the choke. Adaptive network-based fuzzy inference system (ANFIS), and functional networks (FN) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (550 wells) was obtained from oil fields in the Middle East. GOR varied from 1,000 to 9,351 scf/stb, WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. The developed AI models were compared against the previous published formulas. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the subcritical flow for ANFIS and FN were 1.25, and 0.95%, respectively. While in the critical flow, the AAPE values were 1.1, and 1.35% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas by 34%. The findings from this study will significantly assist production engineers to predict the oil rate in real-time without adding any cost or field intervention.     

Application of small size cavitating venturi as flow controller and flow meter

The cavitating venturi is using to provide constant mass flow rate of liquid which is passing through a passage, independent of downstream pressure changes. The flow rate is a function of the upstream pressure, the throat area, the density and saturation pressure of the liquid. An experimental setup with capability of supplying water flow rate and constant upstream pressure was designed and manufactured. Three cavitating venturis with throat diameter of 5, 2.5, and 1 mm were designed and built to investigate the effect of venturi size on its mass flow rate. Three different sets of experiments were conducted to investigate the performance of the venturis. In the experiments, the mass flow rates were examined under different downstream and upstream pressure conditions and time varying downstream pressure. The results show for the ratio of downstream pressure to upstream pressure less than 0.8, the mass flow rate is constant and independent of the downstream pressure. Whenever the pressure ratio exceeds 0.8, the venturi acts like an orifice. This pressure ratio has been predicted analytically to highlight the affecting parameters, mainly the geometry of the venturi and viscous losses. It is found that the venturi size has no effect on its expecting function to keep mass flow rate constant. Also, it is shown that by applying a discharge coefficient and using only upstream pressure, the cavitating venturi can be used as a flowmeter with a high degree of accuracy in a wide range of mass flow rate.     

Anti-slug control based on a virtual flow measurement

Feedback control is an effective and economic solution to prevent slugging flow regimes in offshore oil production. For this, the opening value of a choke valve at the topside platform is usually used as the control input to regulate the pressure or the flow rate in the pipeline. Designing such a control system based on topside measurements, without subsea sensing devices, is preferred from a practical point of view. Controlling the topside pressure alone is difficult and it is not robust in practice, but combining the topside pressure and the flow rate results in a robust control solution. However, measuring the flow rate of a multiphase stream is challenging and requires expensive instrumentation. In this paper, we propose an anti-slug control solution based on a virtual flow measurement. This virtual flow is estimated without neither density nor phase fraction involved, but it gives satisfactory results for the stabilizing control. In particular, applying a cascade structure results in a robust and recommended solution. The performance of the proposed controller is demonstrated by simulation using the realistic OLGA simulator and by experiments.     

Prediction of impact erosion in valve geometries

In this paper, the capability of computational fluid dynamics techniques is investigated to predict the rate of solid particle erosion in industrially relevant geometries. An Eulerian–Lagrangian model of the flow is used, in combination with empirically developed equations for the mass removal, to examine erosion in valve components for aqueous slurry flows. Two types of geometries were used: (i) a relative simple geometry with basic geometrical features similar to real valves and (ii) a geometrically complex valve (a choke valve). Predictions of flow coefficients and mass removal rates were directly compared with measurements from a parallel experimental programme. While flow characteristics and erosion locations were identified satisfactorily, erosion rates were seriously underestimated.     

伯努利效应引起滑阀阀芯径向力的研究

运用CFD软件Fluent对液压滑阀内部流场进行可视化分析,详细研究了阀芯受径向压力分布情况和影响因素。计算发现,径向压力分布与阀口开度、入口流量、环割槽深径比、进出口油道的轴交角都有密切的关系。阀口开度越大,径向压力波动越小;入口流量越大,环割槽深径比越小,径向压力波动越大;与进出口轴交角为0°和90°相比,进出口轴交角为180°时x=0截面的径向压力分布更平稳。同时,通过伯努利效应对入口中心截面处阀芯周向压力分布及阀芯轴向分段建立压力方程,通过理论分析验证了仿真模型和结果的可靠性。最后分析了径向力不平衡产生的卡紧力及径向稳态液动力的分布及其影响因素。     

基于最小二乘支持向量机的调节阀建模方法

针对调节阀物理模型存在严重的非线性、时变性及参数不确定性的问题,提出了一种基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的调节阀流量及压力预测模型。根据调节阀的物理模型,分析了能够表征调节阀运行状态的相关参数,主要有阀前压力、阀后压力、开度、流量、负载压力、温度等;设计了调节阀数据采集实验系统,通过全排列组合试验分别确定了流量和压力预测时LS-SVM模型的最佳输入特征向量。实验结果表明,模型能够以较理想的精度预测流量及压力的输出,可为调节阀自动控制或故障诊断系统的设计提供指导。     

基于CFD的压机充液阀流道设计

为提升压机充液阀工作时的通油能力及稳定性,设计全流道的新型导流结构,基于三维计算流体动力学（CFD）方法,分析油液流经充液阀的速度及压力分布规律.结果显示,相同进出口压差下该新结构充液阀比传统形式具有更高的液体更新效率,且作用在充液阀上的油液压力更小,因此更加适用于高压大流量的高端压机.     

Prediction of gas-liquid two-phase choke flow using Gaussian process regression

In the process of shale gas production, it is of great significance to select an appropriate mathematical method to accurately predict the gas flow rate of wellhead choke for the rational formulation of shale gas well production plan. Gas-liquid two-phase flow occurs in most of the time from the flowback to the production period in shale gas wells. Wellhead chokes play key roles in regulating the flowing rates of both the flowback fluid and shale gas. Therefore, it is important to study the law of two-phase choke flow clearly so as to accurately predict gas flow rate through wellhead chokes. Up to now, previous studies have proposed a variety of applicable empirical methods, including Gilbert-type correlation (GC), artificial neural network (ANN) and support vector machine (SVM). The analysis of training data and the establishment of accurate prediction models determine the accuracy of prediction. In this study, Gaussian process regression (GPR) was adopted to learn and predict the behavior of gas-liquid two-phase flow through wellhead chokes, and huge amounts of data collected from Chuannan Shale gas wells were used to verify the effectiveness of the GPR method. The prediction accuracy of the GPR method was compared with those of other methods like GC, ANN and SVM. In addition, we also compared the prediction accuracy of different kernel functions to select the best kernel function for GPR. The kernel functions considered are exponential function, squared exponential function, rational quadratic function and Matérn function. The results showed that GPR method is accurate and applicable for analyzing the behavior of gas-liquid two-phase flow through wellhead chokes, and GPR method with exponential kernel function could achieve greater prediction accuracy than other kernel functions.     

轴向柱塞泵低气压运行特性建模与试验分析

空化是影响液压系统动态特性的重要因素,为此开展了轴向柱塞泵低压环境下的工况研究。考虑气液两相混合油液的密度、体积弹性模量和黏度的影响,限制入口油腔的最低压力,建立轴向柱塞泵的压力流量模型,计算获得轴向柱塞泵在不同工况下的流量特性,并通过试验验证。研究表明:负载增大导致更严重的空化以及泄漏,并使容积效率降低;轴向柱塞泵在达到临界流量之后,转速提升只会加剧空化,而不能提升流量;最大容积效率出现在临界流量产生之前。为轴向柱塞泵低气压性能预测提供了理论支撑。     

柱面螺旋槽气液两相流体动压密封稳态性能研究

为揭示航空发动机轴承腔内润滑油与加压气流形成复杂两相润滑状态下的柱面流体动压密封性能,基于两相流Mixture模型,研究气液两相介质柱面螺旋槽流体动压密封稳态性能,分析操作参数和结构参数对动压密封性能的影响.结果表明:在同样工况参数下,气液两相下柱面流体动压密封具有较好的动压效应;转速、压差以及液气比的增大均有利于提高...     

液压油在线除气装置设计与试验

液压油中气体的存在,会影响液压系统的性能。该文基于漩涡流的离心原理设计了一种液压油在线除气装置,并开展了可视化试验工作。试验结果表明在一定流动参数条件下除气装置中气液两相流可以达到稳定状态,聚集的气体与排出的气体相平衡,能有效的去除油液中的气体。     

A study on the relationship between upstream and downstream conditions in swirling two-phase flow

Inline fluid separation is a concept, which is used in the oil and gas industry. Inline fluid separators typically have a static design and hence changing inlet conditions lead to less efficient phase separation. For introducing flow control into such a device, additional information is needed about the relationship of upstream and downstream conditions. This paper introduces a study on this relationship for gas/liquid two-phase flow. The downstream gas core development was analyzed for horizontal device installation in dependence of the inlet gas and liquid flow rates. A wire-mesh sensor was used for determining two-phase flow parameters upstream and a high-speed video camera to obtain core parameters downstream the swirling device. For higher accuracy of the calculated void fraction, a novel method for wire-mesh sensor data analysis has been implemented. Experimental results have shown that void fraction data of the wire-mesh sensor can be used to predict the downstream behavior for a majority of the investigated cases. Additionally, the upstream flow pattern has an impact on the stability of the gas core downstream which was determined by means of experimental data analysis.     

乳化液泵配流机理的理论研究与试验分析

利用流体可压缩性、流量连续方程及阀口流量公式建立了单柱塞腔流动特性方程；在考虑了重力、弹簧力、压差力、接触力及液动力基础上，建立了配流阀动力学模型；以排液歧管过流孔道为控制体积，建立了包含3个柱塞腔、蓄能器及负载的整泵流动特性方程。用AMESim软件创建了具有3柱塞结构的BRW125/31.5C型乳化液泵模型，将不同曲轴转速下的配流阀阀芯位移及泵出口压力的仿真值与试验值对比，验证了理论分析与仿真模型的正确性。结果表明：当该型号泵驱动电机输入频率分别为50， 40， 30 Hz时，上流量脉动率分别为1.25%，1.19%，1.37%，而下流量脉动率分别为1.76%，1.73%，176%；柱塞腔内流场在从低压向高压转换时存在压力冲击；在吸、排液行程转换阶段，存在流量倒灌现象。     

Application of variable area cavitating venturi as a dynamic flow controller

The variable area cavitating venturi is an effective means to throttle the mass flow rate of liquid. The mass flow rate is a function of the upstream pressure, the pintle stroke, the density and saturation pressure of the liquid, independent of the downstream pressure. In this paper, a variable area cavitating venturi is designed and four different sets of experiments are conducted to investigate the performance of the variable area cavitating venturi. In these experiments, the mass flow rates are examined under different pintle positions, upstream pressures, downstream pressures and dynamic motions of the pintle. The experimental results indicate that the mass flow rate is independent of the downstream pressure when the ratio of the downstream pressure to upstream pressure is less than 0.8. The mass flow rate is almost linearly dependent on the pintle stroke for a constant upstream pressure. The discharge coefficient is a function of the pintle stroke, whereas the upstream and downstream pressures have rare influence on the discharge coefficient. The variable area cavitating venturi can control and measure the mass flow rate dynamically by determining the pintle stroke and the upstream pressure.