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Collaborating Authors
Xue, Liang
Multiple Production Time Series Forecasting Using Deepar and Probabilistic Forecasting
Han, JiangXia (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China / Department of Oil-Gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum, Beijing, China) | Xue, Liang (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China / Department of Oil-Gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum, Beijing, China)
Abstract The majority of production forecasting methods currently used are point forecasting methods developed in the setting of individual well forecasting. For an actual oilfield, instead of needing to predict individual production time series, one is faced with forecasting thousands of related time series and the uncertainty can be assessed. The objective of this work is to enable global modeling and probabilistic forecasting of a large number of related production time series using Deep Autoregressive Recurrent Neural Networks (DeepAR). The DeepAR model consists of three parts. First, the auxiliary data such as static classification covariates and dynamic covariates are encoded. Second, establish a forward model based on an autoregressive recurrent neural network. Third, the normal distribution is defined as the output distribution function. And the variance and mean are obtained by solving the maximum log-likelihood function using the gradient descent algorithm. We demonstrate how the application of DeepAR to forecasting can overcome many of the challenges(e.g. frequent well shut-in and opening, probabilistic prediction, classification prediction) that are faced by widely-used classical approaches to the problem. In this work, history fitting and prediction were performed on a dataset from more than 2000 tight gas reservoir wells in the Ordos Basin, China. The DeepAR and conventional methods were tested and compared based on the datasets. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 30% compared to RNN-based networks. In the case of frequent well shut-ins and openings, the RNN-based network structure cannot capture the fast pressure response and extreme fluctuations, which eventually leads to high errors. In contrast, DeepAR is more stable to frequent or significant well variations, can learn different dynamic and static category features, generates calibrated probabilistic forecasts with high accuracy, and can learn complex patterns such as seasonality and uncertainty growth over time from the data. This study provides more general production forecasting and analysis of production dynamics methods from a big data perspective. Instead of performing costly well tests or shut-ins, reservoir engineers can extract valuable long-term reservoir performance information from predictions estimated by DeepAR trained on an extensive collection of related production time series data.
- Asia > China > Shanxi Province (0.24)
- Asia > China > Shaanxi Province (0.24)
- Asia > China > Gansu Province (0.24)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > ร sgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (20 more...)
ABSTRACT Based on the theory of computational fluid dynamics (CFD), an integrated numerical model of the positive pulse generator with the upstream and downstream pipeline was established. Compared with the previous numerical simulation, this model can simulate the generation and transmission process of positive pulse signals efficiently and accurately for the first time. Based on the mechanical structure of the common positive pulse generator, the simplified two-dimensional axisymmetric geometric model of the flow region was established considering its flow symmetry characteristics. Combined with dynamic mesh and sliding mesh technology, the reciprocating motion of the valve is simulated, and a custom function controls the motion to generate low-frequency pulse signals. Comparing the new CFD model with the previous numerical model, it is found that with the periodic change of flow area, compression wave, and expansion wave are generated simultaneously in the upstream and downstream of the positive pulse, with the same frequency and opposite phase, which satisfies the transient hydrodynamics theory. Meanwhile, as the pressure wave propagates, the irregular pulse waveform becomes sinusoidal, and its amplitude decreases gradually. This paper presents a new CFD numerical model and simulation scheme, which can provide an efficient and accurate method for designing and simulating a mud pulse telemetry system. INTRODUCTION With the development of the global oil and gas exploration and development objects in the direction of "ultra-deep formation, unconventional formation, old development formation," the scale of horizontal wells has increased significantly, and there are many complex underground conditions (Antunes et al., 2015; Mwachaka et al., 2018). We need to integrate logging and measuring while drilling (LWD and MWD), downhole control, and big data analysis in real-time to solve the problems encountered during drilling and completion. As a wireless data transmission mode, mud pulse telemetry (MPT) has a long transmission distance, good economy, and wide application range (Jia et al., 2018; Chin et al., 2014). Among them, a positive pulse is developed earliest, with stable transmission and strong anti-interference ability.
ABSTRACT Stress sensitivity is a vital factor affecting the seepage mechanism in oil and gas reservoirs. The accurate simulation of reservoir development must comprehensively consider the mechanical properties of fluid and rock. However, the description of boundary conditions in fluidโsolid coupling calculations of oil and gas reservoirs mainly adopts a constant overburden stress and uniaxial deformation assumption or involves artificial boundary conditions of stress and displacement; these methods produce results that do not accurately represent the actual situation of the reservoir. In this study, under the condition that the specified compressive stress is positive, the reservoir stress and strain are completely defined, the reservoir induced stress model is constructed, the deformation-seepage dual boundary is proposed, and the semi-analytical solution method of the model under dual boundary conditions is examined. The study shows that the mechanical law followed by the skeleton stress change in the process of oil and gas reservoir development is equivalent to the continuum stress change under the action of certain body and boundary forces. The seepage boundary acts as a boundary force in the deformation model. The induced stress model can be solved by convolution according to the superposition principle using the help of basic solution of the semi-infinite body under the action of concentrated force. The case study of radial flow in infinite formations shows that the radial and vertical strains are in the same order of magnitude near the wellbore, and there is a large error in the assumption of uniaxial deformation and constant overburden stress. The radial deformation of the reservoir has a triple structure, consisting of the compression, micro-tension, and undeformed zones. Vertically, all the reservoirs are compressive and deformed; however, the vertical skeleton stress has a triple structure consisting of falling, rising, and unchanged zones. The proposed solution method lays the foundation for studying the stress sensitivity of oil and gas reservoirs and the numerical simulation of fluidโsolid coupling.
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.48)
Anisotropic Characteristics of Relative Permeability in Sedimentary Reservoirs
Pei, Xuehao (China University of Petroleum-Beijing) | Liu, Yuetian (China University of Petroleum-Beijing) | Lin, Ziyu (China University of Petroleum-Beijing) | Mao, Yuxin (China University of Petroleum-Beijing) | Xue, Liang (China University of Petroleum-Beijing)
ABSTRACT Sedimentary and diagenetic processes are known to cause anisotropy in the physical properties of rocks. However, most existing studies primarily focus on the anisotropy of the absolute permeability of reservoirs. To investigate the effect of the anisotropic structure on oilโwater two-phase flow, a three-dimensional staggered coring method is developed to prevent or reduce the influence of the end effect in core experiments. Subsequently, the proposed method was used to perform the anisotropy experiment on two-phase relative permeability of natural sandstone, with oil and water, which verifies the existence of relative permeability anisotropy in sandstone reservoirs. Furthermore, we analyzed the tensor representation of anisotropic relative permeability and established a mathematical model of flow considering the anisotropic relative permeability. Numerical simulation calculations of different types of reservoirs were performed considering anisotropic relative permeability. The results indicate that anisotropic relative permeability impacts reservoir development significantly. Particularly, the direction of oilโwater flow gradually exhibits apparent differences with the development process. Moreover, one-way and plane dead oil areas appear successively, resulting in a more complex distribution of remaining oil. In the case of reservoirs with relative permeability anisotropy, the downward flow of the oil phase should be avoided to the highest possible extent. Additionally, the advantages of low residual oil saturation and high oil displacement efficiency in the vertical flow should be exploited to promote the upward flow of crude oil into the wellbore to improve recovery. INTRODUCTION Geological effects, such as sedimentation, diagenesis, and tectonics, are known to influence formations. These processes are often characterized by anisotropy, wherein the physical parameters, such as the permeability of a reservoir, are directional. Various mathematical models and simulation methods have been proposed for performing absolute permeability anisotropy (da Cunha Teixeira et al. 2021; Fanchi 2008; Hassanpour et al. 2010; Leung 1986; Mortada and Nabor 1961; Park et al. 2002; Peaceman 1983; Shiralkar 1990). However, significant problems continue to exist in the description methods associated with multiphase flow in anisotropic reservoirs. Several studies have reported that relative permeability is characterized by anisotropy based on different scales, including the representative elementary volume scale (Corey et al. 1956a, 1956b; Keilegavlen et al. 2011, 2012), laboratory scale (Bakhshian et al. 2020; Eichel et al. 2005; Honarpour and Saad 1994), and reservoir scale (Blonsky et al. 2017; Rustad et al. 2008; Yeh et al. 1985a, 1985b). Although these studies report significant differences between the relative permeability scalar models and the actual situation, relative permeability is nearly always modeled as a scalar. This is because anisotropic relative permeability measurements are difficult to obtain, and the saturation-related anisotropic characteristics can significantly increase the complexity of numerical methods. From the perspective of crystal physics, Dmitriev et al. (1998, 2003) mathematically investigated two-phase percolation in anisotropic porous media. Although they provided an expression for relative permeability that applies to all media types, this complete expression is extremely complex for practical determination and application. Additionally, they used the concept of symmetry in crystal physics and mathematically proved that the direction of the effective permeability principal axis of the fluid phase in media with orthogonal symmetry is always in line with the absolute permeability principal axis. By contrast, the direction of the effective permeability principal axis of the fluid phase varies with saturation in the case of media with monoclinic or triclinic symmetry. Subsequently, microscopic two-phase percolation in anisotropic media of different types of symmetry was investigated by several researchers using group theory methods (Dmitriev et al. 2005, 2007, 2010; Kalam et al. 2020) and numerical methods (Bear et al. 1987; Ezeuko et al. 2008; Lei et al. 2016; Pergament et al. 2012; Ringrose et al. 1996; Sedaghat et al. 2019). All these studies verified that anisotropy exists in relative permeability, similar to absolute permeability. Pei et al. (2022) demonstrated the existence of relative permeability anisotropy in sandstone reservoirs using cubic core experiments; however, the limitation of the length-to-diameter ratio of the cubic core itself influences the result. Wenkuan et al. (2019) experimentally verified that micro-fractures significantly influence the shape of the relative permeability curve, which leads to anisotropic relative permeability of the matrix. The aforementioned studies indicate that the research on two-phase flow in anisotropic reservoirs is primarily based on numerical methods, lacking reliable physical experimental methods and computational models with high practicality. However, the oilโwater relative permeability curve is essential and forms the basis for development planning and dynamic prediction. Moreover, considering only the absolute permeability anisotropy does not reflect the oilโwater flow pattern sufficiently in the reservoir.
- North America > United States (1.00)
- Asia (0.93)
A Deep Learning Framework Using Graph Convolutional Networks for Adaptive Correction of Interwell Connectivity and Gated Recurrent Unit for Performance Prediction
Du, Leding (China University of Petroleum, Beijing) | Liu, Yuetian (China University of Petroleum, Beijing (Corresponding author)) | Xue, Liang (China University of Petroleum, Beijing (Corresponding author)) | You, Guohui (China University of Petroleum, Beijing)
Summary Oilfield development performance prediction is a significant and complex problem in oilfield development. Reasonable prediction of oilfield development performance can guide the adjustment of the development plan. Moreover, the reservoir will change slowly during reservoir development because of flowing water however, previous networks that forecast production dynamics ignored it, which leads to inaccurate predictions. Routine well-wise injection and production measurements contain important subsurface structure and properties. So, for the dynamic prediction of oil/water two-phase waterflooded reservoirs, we built a deep learning framework named adaptive correction interwell connectivity model based on graph convolutional networks (GCN) and gated recurrent unit (GRU). It includes two parts: The first part is the adaptive correction model based on GCN, which uses dynamic production data to automatically correct the initial interwell connectivity computed by permeability, porosity, interwell distance, and so on. The second part is the adaptive learning model based on GRU, which predicts the production performance of oil wells according to the time characteristics of production performance data. This framework considers the influence that changes in reservoir conditions have on production over time to solve the problem of inaccurate production dynamic prediction. It can also predict interwell connectivity. For oilfields with too many wells, using the embedding idea classifies similar wells into one category, saving time for training and avoiding overfitting problems. Applying the model to five different reservoirs to predict interwell connectivity, well oil production rate, and well water cut compare the results with artificial neural networks (ANN), GRU, and long short-term memory (LSTM) models and compare the interwell connectivity with numerical simulation software ,tNavigatorยฎ (Rock Flow Dynamics Llc), too. When the model is applied in Block B of Bohai A reservoir, the mean absolute percentage error of โAdaptive Graph convolutional network and GRUโ (AG-GRU) is 2.1150% while the LSTM is 9.8872%. The error reduces by 78.6%. The injected water has a direction from the water injection well to the production well; this paper only considers the interwell connectivity without considering the direction. Further research is needed to consider the water injection direction and form a weighted directed graph.
- Asia (0.68)
- North America > United States > Texas (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.57)
- North America > United States > Kansas > State Field (0.98)
- Europe > United Kingdom > North Sea > Southern North Sea > Southern Gas Basin > Sole Pit Basin > Block 49/6a > Ann Field > Rotliegend Formation (0.98)
- Europe > United Kingdom > North Sea > Southern North Sea > Southern Gas Basin > Sole Pit Basin > Block 48/10a > Ann Field > Rotliegend Formation (0.98)
The Effect of Fracture Aperture on Fracturing Fluid Imbibition into Gas-Saturated Rocks
Li, Guanlin (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing)) | Liu, Yuetian (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing)) | Cheng, Ziyan (Exploration and Development Research Institute, Shengli Oilfield Company) | Xue, Liang (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing)) | Kong, Xiangming (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing))
ABSTRACT: During well shut in after hydraulic fracturing, a large amount of fracturing fluid enters the formation through imbibition, which significantly reduces the recovery of injected water. However, the capillary pressure of fractures is usually ignored, and the effect of fracture aperture on this process is not clear. We examined the imbibition characteristics between fractures and matrix in the capillary-controlled gas-water flow. The focus is on the flow behavior and effects caused by capillary pressure of fractures. The imbibition experiments were carried out under different fracture apertures (50-950 ฮผm). The results show that fractures within a certain opening range accelerate water imbibition by providing a plane source. The water flows rapidly in the fractures, then being absorbed by the matrix on both sides. The water imbibition rate of fractures is mainly affected by the fractures aperture, which controls the fracture capillary pressure and permeability. The overall trend is that the imbibition rate is inversely proportional to the fracture aperture. This indicates that the small aperture fractures without proppants can quickly transport water to the interior of the matrix. The research is significant to understand the law of fracturing fluid imbibition in the formation. 1. INTRODUCTION Horizontal wells with multistage hydraulic fracturing are an important technical means for developing unconventional gas reservoirs (Fazelipour, 2011; Yu and Sepehrnoori, 2013; Lei et al., 2022). A large volume of water-based fracturing fluid such as slick water is injected into the formation to form a complex fracture network (Barati and Liang, 2014; Cai et al., 2017). Field practice shows that the flowback rate of fracturing fluid is generally low (Alkouh and Wattenbarger, 2013; Osselin et al., 2018). Spontaneous water imbibition is considered to be one of the main reasons for this phenomenon (Almulhim et al., 2014; Ge et al., 2015; Al-Ameri et al., 2018). The fracturing fluid spontaneously imbibes into the matrix from fractures, which could increase water saturation and reduce the gas phase relative permeability, affecting the production of gas reservoirs (Saini et al., 2021). The study of spontaneous imbibition mostly focuses on the capillary pressure of matrix pores and related problems (Makhanov et al., 2012; Dehghanpour et al., 2013; Roychaudhuri et al., 2013; Sadjadi and Rieger, 2013; Mason and Morrow, 2013; Shi et al., 2018). But spontaneous imbibition in unsaturated water fractures also deserves attention. There are different scales of fractures in the complex fracture network formed by artificial and natural fractures (Yushi et al., 2016). Studying the effect of fracture aperture on fracturing fluid imbibition into gas-saturated rocks has significance for improving fracturing technology and enhancing recovery.
- North America > United States (1.00)
- Asia > China (0.70)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.48)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.31)
- Well Completion > Hydraulic Fracturing > Fracturing materials (fluids, proppant) (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- (3 more...)
Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network
Du, Enda (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China) | Liu, Yuetian (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China (Corresponding author)) | Cheng, Ziyan (Exploration and Development Research Institute, Sinopec Shengli Oilfield, China) | Xue, Liang (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China (Corresponding author)) | Ma, Jing (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China) | He, Xuan (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China)
Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.
- North America > United States (0.93)
- Asia > China (0.68)
- Geology > Rock Type (0.47)
- Geology > Structural Geology > Tectonics (0.46)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Production forecasting (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network
Du, Enda (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing) | Liu, Yuetian (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing (Corresponding author)) | Cheng, Ziyan (Exploration and Development Research Institute, Sinopec Shengli Oilfield) | Xue, Liang (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing) | Ma, Jing (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing) | He, Xuan (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing)
Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.
- North America > United States (0.93)
- Asia > China (0.68)
- Geology > Rock Type (0.47)
- Geology > Structural Geology > Tectonics (0.46)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Production forecasting (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Flowback Analysis of Complex Fracture Networks in the Unconventional Reservoir Using Finite Element Method with Coupled Flow and Geomechanics
Li, Jun (China University of Petroleum-Beijing) | Liu, Yuetian (China University of Petroleum-Beijing) | Xue, Liang (China University of Petroleum-Beijing) | Cheng, Ziyan (China Petroleum & Chemical Corp SINOPEC) | Kong, Xiangming (China University of Petroleum-Beijing) | Li, Songqi (China University of Petroleum-Beijing)
Abstract After fracture treatment in unconventional reservoirs, the in-situ stress and fluid pressure are greatly changed in the reservoir because of the generation of fracture networks. In order to get high production, efforts are made to get close fracture spacing and long fracture length in-situ field, which in turn make fracture distribution become complicated as the range of fractures size and density is widespread. In this work, the finite element method is used to analysis flowback around hydraulic fracture among complex fractures networks, which consider the coupled effects of flow and geomechanics. The reservoir is assumed to be a 3-D poroelastic medium. According to the fracture sizes, the fracture is divided into three types. These small natural fractures are treated as SRV regions, hydraulic fractures, natural fracture in middle and large sizes are explicitly represented using LGR. Finite element method simulates fracture deformation and the two-phase fluid flow in the reservoir during flowback stage. The physical properties are altered by the coupled flow and geomechanics in the reservoir. The fluid pressure, stress and flowback production over time around these fractures are recorded. The results show that during the flowback period, the production experience a sharp decrease. The porosity and permeability in the reservoir are greatly reduced because of the coupled effects. These explicit natural fractures influence the hydraulic fractures. As the hydraulic fracture spacing reduced, the stress shadow effects become more serious and the flowback production decreases. This work helps understand the flowback analysis with coupled geomechanics and flow effects in the complex fracture networks in the unconventional reservoirs and physical properties effects in different reservoir conditions.
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Maverick Basin > Eagle Ford Shale Formation (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tuha Field (0.98)
Simulation and Testing of the Hydraulic Performance of the Sliding Vane Pump
Zhao, Yanlong (China University of Petroleum, Beijing) | Wang, Zhiming (China University of Petroleum, Beijing) | Xue, Liang (China University of Petroleum, Beijing) | Zhang, Lixin (PetroChina Research Institute of Petroleum Exploration and Development) | Hao, Zhongxian (PetroChina Research Institute of Petroleum Exploration and Development)
Summary A new all-metal sliding-vane pump (SVP) and matching lift system were developed as an alternative to the low efficiency and poor high-temperature performance of conventional artificial-lift systems. Numerical simulation and laboratory tests were used to conduct a comparative study of the hydraulic performance of the pump. The effects of pump-lifting-pressure difference and rotational speed on pump rate and efficiency were studied. The test results showed that, if the rotation speed is fixed, the pump rate and efficiency will decrease with an increase in required pressure difference. With a constant pressure difference, the flow rate can be controlled by varying the pump speed. On the basis of computational fluid dynamics (CFD) and finite-element-method (FEM) numerical simulation, a numerical model of the SVP was created. The pressure and fluid-flow distribution in the vane pump were determined under zero pump-pressure-difference condition, which helped realize the working principles of the pump. The simulation results agreed well with the test results, thus validating the reliability of the numerical models in this paper.
- Asia > China (0.96)
- North America > United States > Texas (0.28)