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石油科学（英文版）
石油科学（英文版）

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石油科学（英文版）/Journal Petroleum ScienceCSCD北大核心SCI
查看更多>>本刊办刊宗旨在于向国外介绍中国石油界最新的学术、科研成果，广泛开展国际间的学术交流，促进中国石油科学技术的发展。主要刊登反映中国石油石油科学技术领域最新、最高水平科研成果的科技论文。其专业内容包括石油勘探与开发、石油储运工程、石油炼制与化工、石油机电工程、油田化工、石油工业经济管理与营销以及与石油工业有关的各个学科。
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    Synthetic polymers:A review of applications in drilling fluids

    Shadfar DavoodiMohammed Al-ShargabiDavid A.WoodValeriy S.Rukavishnikov...
    475-518页
    查看更多>>摘要：With the growth of deep drilling and the complexity of the well profile,the requirements for a more complete and efficient exploitation of productive formations increase,which increases the risk of various complications.Currently,reagents based on modified natural polymers(which are naturally occurring compounds)and synthetic polymers(SPs)which are polymeric compounds created industrially,are widely used to prevent emerging complications in the drilling process.However,compared to modified natural polymers,SPs form a family of high-molecular-weight compounds that are fully synthesized by undergoing chemical polymerization reactions.SPs provide substantial flexibility in their design.Moreover,their size and chemical composition can be adjusted to provide properties for nearly all the functional objectives of drilling fluids.They can be classified based on chemical ingredients,type of reaction,and their responses to heating.However,some of SPs,due to their structural characteristics,have a high cost,a poor temperature and salt resistance in drilling fluids,and degradation begins when the temperature reaches 130 ℃.These drawbacks prevent SP use in some medium and deep wells.Thus,this review addresses the historical development,the characteristics,manufacturing methods,classifi-cation,and the applications of SPs in drilling fluids.The contributions of SPs as additives to drilling fluids to enhance rheology,filtrate generation,carrying of cuttings,fluid lubricity,and clay/shale stability are explained in detail.The mechanisms,impacts,and advances achieved when SPs are added to drilling fluids are also described.The typical challenges encountered by SPs when deployed in drilling fluids and their advantages and drawbacks are also discussed.Economic issues also impact the applications of SPs in drilling fluids.Consequently,the cost of the most relevant SPs,and the monomers used in their synthesis,are assessed.Environmental impacts of SPs when deployed in drilling fluids,and their manufacturing processes are identified,together with advances in SP-treatment methods aimed at reducing those impacts.Recommendations for required future research addressing SP property and performance gaps are provided.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    CO2 flooding in shale oil reservoir with radial borehole fracturing for CO2 storage and enhanced oil recovery

    Jia-Cheng DaiTian-Yu WangJin-Tao WengKang-Jian Tian...
    519-534页
    查看更多>>摘要：This study introduces a novel method integrating CO2 flooding with radial borehole fracturing for enhanced oil recovery and CO2 underground storage,a solution to the limited vertical stimulation reservoir volume in horizontal well fracturing.A numerical model is established to investigate the production rate,reservoir pressure field,and CO2 saturation distribution corresponding to changing time of CO2 flooding with radial borehole fracturing.A sensitivity analysis on the influence of CO2 injection location,layer spacing,pressure difference,borehole number,and hydraulic fractures on oil production and CO2 storage is conducted.The CO2 flooding process is divided into four stages.Reductions in layer spacing will significantly improve oil production rate and gas storage capacity.However,serious gas channeling can occur when the spacing is lower than 20 m.Increasing the pressure difference between the producer and injector,the borehole number,the hydraulic fracture height,and the fracture width can also increase the oil production rate and gas storage rate.Sensitivity analysis shows that layer spacing and fracture height greatly influence gas storage and oil production.Research outcomes are expected to provide a theoretical basis for the efficient development of shale oil reservoirs in the vertical direction.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

    Song DengHao-Yu PanHai-Ge WangShou-Kun Xu...
    535-550页
    查看更多>>摘要：Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical param-eters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization al-gorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R2)of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Chemical modification of barite for improving the performance of weighting materials for water-based drilling fluids

    Li-Li YangZe-Yu LiuShi-bo WangXian-Bo He...
    551-566页
    查看更多>>摘要：With increasing drilling depth and large dosage of weighting materials,drilling fluids with high solid content are characterized by poor stability,high viscosity,large water loss,and thick mud cake,easier leading to reservoir damage and wellbore instability.In this paper,micronized barite(MB)was modified(mMB)by grafting with hydrophilic polymer onto the surface through the free radical polymerization to displace conventional API barite partly.The suspension stability of water-based drilling fluids(WBDFs)weighted with API barite∶mMB=2∶1 in 600 g was significantly enhanced compared with that with API barite/WBDFs,exhibiting the static sag factor within 0.54 and the whole stability index of 2.The viscosity and yield point reached the minimum,with a reduction of more than 40％compared with API barite only at the same density.Through multi-stage filling and dense accumulation of weighting materials and clays,filtration loss was decreased,mud cake quality was improved,and simultaneously it had great reservoir protection performance,and the permeability recovery rate reached 87％.In addition,it also effectively improved the lubricity of WBDFs.The sticking coefficient of mud cake was reduced by 53.4％,and the friction coefficient was 0.2603.Therefore,mMB can serve as a versatile additive to control the density,rheology,filtration,and stability of WBDFs weighted with API barite,thus regulating compre-hensive performance and achieving reservoir protection capacity.This work opened up a new path for the productive drilling of extremely deep and intricate wells by providing an efficient method for managing the performance of high-density WBDFs.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Fracture sealing performance of granular lost circulation materials at elevated temperature:A theoretical and coupled CFD-DEM simulation study

    Chong LinQi-Cong XuLie-Xiang HanGao Li...
    567-581页
    查看更多>>摘要：Lost circulation is a common downhole problem of drilling in geothermal and high-temperature,high-pressure(HTHP)formations.Lost circulation material(LCM)is a regular preventive and remedial mea-sure for lost circulation.However,conventional LCMs seem ineffective in high-temperature formations.This may be due to the changes in the mechanical properties of LCMs and their sealing performance under high-temperature conditions.To understand how high temperature affects the fracture sealing performance of LCMs,we developed a coupled computational fluid dynamics-discrete element method(CFD-DEM)model to simulate the behavior of granular LCMs in fractures.We summarized the literature on the effects of high temperature on the mechanical properties of LCMs and the rheological properties of drilling fluid.We conducted sensitivity analyses to investigate how changing LCM slurry properties affected the fracture sealing efficiency at increasing temperatures.The results show that high temper-ature reduces the size,strength,and friction coefficient of LCMs as well as the drilling fluid viscosity.Smaller,softer,and less frictional LCM particles have lower bridging probability and slower bridging initiation.Smaller particles tend to form dual-particle bridges rather than single-particle bridges.These result in a deeper,tighter,but unstable sealing zone.Reduced drilling fluid viscosity leads to faster and shallower sealing zones.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Interpretation and characterization of rate of penetration intelligent prediction model

    Zhi-Jun PeiXian-Zhi SongHai-Tao WangYi-Qi Shi...
    582-596页
    查看更多>>摘要：Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms tradi-tional ROP equations and machine learning algorithms,its lack of interpretability undermines its cred-ibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

    Xing-Yuan MiaoHong Zhao
    597-608页
    查看更多>>摘要：Pipeline isolation plugging robot(PIPR)is an important tool in pipeline maintenance operation.During the plugging process,the violent vibration will occur by the flow field,which can cause serious damage to the pipeline and PIPR.In this paper,we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity.Firstly,the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging.And the pressure difference is proposed to evaluate the degree of flow field vibration.Secondly,the math-ematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine(ELM)optimized by improved sparrow search algorithm(ISSA).Finally,a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time.The results show that the proposed method can reduce the plugging-induced vibration by 19.9％and 32.7％on average,compared with single-regulating methods.This study can effectively ensure the stability of the plugging process.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Study on the clogging mechanism of punching screen in sand control by the punching structure parameters

    Fu-Cheng DengFu-Lin GuiBai-Tao FanLei Wen...
    609-620页
    查看更多>>摘要：As an independent sand control unit or a common protective shell of a high-quality screen,the punching screen is the outermost sand retaining unit of the sand control pipe which is used in geothermal well or oil and gas well.However,most screens only consider the influence of the internal sand retaining me-dium parameters in the sand control performance design while ignoring the influence of the plugging of the punching screen on the overall sand retaining performance of the screen.To explore the clogging mechanism of the punching screen,this paper established the clogging mechanism calculation model of a single punching screen sand control unit by using the computational fluid mechanics-discrete element method(CFD-DEM)combined method.According to the combined motion of particles and fluids,the influence of the internal flow state on particle motion and accumulation was analyzed.The results showed that(1)the clogging process of the punching sand control unit is divided into three stages:initial clogging,aggravation of clogging and stability of clogging.In the initial stage of blockage,coarse particles form a loose bridge structure,and blockage often occurs preferentially at the streamline gathering place below chamfering inside the sand control unit.In the stage of blockage intensification,the particle mass develops into a relatively complete sand bridge,which develops from both ends of the opening to the center of the opening.In the stable plugging stage,the sand deposits show a"fan shape"and form a"V-shaped"gully inside the punching slot element.(2)Under a certain reservoir particle-size distribution,The slit length and opening height have a large influence on the permeability and blockage rate,while the slit width size has little influence on the permeability and blockage rate.The microscopic clogging mechanism and its law of the punching screen prevention unit are proposed in this study,which has some field guidance significance for the design of punching screen and sand prevention selection.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

    Xue-Qiang ZhangQing-Lin ChengWei SunYi Zhao...
    621-640页
    查看更多>>摘要：As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gath-ering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

    Yun-Peng HeHai-Bo ChengPeng ZengChuan-Zhi Zang...
    641-653页
    查看更多>>摘要：High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort Addi-tionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is con-structed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experi-ments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普