Search Results (37)

The polished rod dynamometer operates under alternating loads and large temperature differences for a long time, inevitably leading to zero drift and temperature drift issues. At the same time, conventional inversion of polished rod dynamometer cards fails to consider the impact of friction loads, resulting in inaccurate production and liquid level calculations from pump dynamometer cards. Based on the oil-filled environment in the sucker rod and tubing during the upstroke of the pumping unit, this paper proposes a rapid identification method for the four characteristic points of the polished rod dynamometer card to obtain a calculation method for friction loads at the velocity reversal points A and C. The gravity of the polished rod string in the liquid column serves as the benchmark for calibrating the polished rod dynamometer card. Combined with basic well data, a one-dimensional wave equation difference calculation method is used to solve for the pump dynamometer card. An approximation algorithm is employed to achieve rapid calibration of the polished rod dynamometer card and inversion of the pump dynamometer card. Calculation and engineering application results indicate that the accuracy of production and liquid level calculations obtained from the pump dynamometer card through online correction of the polished rod dynamometer card exceeds 90%, achieving the goal of engineering digitization applications. Full article

The complex and variable operating conditions of sucker-rod pumping wells pose a significant challenge for the timely and accurate identification of oil well operating conditions. Effective deep learning based on measured multi-source data obtained from the sucker-rod pumping well production site offers a promising solution to the challenge. However, existing deep learning-based operating condition recognition methods are constrained by several factors: the limitations of traditional operating condition recognition methods based on single-source and multi-source data, the need for large amounts of labeled data for training, and the high robustness requirement for recognizing complex and variable data. Therefore, we propose a semi-supervised class-incremental sucker-rod pumping well operating condition recognition method based on measured multi-source data distillation. Firstly, we select measured ground dynamometer cards and measured electrical power cards as information sources, and construct the graph neural network teacher models for data sources, and dynamically fuse the prediction probability of each teacher model through the Squeeze-and-Excitation attention mechanism. Then, we introduce a multi-source data distillation loss. It uses Kullback-Leibler (KL) divergence to measure the difference between the output logic of the teacher and student models. This helps reduce the forgetting of old operating condition category knowledge during class-incremental learning. Finally, we employ a multi-source semi-supervised graph classification method based on enhanced label propagation, which improves the label propagation method through a logistic regression classifier. This method can deeply explore the potential relationship between labeled and unlabeled samples, so as to further enhance the classification performance. Extensive experimental results show that the proposed method achieves superior recognition performance and enhanced engineering practicality in real-world class-incremental oil extraction production scenarios with complex and variable operating conditions. Full article

In oilfield extraction activities, traditional downhole condition monitoring is typically conducted using dynamometer cards to capture the dynamic changes in the load and displacement of the sucker rod. However, this method has severe limitations in terms of real-time performance and maintenance costs, making it difficult to meet the demands of modern extraction. To overcome these shortcomings, this paper proposes a novel fault detection method based on the analysis of motor power parameters. Through the dynamic mathematical modeling of the pumping unit system, we transform the indicator diagram of beam-pumping units into electric power diagrams and conduct an in-depth analysis of the characteristics of electric power diagrams under five typical operating conditions, revealing the impact of different working conditions on electric power. Compared to traditional methods, we introduce fourteen new features of the electrical parameters, encompassing multidimensional analyses in the time domain, frequency domain, and time-frequency domain, significantly enhancing the richness and accuracy of feature extraction. Additionally, we propose a new effectiveness evaluation method for the FCM clustering algorithm, integrating fuzzy membership degrees and the geometric structure of the dataset, overcoming the limitations of traditional clustering algorithms in terms of accuracy and the determination of the number of clusters. Through simulations and experiments on 10 UCI datasets, the proposed effectiveness function accurately evaluates the clustering results and determines the optimal number of clusters, significantly improving the performance of the clustering algorithm. Experimental results show that the fault diagnosis accuracy of our method reaches 98.4%, significantly outperforming traditional SVM and ELM methods. This high-precision diagnostic result validates the effectiveness of the method, enabling the efficient real-time monitoring of the working status of beam-pumping unit wells. In summary, the proposed method has significant advantages in real-time performance, diagnostic accuracy, and cost-effectiveness, solving the bottleneck problems of traditional methods and enhancing fault diagnosis capabilities in oilfield extraction processes. Full article

Carbon fiber composite sucker rods represent a technological innovation in oil production systems, exhibiting excellent performance. This sucker rod not only improves oil production efficiency and reduces accidents, but also saves energy and lowers the operating costs of oil wells. However, the working conditions of the carbon fiber composite sucker rod oil extraction system are relatively complex. The carbon fiber composite sucker rod body adopts a symmetrical structure formed by one-time solidification of three layers of fiber (carbon/glass fiber) materials, requiring the use of steel sucker rods in combination, and the impact of various system parameters is not fully understood. This paper focuses on the carbon fiber composite sucker rod as the research object, analyzing the external loads of the carbon fiber composite sucker rod oil extraction system. It also establishes a mechanical model of carbon fiber composite sucker rods, adopts a new finite element modeling method for sucker rod pumping systems, conducts transient dynamic analysis on the lifting motion of carbon fiber composite sucker rods in oil wells, and optimizes system parameters. The example verifies the rationality and feasibility of the finite element model. The results show that the higher the dynamic viscosity of crude oil, the more polished rod dynamometer cars tend to approach a “parallelogram”, and the polished rod load becomes more stable during the lifting process. With larger strokes, the maximum polished rod load increases, the longitudinal vibration amplitude of the carbon fiber composite sucker rod increases, and the load variation becomes more unstable. As the number of strokes increases, the maximum polished rod load and the pump plunger stroke length both increase, leading to higher pump efficiency, but the fluctuation amplitude of the polished rod dynamometer cars also increases, which affects the stability of the sucker rod’s lifting motion. When the carbon fiber sucker rod ratio exceeds 0.5, the difference between the self-weight and polished rod load initially decreases, then increases. As the carbon fiber sucker rod ratio increases, the pump plunger stroke length gradually decreases, and pump efficiency declines. Full article

In deep oil and gas wells, sucker rod strings (SRS) frequently experience breakage and eccentric wear problems. To address this engineering challenge, this study establishes a new coupled three-dimensional (3D) mechanical-mathematical model for sucker rod strings in 3D curved wellbores. The model comprehensively considers well trajectory, rod string structure, and external excitation, analysing the influences of elastic force, inertial force, and friction force on the sucker rod micro-elements. The formulated differential equations are discretised using the central difference method to obtain the configuration of each point on SRS and the 3D distribution of stress and strain, thereby determining the eccentric wear points between the rod and tube. A numerical solution program was developed and successfully applied in the Daqing oilfield. Results from two case studies demonstrate significant improvements: for A1# well, the system efficiency increased from 16% to 20%, while for A2# well, the pump efficiency improved from 39.8% to 58.9% and system efficiency from 33.4% to 35%. The model overcomes previous limitations by considering rod torque, 3D curved tubing spatial coordinates, tubing non-anchoring effects, and forced buckling influence, providing a theoretical basis for dynamic calculations of sucker rod pumping systems in 3D curved wells. Full article

In the process of developing mature deposits, a number of geological and technological complications arise. In order to increase the smooth operation of downhole pumping equipment in oil and gas wells, companies use various methods and techniques. This article presents a novel methodology for predicting downhole pumping equipment failures. A detailed analysis was conducted on historical data regarding downhole pumping equipment failures, which were then incorporated into algorithms to calculate the operation of downhole equipment. As a result, it was discovered that in order to predict failures of downhole equipment, it is crucial to consider the historical data of the field and perform an assessment of the well’s potential. In the process of building a failure prediction model, the authors encountered the quality and completeness of historical data from the pilot field. They concluded that the data classes needed to be more balanced. The authors applied machine learning approaches to an imbalanced dataset. The significance of our approach lies in its ability to forecast equipment failures, thereby ensuring the smooth operation of wells operated by sucker rod pumps. Full article

This paper investigates the application of a self-coder neural network in oilfield rod pump anomaly detection. Rod pumps are critical equipment in oilfield production engineering, and their stability and reliability are crucial to the production efficiency and economic benefits. However, rod pumps are often affected by anomalies such as wax deposition, leading to increased maintenance costs and production interruptions. Traditional wax deposition detection methods are inefficient and fail to provide early warning capabilities. This paper reviews the research progress in sucker rod pump anomaly detection and autoencoder neural networks, providing a detailed description of the construction and training process of the autoencoder neural network model. Utilizing data from the rod-pumped wells of the Tuha oilfield in China, this study achieves the automatic recognition of various anomalies through data preprocessing and the training of an autoencoder model. This study also includes a comparative analysis of the differences in the anomaly detection performance between the autoencoder and traditional methods and verifies the effectiveness and superiority of the proposed method. Full article

The warning of the potential faults occurring in the future in a sucker rod well can help technicians adjust production strategies in time. It is of great significance for safety during well production. In this paper, the key characteristic parameters of dynamometer cards were predicted by a temporal neural network to implement the warning of different working conditions which might result in failures. First, a one-dimensional damped-wave equation was used to eliminate the dynamic loads’ effect of surface dynamometer cards by converting them into down-hole dynamometer cards. Based on the down-hole dynamometer cards, the characteristic parameters were extracted, including the load change, the position of the valve opening and closing point, the dynamometer card area, and so on. The mapping relationship between the characteristic parameters and working conditions (classification model) was obtained by the Xgboost algorithm. Meanwhile, the noise in these parameters was reduced by wavelet transformation, and the rationality of the results was verified. Second, the Encoder–Decoder and multi-head attention structures were used to set up the time series prediction model. Then, the characteristic parameters were predicted in a sequence-to-sequence way by using historical characteristic parameters, date, and pumping parameters as input. At last, by inputting the predicted results into the classification model, a working conditions warning method was created. The results showed that noise reduction improved the prediction accuracy significantly. The prediction relative error of most characteristic parameters was less than 15% after noise reduction. In most working conditions, their F1 values were more than 85%. Most Recall values could be restored to over 90% of those calculated by real parameters, indicating few false negative cases. In general, the warning method proposed in this paper can predict faulty working conditions that may occur in the future in a timely manner. Full article

The production of deep-coalbed methane (CBM) wells undergoes four stages sequentially: drainage depressurization, unstable gas production, stable gas production, and gas production decline. Upon entering the stable production stage, the recovery rate of deep CBM wells is constrained by bottom hole flowing pressure (BHFP). Reducing BHFP can further optimize CBM productivity, significantly increasing the production and recovery rate of CBM wells. This paper optimizes the deliquification process for deep CBM in the Linxing Block. By analyzing the production of deep CBM wells, an improved sucker rod pump deliquification process is proposed, and a method considering the flow in the tubing, annulus, and reservoir is established. Using the production data of Well GK-25D in the Linxing CBM field as an example, an optimized design of the improved rod pump deliquification process was undertaken, with design parameters including the depth of the sucker rod pump, the stroke length, and stroke rate. The results show that the improved process significantly lowers the pressure at the coalbed, enhancing CBM well production by 12.24%. The improved sucker rod pump process enriches deliquification technology for deep CBM, offering a new approach for its development and helping to maximize CBM well productivity. Full article

Simulating surface conditions by solving the wave equation of a sucker-rod string is the theoretical basis of a sucker-rod pumping system. To overcome the shortcomings of the conventional finite difference method and analytical solution, this work describes a novel hybrid method that combines the analytical solution with the finite difference method. In this method, an analytical solution of the tapered rod wave equation with a recursive matrix form based on the Fourier series is proposed, a unified pumping condition model is established, a modified finite difference method is given, a hybrid strategy is established, and a convergence calculation method is proposed. Based on two different types of oil wells, the analytical solutions are verified by comparing different methods. The hybrid method is verified by using the finite difference method simulated data and measured oil data. The pumping speed sensitivity and convergence of the hybrid method are studied. The results show that the proposed analytical solution has high accuracy, with a maximum relative error relative to that of the classical finite difference method of 0.062%. The proposed hybrid method has a high simulation accuracy, with a maximum relative area error relative to that of the finite difference method of 0.09% and a maximum relative area error relative to measured data of 1.89%. Even at higher pumping speeds, the hybrid method still has accuracy. The hybrid method in this paper is convergent. The introduction of the finite difference method allows the hybrid method to more easily converge. The novelty of this work is that it combines the advantages of the finite difference method and the analytical solution, and it provides a convergence calculation method to provide guidance for its application. The hybrid method presented in this paper provides an alternative scheme for predicting the behavior of sucker-rod pumping systems and a new approach for solving wave equations with complex boundary conditions. Full article

In this paper, the entire downhole fluid-sucker rod-pump system is replaced with a viscoelastic vibration model, namely a third-order differential equation with an inhomogeneous forcing term. Both Kelvin’s and Maxwell’s viscoelastic models can be implemented along with the dynamic behaviors of a mass point attached to the viscoelastic model. By employing the time-dependent polished rod force measured with a dynamometer as the input to the viscoelastic dynamic model, we have obtained the displacement responses, which match closely with the experimental measurements in actual operations, through an iterative process. The key discovery of this work is the feasibility of the so-called inverse optimization procedure, which can be utilized to identify the equivalent scaling factor and viscoelastic system parameters. The proposed Newton–Raphson iterative method, with some terms in the Jacobian matrix expressed with averaged rates of changes based on perturbations of up to two independent parameters, provides a feasible tool for optimization issues related to complex engineering problems with mere information of input and output data from either experiments or comprehensive simulations. The same inverse optimization procedure is also implemented to model the entire fluid delivery system of a very viscous non-Newtonian polymer modeled as a first-order ordinary differential equation (ODE) system similar to the transient entrance developing flow. The convergent parameter reproduces transient solutions that match very well with those from fully fledged computational fluid dynamics models with the required inlet volume flow rate and outlet pressure conditions. Full article

Artificial lift systems, such as electrical submersible pumps and sucker rod pumps, frequently encounter operational challenges due to high gas–oil ratios, leading to premature tool failure and increased downtime. Effective upstream gas separation is critical to maintain continuous operation. This study aims to predict the efficiency of downhole gas separator using machine learning models trained on data from a centrifugal separator and tested on data from a gravity separator (blind test). A comprehensive experimental setup included a multiphase flow system with horizontal (31 ft. (9.4 m)) and vertical (27 ft. (8.2 m)) sections to facilitate the tests. Seven regression models—multilinear regression, random forest, support vector machine, ridge, lasso, k-nearest neighbor, and XGBoost—were evaluated using performance metrics like RMSE, MAPE, and R-squared. In-depth exploratory data analysis and data preprocessing identified inlet liquid and gas volume flows as key predictors for gas volume flow per minute at the outlet (GVFO). Among the models, random forest was most effective, exhibiting an R-squared of 96% and an RMSE of 112. This model, followed by KNN, showed great promise in accurately predicting gas separation efficiency, aided by rigorous hyperparameter tuning and cross-validation to prevent overfitting. This research offers a robust machine learning workflow for predicting gas separation efficiency across different types of downhole gas separators, providing valuable insights for optimizing the performance of artificial lift systems. Full article

Fault dynamometer cards are the basis of the diagnosis technique for sucker rod pumping systems. Predicting fault cards with a pumping condition model is an economical and effective method. The usual model is described by a mixed function of the pump displacement and pump load, and it is difficult to use in the prediction method based on the analytical solution of the sucker rod string wave equation. In this paper, a normal pumping condition model described by a function of polished rod velocity is proposed. For the analytical solution of the sucker rod wave equation, an iterative prediction algorithm with pumping condition models is proposed, its convergence is analyzed, and then it is validated by classical finite difference method simulated cards and measured surface dynamometer cards. The results show that the proposed algorithm is accurate. The algorithm has a maximum relative error of 0.10% for the classical method simulated card area and 1.45% for the measured card area. The research of this paper provides an effective scheme for the design, prediction, and fault diagnosis of a sucker rod pumping system with an analytical solution. Full article

Corrosion fatigue test is the most direct and effective method to study the corrosion fatigue characteristics of sucker rod. At present, the commonly used test method is the high frequency fatigue test, but the working state of sucker rod is typical low-frequency and high-cycle corrosion fatigue, and the test with high frequency will reduce the impact of corrosion. Alloy steel 4330 is widely used in coalbed gas well high strength sucker rod, but the research on its low frequency corrosion fatigue life is relatively few. Therefore, in this paper, the corrosion fatigue test method of axial low-frequency and high-cycle was adopted to study the corrosion fatigue characteristics of 4330 steel sucker rod through the corrosion fatigue test under different typical corrosion media, temperature, and stress levels. The results show that the fatigue life of 4330 sucker rod drops sharply when the Cl⁻ concentration in high salinity well fluid exceeds the threshold value of 155 mg/L. When this threshold is exceeded, the downward trend slows down. It can be seen that the significant factor affecting the corrosion fatigue life of 4330 material is not the concentration of Cl⁻, but the existence of Cl⁻. The presence of HCO₃⁻ promotes a further decrease in the corrosion fatigue life of the 4330 sucker rod by Cl⁻. The corrosion fatigue life of 4330 sucker rod decreases with the increase of temperature. When the well fluid temperature is less than 50 °C, the impact is relatively significant. When the well fluid temperature is more than 70 °C, the decline trend of corrosion fatigue life slows down. Based on the fitted S-N curve (stress-fatigue life curve), it is calculated that the fatigue limit of 4330 sucker rod at the stress ratio of 0.6 is 196 MPa in the solution of 10,000 mg/L Cl⁻ at room temperature. These could provide valuable theoretical and technical guidance for design and selection of high-strength sucker rod in high-salinity corrosion well fluid environment. Full article

This study delves into the heightened efficiency and accuracy of 11 deep learning models classifying 11 dynamograph classes in the oil production sector. Introducing a novel framework with the Grad–CAM method, we address the “black box” issue, providing transparency in the models’ decision-making processes. Our analysis includes a comparative study with human experts, revealing a comprehensive understanding of both machine and human interpretive strategies. Results highlight the notable speed and precision of machine learning models, marking a significant advancement in rapid, reliable dynamograph classification for oil production decision-making. Additionally, nuanced findings in the model’s diagnostic accuracy reveal limitations in situations featuring the simultaneous occurrence of multiple pump issues. This underscores the need for additional features and domain-specific logic to enhance discernment and diagnostic precision in complex scenarios. The exploration of qualitative aspects distinguishes interpretive approaches, highlighting strengths and limitations. Machines, driven by algorithmic patterns and data processing, excel in rapid identification, albeit with occasional misclassifications. In contrast, human experts leverage experience and domain-specific knowledge for nuanced interpretation, providing a comprehensive understanding of both quantitative metrics and qualitative nuances. In conclusion, this study not only demonstrates the accelerated and enhanced accuracy of dynamograph classification by machine learning models compared to junior and medior domain experts, but also provides valuable insights into specific features and patterns guiding the decision-making process. This understanding allows continuous refinement, combining machine speed with human understanding for improved results in oil production. The potential for further studies and improvements in this domain is substantial. Full article