In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the ...In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.展开更多
An exact forecast of the failures of a sucker rod-pumped well in a production area means much for an oilfield’s operation budget, operational arrangement and production plan. In this paper, according to the characte...An exact forecast of the failures of a sucker rod-pumped well in a production area means much for an oilfield’s operation budget, operational arrangement and production plan. In this paper, according to the characteristics of failed sucker rod-pumped well randomness and strong outburst, with the gray GM (1,1) forecast model and the Markov forecast model combined, gray GM (1,1) forecast model is utilized to handle the primary data of an oilfield, and Markov forecast model is utilized to calculate the state transfer probability of forecast value. Then, the gray Markov forecast model considering the influence of randomness factors is formed. Field results prove that the calculation precision of this method is higher and the practicability is greater.展开更多
A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method i...A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method is pattern recognition of a dynamometer card(DC)based on feature extraction and perceptron.The premise of this method is that the training and target data have the same distribution.However,the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions,which may significantly affect the diagnostic accuracy.To address this issue,in this study,an improved model of the sucker rod string(SRS)is derived by adding faultparameter dimensions,with which DCs under 16 working conditions could be generated.Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data.Meanwhile,to further improve the accuracy of the proposed method,the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples.The parameters of the perceptron are optimized to promote its discriminability.Finally,the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.展开更多
Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification a...Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.展开更多
Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accurac...Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.展开更多
Inuestigated wear tests of grade D sucker rod and N80 tubing with a produced liquid are the effect of the water cut of produced liquid on the wear rate and the wear coefficient of the friction pair. And presented here...Inuestigated wear tests of grade D sucker rod and N80 tubing with a produced liquid are the effect of the water cut of produced liquid on the wear rate and the wear coefficient of the friction pair. And presented here is a comparison of tribological performance of tubing/sucker rod with sprayed tubing/sucker rod and tubing/sprayed sucker rod. The results show that one of the main reasons for wear failure between the sucker rod and the tubing is a high water cut of the produced liquid. The wear rate increases greatly when the water cut ranges from 70% to 85%, and increases gradually when the water cut is less than 70% or more than 85%. The higher the water cut is, the greater the wear rate will be. SEM (scanning electron microscope) micrographs show that the worn surface of the tubing is smoother, with shallow grooves, when the water cut is 55%; when the water cut is 95%, there are broader and deeper grooves on the worn surface. The results also show that the wear rate of the sprayed wear-resistant coating of sucker rod/tubing is less than that of the unsprayed sucker rod/tubing.展开更多
Composite sucker rods are widely used in oil fields because of light weight,high strength,and corrosion resistance.Bonded technology becomes the primary connection method of composites.However,the joints with composit...Composite sucker rods are widely used in oil fields because of light weight,high strength,and corrosion resistance.Bonded technology becomes the primary connection method of composites.However,the joints with composite sucker rods are prone to debone and fracture.The connected characteristics are less considered,so the failure mechanism of the joint is still unclear.Based on the cohesive zone model(CZM)and the Johnson-Cook constitutive model,a novel full-scale numerical model of the joint with composite sucker rod was established,and verified by pull-out experiments.The mechanical properties and slip characteristics of the joint were studied,and the damaged procession of the joint was explored.The results showed that:a)the numerical model was in good agreement with the experimental results,and the error is within 5%;b)the von Mises stress,shear stress,and interface stress distributed symmetrically along the circumferential path increased gradually from the fixed end to the loading end;c)the first-bonded interface near the loading end was damaged at first,followed by debonding of the second-bonded interface,leading to the complete shear fracture of the epoxy,and resulted in the debonding of the joint with composite sucker rod,which can provide a theoretical basis for the structural design and optimization of the joint.展开更多
It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production sche...It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production scheme can be designed when computer diagnosis is used. However, it is difficult to make a comprehensive analysis to supply efficient technical guidance for operation of the pumping well with multiple faults of down-hole conditions, which cannot be effectively dealt with by the common methods. To solve this problem, a method based on designated component analysis （DCA） is used in this paper. Freeman chain code is used to represent the down-hole dynamometer card whose important characteristics are extracted to construct a designated mode set. A control chart is used as a basis for fault detection. The upper and lower control lines on the control chart are determined from standard samples in normal working conditions. In an incompletely orthogonal mode, the designated mode set could be divided into some subsets in which the modes are completely orthogonal. The observed data is projected into each designated mode to realize fault detection according to the upper and lower control lines. The examples show that the proposed method can effectively diagnose multiple faults of down-hole conditions.展开更多
基金supported by the National Natural Science Foundation of China under Grant 52325402, 52274057, 52074340 and 51874335the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028。
文摘In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.
文摘An exact forecast of the failures of a sucker rod-pumped well in a production area means much for an oilfield’s operation budget, operational arrangement and production plan. In this paper, according to the characteristics of failed sucker rod-pumped well randomness and strong outburst, with the gray GM (1,1) forecast model and the Markov forecast model combined, gray GM (1,1) forecast model is utilized to handle the primary data of an oilfield, and Markov forecast model is utilized to calculate the state transfer probability of forecast value. Then, the gray Markov forecast model considering the influence of randomness factors is formed. Field results prove that the calculation precision of this method is higher and the practicability is greater.
基金support by the Major Scientific and Technological Projects of CNPC under Grant no.ZD2019-184-004the National Research Council of Science and Technology Major Project under Grant no.2016ZX05042004+1 种基金the Fundamental Research Funds for the Central University under Grant no.20CX02307Athe Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment under Grant no.20CX02307A
文摘A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method is pattern recognition of a dynamometer card(DC)based on feature extraction and perceptron.The premise of this method is that the training and target data have the same distribution.However,the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions,which may significantly affect the diagnostic accuracy.To address this issue,in this study,an improved model of the sucker rod string(SRS)is derived by adding faultparameter dimensions,with which DCs under 16 working conditions could be generated.Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data.Meanwhile,to further improve the accuracy of the proposed method,the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples.The parameters of the perceptron are optimized to promote its discriminability.Finally,the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.
基金support from the Key Project of the National Natural Science Foundation of China (61034005)Postgraduate Scientific Research and Innovation Projects of Basic Scientific Research Operating Expenses of Ministry of Education (N100604001)
文摘Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.
基金the National Natural Science Foundation of China (Grant No. 61403040)
文摘Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.
文摘Inuestigated wear tests of grade D sucker rod and N80 tubing with a produced liquid are the effect of the water cut of produced liquid on the wear rate and the wear coefficient of the friction pair. And presented here is a comparison of tribological performance of tubing/sucker rod with sprayed tubing/sucker rod and tubing/sprayed sucker rod. The results show that one of the main reasons for wear failure between the sucker rod and the tubing is a high water cut of the produced liquid. The wear rate increases greatly when the water cut ranges from 70% to 85%, and increases gradually when the water cut is less than 70% or more than 85%. The higher the water cut is, the greater the wear rate will be. SEM (scanning electron microscope) micrographs show that the worn surface of the tubing is smoother, with shallow grooves, when the water cut is 55%; when the water cut is 95%, there are broader and deeper grooves on the worn surface. The results also show that the wear rate of the sprayed wear-resistant coating of sucker rod/tubing is less than that of the unsprayed sucker rod/tubing.
基金Innovation fund project for graduate students of ChinaUniversity of Petroleum(East China)(No.22CX04032A)the Fundamental Research Funds for the CentralUniversities on this study is gratefully acknowledged+2 种基金the support of‘National Natural Science Foundation of China’(No.52304015)‘Postdoctoral Innovation Project of Shandong Province’(No.SDCX-ZG-202203098)‘Qingdao Postdoctoral Grant Project’(No.qdyy20210083).
文摘Composite sucker rods are widely used in oil fields because of light weight,high strength,and corrosion resistance.Bonded technology becomes the primary connection method of composites.However,the joints with composite sucker rods are prone to debone and fracture.The connected characteristics are less considered,so the failure mechanism of the joint is still unclear.Based on the cohesive zone model(CZM)and the Johnson-Cook constitutive model,a novel full-scale numerical model of the joint with composite sucker rod was established,and verified by pull-out experiments.The mechanical properties and slip characteristics of the joint were studied,and the damaged procession of the joint was explored.The results showed that:a)the numerical model was in good agreement with the experimental results,and the error is within 5%;b)the von Mises stress,shear stress,and interface stress distributed symmetrically along the circumferential path increased gradually from the fixed end to the loading end;c)the first-bonded interface near the loading end was damaged at first,followed by debonding of the second-bonded interface,leading to the complete shear fracture of the epoxy,and resulted in the debonding of the joint with composite sucker rod,which can provide a theoretical basis for the structural design and optimization of the joint.
基金supported by the Key Program of National Natural Science Foundation of China (61034005)Postgraduate Scientific Research and Innovation Projects of Basic Scientific Research Operating Expensesof Ministry of Education (N100604001)Excellent Doctoral Dissertations Cultivation Project of Northeastern University
文摘It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production scheme can be designed when computer diagnosis is used. However, it is difficult to make a comprehensive analysis to supply efficient technical guidance for operation of the pumping well with multiple faults of down-hole conditions, which cannot be effectively dealt with by the common methods. To solve this problem, a method based on designated component analysis （DCA） is used in this paper. Freeman chain code is used to represent the down-hole dynamometer card whose important characteristics are extracted to construct a designated mode set. A control chart is used as a basis for fault detection. The upper and lower control lines on the control chart are determined from standard samples in normal working conditions. In an incompletely orthogonal mode, the designated mode set could be divided into some subsets in which the modes are completely orthogonal. The observed data is projected into each designated mode to realize fault detection according to the upper and lower control lines. The examples show that the proposed method can effectively diagnose multiple faults of down-hole conditions.
