Pump Detection Period Predicting of Pump Well Based on Feature Fusion

Abstract

Abstract: Pump detection period is an important index to reflect the working reliability of pumping wells. Accurate prediction of pump detection period is of great practical significance to improve oil well production efficiency and economic benefits. Aiming at the low accuracy of pump detection period prediction in oil field, a pump detection period prediction method based on feature fusion is proposed. This method introduces SVR to extract the static characteristics of oilfield data, reconstructs the characteristics of oilfield dynamic data, uses convolution neural network to learn the dynamic characteristics of oilfield data, introduces multi-modal compression bilinear pooling to fuse the static and dynamic characteristics, and uses discriminant model to train the fusion characteristics to realize the accurate prediction of pump detection cycle. The experimental results verify the effectiveness and feasibility of the model.

Key words: feature extraction, feature fusion, pump detection period predicting, support vector regression, convolutional neural network

ZHANG Xiao-dong, WANG Xu-ying, QIN Zi-xuan. Pump Detection Period Predicting of Pump Well Based on Feature Fusion[J]. Computer and Modernization, 2022, 0(12): 60-66.

References

［1］吴小文. 油田油井检泵作业原因分析及治理对策［J］. 化学工程与装备, 2021(1):33-34.
［2］周平,单长吉,刘丽丹. 抽油机井管杆偏磨检泵周期与其影响因素回归分析［J］. 东北电力大学学报, 2008,28(4):11-17.
［3］任伟建,任欣元,王磊,等. 基于威布尔分布的油田机采井故障率研究［J］. 信息与控制, 2015,44(6):722-728.
［4］张战敏. 抽油机井检泵周期可靠性预测模型与实际应用［J］. 价值工程, 2015,34(8):31-32.
［5］赵廷峰,赵春艳,何帆,等. 抽油杆柱磨损分析与安全性评价［J］. 石油机械, 2017,45(8):65-70.
［6］王旭东,李斌. 水平井抽油杆扶正器结构优化和疲劳寿命分析［J］. 西部探矿工程, 2016,28(12):30-31.
［7］万夫,周兆明,张健,等. 基于在线检测数据优化连续油管疲劳寿命预测［J］. 钻采工艺, 2020,43(6):9-12.
［8］赵岩龙,方正魁,邱子瑶,等. 基于长短时记忆网络的腐蚀工况下抽油杆剩余使用寿命预测［J］. 科学技术与工程, 2021,21(36):15429-15433.
［9］侯延彬,陈炳均,高宪文. 基于GM-ELM的有杆泵抽油井故障诊断［J］. 东北大学学报(自然科学版), 2019,40(12):1673-1678.
［10］ZHANG R, ZHANG W, SHEN S, et al. Evaluation of the correlations between laboratory measured material properties with field cracking performance for asphalt pavement［J］. Construction and Building Materials, 2021,301. DOI:10.1016/j.conbuildmat.2021.124126.
［11］CHEN Y, XU P, CHU Y, et al. Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings［J］. Applied Energy, 2017,195:659-670.
［12］DENDI S V R, CHANNAPPAYYA S S. No-reference video quality assessment using natural spatiotemporal scene statistics［J］. IEEE Transactions on Image Processing, 2020,29:5612-5624.
［13］DHIMAN H S, DEB D, GUERRERO J M. Hybrid machine intelligent SVR variants for wind forecasting and ramp events［J］.Renewable and Sustainable Energy Reviews, 2019,108:369-379.
［14］FEIZIZADEH B, ROODPOSHTI M S, BLASCHKE T, et al. Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping［J］. Arabian Journal of Geosciences, 2017,10(5). DOI:10.1007/s12517-017-2918-z.
［15］XIONG J, LIANG Q, WAN J, et al. The order statistics correlation coefficient and PPMCC fuse non-dimension in fault diagnosis of rotating petrochemical unit［J］. IEEE Sensors Journal, 2018,18(11):4704-4714.
［16］XIE C, KUMAR A. Finger vein identification using Convolutional Neural Network and supervised discrete hashing［J］. Pattern Recognition Letters, 2019,119:148-156.
［17］JUNG S, LEE J, CHO H, et al. Compton background elimination for in vivo x-ray fluorescence imaging of gold nanoparticles using convolutional neural network［J］. IEEE Transactions on Nuclear Science, 2020,67(11):2311-2320.〖HJ0.6mm〗
［18］ZHANG Y, LEE T S, LI M, et al. Convolutional neural network models of V1 responses to complex patterns［J］. Journal of Computational Neuroscience, 2019,46(1):33-54.
［19］LU Z, JIANG X, KOT A. Deep coupled ResNet for low-resolution face recognition［J］. IEEE Signal Processing Letters, 2018,25(4):526-530.
［20］BALTRUSCHAT I M, NICKISCH H, GRASS M, et al. Comparison of deep learning approaches for multi-label chest x-ray classification［J］. Scientific Reports, 2019,9(1). DOI:10.1038/s41598-019-42294-8.
［21］LU Z, BAI Y, CHEN Y, et al. The classification of gliomas based on a Pyramid dilated convolution resnet model［J］. Pattern Recognition Letters, 2020,133:173-179.
［22］LEE J, SHRIDHAR K, HAYASHI, et al. ProbAct: A probabilistic activation function for deep neural networks ［J］.arXiv preprint arXiv:1905.10761, 2019.
［23］ARIAV I, COHEN I. An end-to-end multimodal voice activity detection using waveNet encoder and residual networks［J］. IEEE Journal of Selected Topics in Signal Processing, 2019,13(2):265-274.
［24］NOWAKOWSKA E, KORONACKI J, LIPOVETSKY S. Clusterability assessment for Gaussian mixture models［J］. Applied Mathematics and Computation, 2015,256(C):591-601.
［25］LORAH J, WOMACK A. Value of sample size for computation of the Bayesian Information Criterion (BIC) in multlevel modeling［J］. Behavior Research Methods, 2019, 51(1):440-450.
［26］YANG M, LAI C, LIN C. A robust EM clustering algorithm for Gaussian mixture models［J］. Pattern Recognition, 2012,45(11):3950-3961.
［27］LEI X, FANG Z. GBDTCDA: Predicting circRNA-disease associations based on gradient boosting decision tree with multiple biological data fusion［J］. International Journal of Biological Sciences, 2019,15(13):2911-2924.
［28］GUO F, LIU Z, HU W, et al. Gain prediction and compensation for subarray antenna with assembling errors based on improved XGBoost and transfer learning［J］. IET Microwaves, Antennas & Propagation, 2020,14(6). DOI:10.1049/iet-map.2019.0182.
［29］MA J, CHENG J C P, XU Z, et al. Identification of the most influential areas for air pollution control using XGBoost and Grid Importance Rank［J］. Journal of Cleaner Production,2020,274. DOI:10.1016/j.jclepro.2020.122835.
［30］ZHANG Z, HUANG Y, QIN R, et al. XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling［J］. Journal of Manufacturing Processes, 2021,64:30-44.
［31］NANDINI N, SHENGPING Y, ENRIQUE G. Impact of mechanical circulatory support on post-transplant stroke risk［J］.The International Journal of Artificial Organs, 2021,44(10):675-680.
［32］WANG Z, ZHAN Z, KWONG S, et al. Adaptive granularity learning distributed particle swarm optimization for large-scale optimization［J］. IEEE Transactions on Cybernetics, 2020. DOI:10.1109/TCYB.2020.2977956.
［33］CHIRI H, ABASCAL A J, CASTANEDO S, et al. Mid-long term oil spill forecast based on logistic regression modelling of met-ocean forcings［J］. Marine Pollution Bulletin, 2019,146:962-976.
［34］王鑫,吴际,刘超,等. 基于LSTM循环神经网络的故障时间序列预测［J］. 北京航空航天大学学报, 2018,44(4):772-784.

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