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Collaborating Authors
Sun, Dakui
Artificial Lift Methods Optimising and Selecting Based on Big Data Analysis Technology
Shi, Junfeng (RIPED, CNPC) | Chen, Shiwen (RIPED, CNPC) | Zhang, Xishun (RIPED, CNPC) | Zhao, Ruidong (RIPED, CNPC) | Liu, Zhaoyu (Drilling & Production Engineering Department of Jilin Oilfield Company, CNPC) | Liu, Meng (RIPED, CNPC) | Zhang, Na (RIPED, CNPC) | Sun, Dakui (Drilling & Production Engineering Department of Jilin Oilfield Company, CNPC)
Abstract The best artificial lifting method for a well is to lifting more with less during the whole life cycle, however, it is difficult to select the best method because there are many factors affecting the choice of artificial lifting methods and most factors cannot be described with mathematical models. At present, the selection mainly depends on expertsโ experience, which results in many incorrect decisions and lead to huge economic loss. In China, there are a large number of artificial lifting wells, and the large amount of data generated by the wells is of great value, which can be used to select the best artificial lifting method for different type of oil wells. This paper selects 11 parameters, including well fluid characteristics, fluid production capacity, well trajectory, pump depth, pump efficiency, pump inspection period, and maintenance cost per year as influence factors. About 40,000 artificial lifting wells of CNPC were selected as the big data analysis sample initially. As not all the wells are good samples, an effect evaluation function is established by taking pump efficiency, power consumption with lifting per ton liquid 100m, annual operating maintenance cost into account, then the samples are selected further according to the value of effect evaluation function, which ensured all the samples participated in machine learning are good samples. A deep recurrent neural network was established which can select the best artificial lifting method through deep learning. Experimental results showed that the neural network model had fast convergence and high prediction accuracy. With the application of this model, artificial lifting method selecting and effect analysis have been conducted for more than 5,000 oil wells of CNPC with different reservoir characteristics, different wellbore structures and different fluid characteristics. The coincidence rate between calculation results of this model and actual production situation is 90.56%. Big data analysis provides a reliable, practical and intelligent method for optimizing and selecting artificial lift.
- Asia > Middle East > Iraq > Maysan Governorate > Arabian Basin > Widyan Basin > Mesopotamian Basin > Halfaya Field > Mishrif Formation (0.99)
- Asia > China > Jilin > Yanji Basin > Jilin Field (0.99)
Research and Application of Electric Power Curve Inversing Dynamometer Diagram Technology Using Big Data Approach
Zhang, Xishun (RIPED CNPC) | Shi, Junfeng (RIPED CNPC) | Zhao, Ruidong (RIPED CNPC) | Sun, Dakui (Jilin Oilfield CNPC) | Zhang, Xin (RIPED CNPC) | Deng, Feng (RIPED CNPC) | Peng, Yi (RIPED CNPC) | Chen, Shiwen (RIPED CNPC) | Chu, Haoyuan (China University of Petroleum) | Dong, Qing (China University of Petroleum)
Abstract The surface dynamometer diagram of a rod-pumped well is obtained by load sensor, from which oil well working conditions can be analyzed, oil well failures can be diagnosed, and oil production can be calculated. However, such problems as high cost, low popularity and susceptibility to drift and distortion existing with the way to obtain a dynamometer diagram have restricted the development of oil well digital management. Electric parameters are the most basic ones for oil well operation, which are characterized by high popularity, low acquisition cost and stable data. Electric parameters are applied to the dynamometer diagram conversion, analysis and metering, which can take place of the load sensor and realize low-cost and high-efficiency digital management of oil wells. In most cases, the application of electric parameters inversing dynamometer diagram is based on the torque factor method. The torque factor is zero when the polished rod is at the top and bottom dead centers. When this factor is used as a divisor, the calculated load will have no convergence. In this paper, an electric power curve inversion dynamometer diagram approach using big data technologies is proposed: Hadoop technology and Spark technology are utilized to establish a big data platform for electric power curve conversion dynamometer diagram, which collects more than 60,000 site power curve-dynamometer diagram sample data to set up an deep learning sample database. 144 points are selected from the power curve as eigenvalues. After filling the dynamometer diagram, 256 pixels are selected as eigenvalues. So, the eigenvalues are sufficient enough. Deep learning technologies such as Restricted Boltzmann Machine, Sparse AutoEncoder and Softmax Mapping are applied to training sample database, finding out the inter-relation between power curve and dynamometer diagram and obtaining the "power curve-dynamometer diagram conversion model" to realize power curve inversing dynamometer diagram. This technology has been applied in Jilin Oilfield for 350 wells. According to the comparison of a dynamometer diagram converted from power curve with one actually measured, the diagnosis accuracy of working conditions can reach 95.4%, the conformance rate of maximum load is 95%, and the conformance rate of minimum load is 93%. It is shown by field application that using big data technologies the deep learning model is accurate, the algorithm is highly stable, the inversion result is highly precise and the conformance rate is high, which is of great significance to the improvement of digital management level as well as the reduction of production cost. This technology has a promising prospect for popularization.
- Asia > China > Jilin > Yanji Basin > Jilin Field (0.99)
- Asia > China > Hebei > Bohai Basin > Huabei Field (0.98)
- Information Technology > Data Science > Data Mining > Big Data (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.37)
Mobility Management Platform Improves Management Level of Artificial Lift Systems
Shi, Junfeng (RIPED CNPC) | Zhang, Xishun (RIPED CNPC) | Gu, Liming (Jidong Oilfield CNPC) | Sun, Dakui (Jilin Oilfield CNPC) | Zhao, Chun (Changqing Oilfield CNPC) | Su, Lei (Liaohe Oilfield CNPC) | Liang, Jing (CPPEI)
Abstract With the development of automatic production data collection and intelligent phone performance, mobile phone production management platform could be used to improve the management of artificial lift system substantially. In this paper, the development and application of mobile phone platform have been introduced, for the platform, internet of things and wireless transmission technology are applied to achieve instantaneous interaction between mobile phone and server, cloud computing technology is applied to develop distributed calculation module, solving the problems of multi-user complex calculation and batch processing. Based on the platform, four functional modules have been developed: first, mobile data collection module. Login operations, field figures, images and videos, and return timely, and perform real-time monitoring, and production command; Second, data view module, check and count the tendency of artificial lift equipment, operation parameters and test data; Third, data analysis module, analyze artificial lift system backstage of wells and blocks regularly; Fourth, plan design module, realize plan design of field parameter, balance and hot washing. The platform have been applied over 3000 well times in 4 field companies of CNPC, improving the management of artificial lift system, increasing the precision of parameter/balance by more than 10%, raising work efficiency by over 5 times, reducing labor power by more than two-thirds. The application prospect will be very extensive.
- Asia > China > Jilin Province (0.29)
- Asia > China > Heilongjiang Province (0.29)
- Asia > China > Tianjin Province (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > China Government (0.50)
- Asia > China > Tianjin > Bohai Basin > Huanghua Basin > Dagang Field (0.99)
- Asia > China > Shanxi > Ordos Basin > Changqing Field (0.99)
- Asia > China > Shaanxi > Ordos Basin > Changqing Field (0.99)
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