Diagnosing injection-production system faults in the same well using the rough set-LVQ neural network.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.

Study on the Powder-Spreading Process of Walnut Shell/Co-PES Biomass Composite Powder in Additive Manufacturing.

Powder laying is a necessary procedure during powder bed additive manufacturing (PBAM), and the quality of powder bed has an important effect on the performance of products. Because the powder particle motion state during the powder laying process of biomass composites is difficult to observe, and the influence of the powder laying process parameters on the quality of the powder bed is still unclear, a simulation study of the biomass composite powder laying process during powder bed additive manufacturing was conducted using the discrete element method. A discrete element model of walnut shell/Co-PES composite powder was established using the multi-sphere unit method, and the powder-spreading process was numerically simulated using two different powder spreading methods (rollers/scrapers). The results showed that the quality of powder bed formed by roller laying was better than that formed by scrapers with the same powder laying speed and powder laying thickness. For both of the two different spreading methods, the uniformity and density of the powder bed decreased as spreading speed increased, although the spreading speed had a more important influence on scraper spreading compared to roller spreading. As powder laying thickness increased, the powder bed formed by the two different powder laying methods became more uniform and denser. When the powder laying thickness was less than 110µm, the particles were easily blocked at the powder laying gap and are pushed out of the forming platform, forming many voids, and decreasing the powder bed's quality. When the powder thickness was greater than 140 µm, the uniformity and density of the powder bed increased gradually, the number of voids decreased, and the quality of the powder bed improved.

Study of Forming Performance and Characterization of DLP 3D Printed Parts.

In order to explore the effect of printing parameter configurations on the forming performance of Digital Light Processing (DLP) 3D printed samples, printing experiments were carried out on the enhanced adhesion and efficient demolding of DLP 3D printing devices. The molding accuracy and mechanical properties of the printed samples with different thickness configurations were tested. The test results show that when the layer thickness increases from 0.02 mm to 0.22 mm, the dimensional accuracy in the X and Y directions increases first and then decreases, while the dimensional accuracy in the Z direction decreases, and the dimensional accuracy is the highest when the layer thickness is 0.1 mm. The mechanical properties of the samples decline with an increasing layer thickness of the samples. The mechanical properties of the 0.08 mm layer thickness are the best, and the tensile, bending, and impact properties are 22.86 Mpa, 48.4 Mpa, and 35.467 KJ/m2, respectively. Under the condition of ensuring molding accuracy, the optimal layer thickness of the printing device is determined to be 0.1 mm. The analysis of the section morphology of samples with different thicknesses illustrates that the fracture of the sample is a river-like brittle fracture, and there are no defects such as pores in the section of samples.

Main control factors affecting mechanical oil recovery efficiency in complex blocks identified using the improved k-means algorithm.

The system efficiency of pumping units in the middle and late stages of oil recovery is characterized by several factors, complex data and poor regulation. Further, the main control factors that affect system efficiency in different blocks vary greatly; therefore, it is necessary to obtain the block characteristics to effectively improve system efficiency. The k-means algorithm is simple and efficient, but it assumes that all factors have the same amount of influence on the output value. This cannot reflect the obvious difference in the influence of several factors in the block on the efficiency. Moreover, the algorithm is sensitive to the selection of the initial cluster centre point, so each calculation result that reflects the efficiency characteristics of the block system cannot be unified. To solve the aforementioned problems affecting the k-means algorithm, the correlation coefficient of all the factors was first calculated, followed by extracting the system efficiency of the positive and negative indicators of standardization. Next, the moisture value was calculated to obtain the weight of each factor used as a coefficient to calculate the Euclidean distance. Finally, the initial centre point selection of the k-means algorithm problem was solved by combining the dbscan and weighted k-means algorithm. Taking an oil production block in the Daqing Oilfield as the research object, the k-means and improved algorithm are used to analyse the main control factors influencing mechanical production efficiency. The clustering results of the two algorithms have the characteristics of overlapping blocks, but the improved algorithm's clustering findings are as follows: this block features motor utilization, pump efficiency and daily fluid production, which are positively correlated with system efficiency. Further, low-efficiency wells are characterized by the fact that the pump diameter, power consumption, water content, daily fluid production, oil pressure and casing pressure are significantly lower than the block average; high-efficiency wells are characterized by pump depths lower than the block average. For this block, it is possible to reduce the depth of the lower pump and increase the water-injection effect to increase the output under conditions of meeting the submergence degree, which can effectively improve the system efficiency.

Impact of Particle Size on Performance of Selective Laser Sintering Walnut Shell/Co-PES Powder.

The agricultural and forestry waste walnut shell and copolyester hot-melt adhesives (Co-PES) powder were selected as feedstock. A kind of low-cost, low-power consumption, and environmentally friendly walnut shell/Co-PES powder composites (WSPC) was used for selective laser sintering (SLS). Though analyzing the size and morphology of walnut shell particle (≤550 µm) as well as performing an analysis of surface roughness, density, and mechanical test of WSPC parts with different particle sizes, results showed that the optimal mechanical performance (tensile strength of 2.011 MPa, bending strength of 3.5 MPa, impact strength of 0.718 KJ/m2) as walnut shell powder particle size was 80 to 120 µm. When walnut shell powder particle diameter was 120 to 180 µm, the minimum value of surface roughness of WSPC parts was 15.711 µm and density was approximately the maximum (0.926 g/cm3).

Fault diagnosis method of submersible screw pump based on random forest.

The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.