Analytical method for evaluating stress field in casing-cement-formation system of oil/gas wells

In this paper, we present an analytical method for evaluating the stress field within a casing-cement-formation system of oil/gas wells under anisotropic in-situ stresses in the rock formation and uniform pressure within the casing. The present method treats the in-situ stresses in the formation as initial stresses since the in-situ stresses have already developed in the formation before placement of cement and casing into the well. It is demonstrated that, via this treatment, the present method excludes additional displacements within the formation predicted by the existing method, and gives more reasonable stress results. An actual tight-oil well is analyzed using the present and existing analytical methods, as well as the finite element method. Good agreement between the analytical results and the finite element analysis （FEA） results is obtained, validating the present method. It is also evident that, compared with the present method, the existing method overestimates the compressive stress level within the casing and the cement. Finally, the effects of elastic properties of the formation, cement, and inner pressure of casing on stresses within the casing and cement are illustrated with a series of sensitivity analyses.

Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT.By feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is improved.Generally,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology.The efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud infrastructure.Nevertheless,the execution efficiency of PW decreases due to inappropriate sub-task and data placement.In addition,the power consumption explodes due to massive data acquisition.To address these challenges,a PW placement method named PWP is devised.Specifically,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement strategies.The simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.

Analysis on Irrational Decision Making of Tobacco Family Farmers from the Perspective of Optimal Scale of Efficiency

The larger the difference between the willingness scale of tobacco family farmers and the optimal scale of efficiency,the greater the degree of irrationality,and the higher the decision making risk.With the aid of DEA model,this study calculated the optimal scale of efficiency of Guiyang tobacco family farms.Using the ratio of willingness scale and efficiency optimal scale,it measured the degree of irrationality of family farmers.In addition,with the help of multiple linear regression model,it explained the irrational decision making mechanism of family farmers.Finally,it made a portrait of farmers who tend to make irrational decisions,to find specific farmers and guide them in their production and operation,reduce the risk of planting scale decision making and stabilize the sustainable development of the tobacco industry.