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Reasonable formation pressure maintenance level is significant to high-efficient development of oilfields. In order to study the effects of overlying strata pressure on permeability, oil-water phase permeability curve, and oil displacement efficiency, a physical simulation experiment is designed. Based on the experimental results and the oil-water phase flow theory, the production equation and the mathematical model of oil displacement efficiency considering stress sensitivity are established. And the productivity changes with pressure drop under different permeability are plotted. Then, the permeability coefficients calculated by quantitative characterization of stress sensitivity under different formation pressures are introduced into the numerical simulation model to quantitatively determine the reasonable formation pressure maintenance level of different reservoir properties. Experimental and theoretical studies show that the permeability decreases continuously with the increase in effective overlying strata pressure. In a low permeability reservoir, the more permeability decrease is caused by the increase in effective overlying strata pressure. When reservoir pressure is restored by water injection, the permeability loss is irreversible. With the increase in effective overlying strata pressure, the producer productivity decreases obviously, and the effective seepage capacity and oil displacement efficiency decrease. For reservoirs with permeability below 100 mD and high stress sensitivity, high formation pressure level should be maintained. For reservoirs with permeability of more than 300 mD, lower formation pressure is acceptable in the initial stage. The results are consistent with the actual production characteristics, which effectively guide the establishing of reasonable oilfield development strategy. It has important guiding significance to the oilfield development plans and development of the middle-deep oilfields. [ABSTRACT FROM AUTHOR]

The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction. [ABSTRACT FROM AUTHOR]