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Productivity prediction is very important in the exploration and development of oilfields. Using well log data to predict productivity is a front-line technology， which is key issue in petroleum exploration phase. The essential factors of productivity prediction is building practical models and correcting various causes to improve precision of prediction parameters. Any errors of parameters selections can affect the calculation of productivity prediction; therefore， how to improve research means and calculation accuracy is an important task of productivity prediction. Theory and case examples are deeply and comprehensively studied in the paper. Based on the theory of mud-filtrate invasion and experimental results， the damage of drilling， cementing， perforating，acidizing and fracturing were investigated. The damage depth was quantitatively evaluated by log data， based on this， the processing results of reservoir sensitivity were used to analysis quantitatively the damage of reservoir. The productivity prediction and reservoir damage were initiatively incorporated according to well logging， and the precision of productivity prediction was effectively improved. The method of NMR was explored to calculate the fluid viscosity on the basis of reservoir physical method， and the differences between the two methods were compared in the paper. From the theory fluid flow in porous media， various of theoretical models of production prediction were explored and several practical models were consided， such as productivity index method， improved productivity index method， improved Bearder method， SVM and so on. The characteristic and the application scope of these methods were studied. The inflow productivity and outflow productivity were incorporated and nodal analysis method was used to forecast wellhead yield， thus achieved scientifically production. On the applied background of conventional logging suite， the applying of special items or new logging method which is practical in the research area were studied， the logging suite was further optimized， and the precision of forecast was improved. On the basis of the modeling and the calculation of parameters， these methods were verified and analyzed， and the reconstruct principle was also built for block reservoir. The research block was processed by these methods and compared with testing data. Based on above the research， a technological system which is practical for shaly sand profiles in Shengli Oilfield was built. The system can reach commercialized degree，and satisfied the need of exploration and development of the oilfield.

Predicting protein interaction interfaces from protein sequences: Case studies of subtilisin and phycocyanin

Identification of protein interaction interfaces is very important for understanding the molecular mechanisms underlying biological phenomena. Here, we present a novel method for predicting protein interaction interfaces from sequences by using PAM matrix (PIFPAM). Sequence alignments for interacting proteins were constructed and parsed into segments using sliding windows. By calculating distance matrix for each segment, the correlation coefficients between segments were estimated. The interaction interfaces were predicted by extracting highly correlated segment pairs from the correlation map. The predictions achieved an accuracy 0.41-0.71 for eight intraprotein interaction examples, and 0.07-0.60 for four interprotein interaction examples. Compared with three previously published methods, PIFPAM predicted more contacting site pairs for 11 out of the 12 example proteins, and predicted at least 34% more contacting site pairs for eight proteins of them. The factors affecting the predictions were also analyzed. Since PIFPAM uses only the alignments of the two interacting proteins as input, it is especially useful when no three-dimensional protein structure data are available.