Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk.

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An average current-sensing circuit integrated in a hybrid DC-DC buck converter is presented. Both the high-side and the low-side switch currents are fully sensed by detecting the filtered smooth voltage of the switch node, which maintains a substantially constant mode changing point and seamless transition between pulse width modulation (PWM) and pulse frequency modulation (PFM) under various duty cycles. The achieved mode changing point is almost independent of temperature and supply voltage. The proposed circuit is suitable for high-frequency hybrid DC-DC applications.

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