Inspired by the consideration of some inside and future market information in financial market, a class of anticipated backward doubly stochastic Volterra integral equations (ABDSVIEs) are introduced to induce dynamic risk measures for risk quantification. The theory, including the existence, uniqueness and a comparison theorem for ABDSVIEs, is provided. Finally, dynamic convex risk measures by ABDSVIEs are discussed.
AbstractThe Sinian gas reservoir in the Sichuan Basin has the potential for natural gas exploration and development. Production prediction and risk quantification are important in planning of natural gas resources. Ultimate recoverable reserves (URRs) of Sinian gas reservoir are estimated. Hubbert and Gauss models are used to predict the growth trend of production in the gas reservoir. Based on the prediction results, the Monte Carlo simulation is used to calculate the probability of production realization. The evaluation matrix of risk level is established by using indices of production realization probability and dispersion degree for assessing the risk level of natural gas production. The results show that: (1) compared to the Hubbert model, the production prediction results of the Gauss model have higher accuracy. The Sinian gas reservoir will reach peak production of $$\left( {140 - 285} \right) \times 10^{8} {\text{m}}^{3} /{\text{a}}$$
140
-
285
×
10
8
m
3
/
a
in 2036 and will have stable production from 2032 to 2040. By the end of the stable production stage, the URR exploitation degree is about 60% and (2) the Monte Carlo method can be used to obtain the production realization probability for each year. The risk level evaluation matrix can be established by taking the probability of realization and the dispersion degree as evaluation indices, which can provide the systematization of the risk levels. The study can help to better understand the guiding significance for the natural gas exploration and development.
Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa
