The storage of petroleum products above ground surface has many constraints and limitations. A viable alternative is to excavate large underground spaces in rock to provide a safer way for oil storage. Soft rock formations such as salt domes provide suitable conditions from environmental and operational aspects. The potential for high volume storage and low permeability are among advantages of oil storage in caverns excavated in salt rocks. The complicated shape of oil storage caverns, complex behavior of salt rock, and boundary conditions associated with large underground openings are major challenges in the design of salt caverns excavated for oil storage purposes. In this study, the deformation mechanism and stability of salt caverns were investigated. A comprehensive 3D numerical study was carried out to investigate the effects of cavern size and depth, salt rock deformation modulus, and ground in-situ stress regime on the behavior of large salt caverns. The stress field and deformation mechanisms were studied numerically aiming at shedding lights into the design aspects of salt caverns for oil storage. The analysis results show that the cavern safety factor is reduced as a function of cavern depth and storage volume. Also, with decrease in k (ratio of horizontal to vertical in-situ stress), the stability of salt caverns will increase; however, with increase in salt rock young modulus, the sensitivity of cavern stability to k ratio is reduced. The Pwipp constitutive model was used in the numerical analysis. This model gives a good description of salt rock creep behavior under varying stress levels. This model also is calibrated based on laboratory and field testing [25]. A set of parameters given for the Pwipp model by [11] was used in the presented study (see Table I). Figure 16. Run no.16 stability analysis results (volume 1, E = 18 GPa, k = 0.75): a) Cavern wall displacement history, b) contours of minimum principal stress distribution, c) safety factor along measuring lines, d) contours of wall safety factor, e) SFR change along measuring lines, f) contours of SFR change. [Colour figurecan be viewed at wileyonlinelibrary.com]

In the first stage of this work, the degradation temperature of polyamide-12 was investigated using biocompatible fuels as a reference with different temperatures by thermogravimetric analysis (TGA) test. Those fuels were containing 20% and 85% ethanol as well as ethanol-free. In the second stage, the multilayer perceptron (MLP) neural network and radial basis function neural network (RBF) were designed to predict the degradation temperature of polyamide-12. Fuel temperature, ethanol percentage, and the time of placing samples in the fuel were selected as input and polymer degradation temperature was defined as network output. The results obtained from the modeling were compared with the results obtained from the test TGA. The results obtained from neural networks MLP and RBF showed slight differences with the experimental results that can be used as an efficient and low-cost tool to predict the degradation temperature of polymers.