Threshold velocity for wind erosion: the effects of porous fences

Porous fence is a kind of artificial windbreak that has many practical applications. The threshold wind velocities at different distances downwind from porous fences were measured and the corresponding characteristics of particle movement observed to assess their shelter effect. It is found that the fence’s porosity is the key factor that determines the resulting shelter effect. The area near a fence can be typically classified into five regions, each with a different mode of particle movement. Dense fences, and especially solid fences, favor the accumulation of sand upwind of the fences. Fences with porosities of 0.3–0.4 produce the maximum threshold wind velocity; those with porosities of 0.3–0.6 (depending on the fence height) provide the maximum effective shelter distance. It is confirmed that the fence porosities of 0.3–0.4 that have been proposed for practical application in previous research are the most effective for abating wind erosion.     

Water–rock interaction during the process of steam stimulation exploitation of viscous crude oil in Liaohe Shuguang Oil Field, Liaoning, China

In the process of steam stimulation exploitation of viscous crude oil, the injected water, at high temperature and under high pressure, reacts intensively with the host rock. This kind of water–rock interaction in Liaohe Shuguang Oil Field was studied on the basis of analysis of water composition changes, laboratory experiments, mineral saturation indices analysis, and mass balance calculation. Compared with the injected water, the changes of the composition of discharged water are mainly the distinct decrease of pH, Na⁺, SiO₂ and Cl⁻, as well as the increase of K⁺, Ca²⁺, Mg²⁺, SO ₄ ²⁻ and HCO ₃ ⁻ . Laboratory experiments under field conditions showed: the dissolution sequence of minerals quantitatively is quartz>potassium feldspar>albite, and the main change of clay minerals is the conversion of kaolinite to analcime. Mass balance calculation indicated during the process of steam stimulation, large quantities of analcime are precipitated with the dissolution of large amounts of quartz, kaolinite, potassium feldspar, and CO₂. These results correlated very well with the experimental results. The calculated results of Liaohe Shuguang Oil Field showed that during the steam stimulation for viscous crude oil, the amounts of minerals dissolved (precipitated) are huge. To control the clogging of pore spaces of oil reservoirs, increased study of water–rock interaction is needed.     

T -distributed Random Fields: A Parametric Model for Heavy-tailedWell-log Data1

Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they are layer specific and include uncertainty caused by using variance estimates.