Ultrasonic velocities of a set of saturated sandstone samples were measured at simulated in-situ pressures in the laboratory. The samples were obtained from the W formation of the WXS Depression and covered low to nearly high porosity and permeability ranges. The brine and four different density oils were used as pore fl uids, which provided a good chance to investigate fluid viscosity-induced velocity dispersion. The analysis of experimental observations of velocity dispersion indicates that (1) the Biot model can explain most of the small discrepancy (about 2 -3%) between ultrasonic measurements and zero frequency Gassmann predictions for high porosity and permeability samples saturated by all the fl uids used in this experiment and is also valid for medium porosity and permeability samples saturated with low viscosity fluids (less than approximately 3 mP•S) and (2) the squirt flow mechanism dominates the low to medium porosity and permeability samples when fl uid viscosity increases and produces large velocity dispersions as high as about 8%. The microfracture aspect ratios were also estimated for the reservoir sandstones and applied to calculate the characteristic frequency of the squirt fl ow model, above which the Gassmann' s assumptions are violated and the measured high frequency velocities cannot be directly used for Gassmann's fluid replacement at the exploration seismic frequency band for W formation sandstones.
In this article, based on the acoustic measurements of core samples obtained from the low to medium porosity and permeability reservoirs in the WXS Depression, the densities and P and S wave velocities of these core samples were obtained. Then based on these data, a series of elastic parameters were computed. From the basic theory and previous pore fluid research results, we derived a new fluid identification factor (F). Using the relative variations, A g/w and A o/w , of the elastic parameters between gas and water saturated samples and between oil and water saturated samples, λρ, σ HSFIF , Kρ, λρ -2μρ, and F as quantitative indicators, we evaluate the sensitivity of the different fluid identification factors to identify reservoir fluids and validate the effects by crossplots. These confirm that the new fluid identification factor (F) is more sensitive for distinguishing oil and water than the traditional method and is more favorable for fliud identification in low to medium porosity and permeability reservoirs.
Based on the 1 • × 1 • depth map of the high conductive layers in upper mantle of China (1996), with the addition of magnetotelluric data, we created the new map of the high conductive layer in upper mantle of China. The depth of the upper mantle high conductive layers vary widely in China, the shallowest depth is about 50∼60 km, the deepest place is about 230 km, and the average depth can be about 100∼120 km. Through further study on the interface depth of high conductive layer in upper mantle as well as contrasting with the distribution of endogenous metallic minerals of the continent of China, we have found that most of the endogenous metallic ore deposits are located at the top of high conductive layer in upper mantle uplift or the top of the steep gradient zone of high conductive layer in upper mantle. Therefore, the depth and shape of the high-conductive layer in upper mantle can be very useful for the prediction of endogenous metal ores. We also found that the structures which have mirror relationship with the high conductive layer in upper mantle such as shallow basin and rift are the best area to detecting inorganic mantle reservoirs. The lithosphere are thinning and stretching while the uplift of high conductive layer in upper mantle, meanwhile a series of nearly parallel beam steep crack can be formed to become the passway for the mass upwelling of magma, liquid and gas from the deep mantle, finally all those materials from deep mantle are accumulated and become inorganic mantle reservoirs, gas reservoirs and endogenous metal ores under certain situations.
Traditional seismic AVO forward study usually uses constant parameters to construct rock physics models, and such parameters in real formations have uncertainties in an exploration area. This work employed laboratory measurements of cores from the target formation to simplify the rock physics model with relationships of reservoir sandstone porosities and P‐ and S‐wave impedances under a dry condition. To study influences of model parameter uncertainties, the probability density functions of key model parameters were introduced based on core measurements and well logging analysis. Using the Monte‐Carlo method and Gassmann fluid replacement technique, the AVO responses of models were obtained for saturation fluids as brine, oil and gas. The comparative analysis indicates that the velocity uncertainty of covering mud is the primary factor affecting the fluid cluster's distribution and deviation from water background trend on AVO intercept‐gradient crossplots, while the porosity uncertainty of sand is also significantly affecting but less than covering mud. Thus, the correct interpretation of AVO anomalies for real seismic data requires forward simulation on a probabilistic rock physics model to acquire the information on reservoir physical properties and fluids as much as possible.
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