Reliable characterization of subsurface soil in urban areas is a major concern in geotechnical and geological engineering projects. In this regard, this research deals with development of a 3D geological engineering model on Mashhad City soil using Sequential Gaussian Simulation (SGS) approach. The intense variability of soil in the study area has sometimes caused serious problems in civil engineering projects in the city. Therefore, a better understanding of these deposits is critical for current and future civil engineering work. The main objectives of this study were investigating the spatial variability of soil through variograms and then predicting the values of soil properties at unsampled locations using SGS method. In this study, some geotechnical index parameters including percentage of fine grained material, plasticity index, and liquid limit have been employed as input data. A database including the data of 1750 boreholes was built and the hard data were transformed into normal scores in order for them to be applicable as input data in SGS modeling. Maps related to the average of all realizations along with Coefficient of Variation (CV) were provided for each variable as well. Then the maps were interpreted according to Mashhad City sedimentary environment.

In the recent past, the applicability and uncertainty of pore-pressure prediction methods that work based on seismic signals have been serious challenges in carbonate reservoirs. In this context, enormous research has been conducted on the prediction of pore pressure in these rocks, some of which have ended with failure due to the complexity of carbonates. To address the main cause of such failures, we experimentally simulate the pore fluid in carbonates under reservoir conditions to interpret and classify the factors contributing to the pore pressure. The results are then built into a novel hybrid experimental model for evaluating the feasibility of pore pressure prediction — one that could bridge the gap between laboratory and empirical approaches. For this purpose, 34 core samples are collected from different geologic formations at different wells penetrating reservoir intervals. The mechanical properties of the samples are investigated by conducting experiments in a triaxial cell where pore pressure is applied under different regimes while simultaneously acquiring the ultrasonic response of the sample. Sensitivity analyses of pore pressure are carried out under drained and undrained conditions and the results are used to evaluate our methodology. Pore pressure coefficients of P- and S-wave velocities are introduced in two different conditions, namely constant differential stress and constant pore pressure, with the same dimension as compressibility. Results are interpreted statistically and effective parameters are identified from a pool of many variables; these include the static bulk modulus, Young’s modulus, Biot’s coefficients, porosity, permeability, and sedimentary textures (e.g., micritization, neomorphism, cementation, fracture, blocky calcite, and dissolution). A feasibility study template is further introduced for investigating pore pressure detectability based on commonly available data in carbonate reservoirs by incorporating the main controlling factors. Our template and findings of this research are important for pore pressure prediction before performing further research and implementing operation programs. They also can decrease the risk of underestimating the pore pressure, thereby improving the well planning performance and mitigating the drilling hazards.

In this paper, the correlation between shear wave velocity and some of the index parameters of soils including standard penetration test blow counts (SPT), fine-content (FC), soil moisture (W), liquid limit (LL) and depth (D) is investigated. The study attempts to show the application of artificial neural networks and multiple regression analysis in the prediction of the shear wave velocity (VS) value of soils. New predicting equations are suggested to correlate VS and mentioned parameters based on a dataset collected from Mashhad city in the north east of Iran. The results suggest that better and more exact correlations in the estimation of VS are acquired when ANN method is used. The predicted values using ANN method are checked against the real values of VS to evaluate the performance of this method. The minimum correlation coefficient obtained in ANN method is higher than the maximum correlation coefficient obtained from the MLR. In addition, the value of estimation error in the ANN method is much less than the MLR method indicating the higher confidence coefficient of the ANN in estimating the VS of soil.