Stochastic Nonlinear Ground Response Analysis Considering Existing Boreholes Locations by the Geostatistical Method

The field and laboratory evidence of nonlinear soil behavior, even at small strains, emphasizes the ‎importance of employing nonlinear methods in seismic ground response analysis. Additionally, ‎determination of dynamic characteristics of soil layers always includes some degree of uncertainty. Most of ‎previous stochastic studies of ground response analysis have focused only on uncertainties of soil ‎parameters, and the effect of soil sample location has been mostly ignored. This study attempts to couple ‎nonlinear time-domain ground response analysis with uncertainty of soil parameters considering existing ‎boreholes’ ‎location through a geostatistical method using a program written in MATLAB. To evaluate ‎the efficiency of the proposed method, stochastic seismic ground responses at construction location were compared with those of the non-stationary random ‎field method‎ through real site data. The ‎results demonstrate that applying the boreholes’ ‎location significantly affects not only the ground ‎responses but also their Coefficient Of Variation (COV). Furthermore, the mean value of the seismic ‎responses is affected more considerably by the values of soil parameters at the vicinity of the construction location. It is also inferred that considering boreholes’ location may reduce the COV of the seismic ‎responses. Among the surface responses in the studied site, the values of Peak Ground Displacement (PGD) ‎and Peak Ground Acceleration (PGA) reflect the highest and ‎lowest dispersion due to uncertainties of soil ‎properties through both non-stationary random field and geostatistical methods.

A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records

We present a Bayesian geostatistical model for mean annual runoff that incorporates simulations from a process-based hydrological model by treating the simulations as a covariate in the statistical model. The regression coefficient of the covariate is modeled as a spatial field such that the relationship between the covariate (simulations from a hydrological model) and the response variable (observed mean annual runoff) is allowed to vary within the study area. Hence, it is a spatially varying coefficient. A preprocessing step for including short records in the modeling is also suggested and we obtain a model that can exploit several data sources by using state of the art statistical methods. The geostatistical model is evaluated by predicting mean annual runoff for 1981–2010 for 127 catchments in Norway based on observations from 411 catchments. Simulations from the process-based HBV model on a 1 km × 1 km grid are used as input. We found that on average the proposed approach outperformed a purely process-based approach (HBV) when predicting runoff for ungauged and partially gauged catchments: The reduction in RMSE compared to the HBV model was 20 % for ungauged catchments and 58 % for partially gauged catchments, where the latter is due to the preprocessing step. For ungauged catchments the proposed framework also outperformed a purely geostatistical method with a 10 % reduction in RMSE compared to the geostatistical method. For partially gauged catchments however, purely geostatistical methods performed equally well or slightly better than the proposed combination approach. It is not surprising that purely geostatistical methods perform well in areas where we have data. In general, we expect the proposed approach to outperform geostatistics in areas where the data availability is low to moderate.

Estimating mean annual runoff by using a geostatistical spatially varying coefficient model that incorporates process-based simulations and short records

<p>In this work, we suggest a new framework for estimating mean annual runoff, which is a key water balance component.  The framework consists of two steps: 1) A process-based hydrological model is used to simulate mean annual runoff on a grid covering the whole study area. 2) Since the parameters of the process-based model are calibrated globally, there are local biases in the runoff estimates relative to the observed runoff. We therefore correct the gridded simulations based on runoff data. Here, step 2 is done by using a Bayesian geostatistical model that treats the process-based simulations as a covariate. The regression coefficient of the covariate is modelled as a spatial field such that the relationship between the covariate (simulations from the process-based model) and the response variable (the observed mean annual runoff) is allowed to vary within the study area. Hence, it is a spatially varying coefficient model. A preprocessing step for including short records in the modelling is also suggested such that we can exploit as much data as possible in the correction procedure. We use state of the art statistical methods such as SPDE and INLA to ensure fast Bayesian inference.</p><p> </p><p>The framework for estimating mean annual runoff is evaluated by predicting mean annual runoff for 1981-2010 for 127 catchments in Norway based on streamflow observations from 411 catchments. Simulations from the process-based HBV model on a 1 km x 1 km grid for the whole country are used as input. We found that on average the proposed approach outperformed a purely process-based approach (HBV) when predicting runoff for ungauged and partially gauged catchments: The reduction in RMSE compared to the HBV model was 20 % for ungauged catchments and 58 % for partially gauged catchments. For ungauged catchments the proposed framework also outperformed a purely geostatistical method with a 10 % reduction in RMSE compared to the geostatistical method. For partially gauged catchments however, purely geostatistical methods performed equally well or slightly better than the proposed two step procedure. In general, we expect the proposed approach to outperform purely geostatistical models in areas where the data availability is low to moderate.</p>

Constructing 3D Geological Model for Tertiary Reservoir in Khabaz Oil Field by using Petrel software.

3D Geological model for tertiary reservoir in khabaz oil field had been constructed byusing petrel software. Seven wells have been selected in this study in order to designPetrophysical properties (porosity, water saturation, and permeability). Structural modelcan be clarified tertiary reservoir in term of geological structures is a symmetrical smallanticline fold with four faults. Tertiary reservoir consist of six units are (Jeribe, UnitA,UnitA', UnitB, UnitBE, and UnitE). According to Petrophysical properties, layering hadbeen constructed for each tertiary units. Petrophysical model has been designed using thesequential Gaussian simulation algorithm as a geostatistical method. The results illustratesthat Unit B and Unit BE have the best petrophysical properties and the big amount of oil.