Abstract:
A method of calculating the temperature and/or porosity of a geological structure, wherein there is provided at least two geophysical parameters of the geological structure, the method including inverting the at least two geophysical parameters to estimate the temperature and/or porosity of the geological structure.
Abstract:
A method can include acquiring imagery of an exposed surface of the Earth; generating a multi-dimensional model based at least in part on the imagery; generating synthetic seismic data utilizing the multi-dimensional model; acquiring seismic data of a subsurface region of the Earth; performing a search that matches a portion of the acquired seismic data and a portion of the synthetic seismic data; and characterizing the subsurface region of the Earth based at least in part on the portion of the synthetic seismic data.
Abstract:
A method and system are provided for acquiring the probability of slope failure and destabilization caused by an earthquake. For example, the method includes performing azimuth division in an area around a site at which a slope is located as a center, pre-setting a seismic acceleration threshold value that varies within a certain range, and calculating an exceeding probability that the seismic acceleration of the slope site generated by an earthquake in each azimuth domain is greater than or equal to the seismic acceleration threshold value, to establish an exceeding probability curve of site seismic acceleration corresponding to each azimuth domain. The method and system achieve estimation of the probability of slope destabilization caused by an earthquake by comprehensively considering the uncertainty of the seismic action and the uncertainty of slope failure and destabilization.
Abstract:
A system and method for analyzing geologic features including fluid estimation and lithology discrimination may include the steps of identifying areas of interest on a seismic horizon, computing statistical data ranges for the seismic amplitudes within the areas of interest, and analyzing the geologic features based on the amplitude variation with offset (AVO) or angle (AVA) curves including the statistical data ranges.
Abstract:
A method of processing geological data is provided for input to a geostatistical modelling algorithm to predict a value for a parameter relating to a physical property of the Earth. An input data set corresponding to a measured geological parameter is processed to determine a characteristic function of the input data with respect to a geological measure. The input data is transformed to reduce spatial bias with respect to the geological distance measure by applying an inverse function. A statistical weighting is calculated for the transformation and the transformation and weighting are used to predict a representative value of the physical property corresponding to the measured geological parameter. A data processing apparatus and computer program product are also provided.
Abstract:
A method of estimating a velocity of a geological layer includes a. providing a first, initial model including an interval velocity associated with a subsurface location and an uncertainty associated with the interval velocity; b. providing data including an actual or approximated root-mean-square (RMS) velocity associated with a subsurface location and an uncertainty associated with the RMS velocity; and c. estimating a second model including an interval velocity associated with a subsurface location and an uncertainty associated with the interval velocity, based on the interval velocity and the uncertainty of the first model, and the RMS velocity and the uncertainty of the data.
Abstract:
A system for and computer implemented method for analysis of data representative of subsurface properties of a subsurface region. The method includes transforming the data representative of subsurface properties of the subsurface region into transformed data in accordance with a selected criterion. A three dimensional window geometry to be applied to the transformed data is selected, based, at least in part, on expected feature sizes present, data sampling density and a size of the subsurface region. A plurality of values for a three dimensional lacunarity statistic are calculated by applying the selected three dimensional window geometry to randomly selected regions of the subsurface region, and correlating the calculated values to the subsurface properties of the subsurface region.
Abstract:
A basically time-domain method for performing full wavefield inversion of seismic data to infer a subsurface physical property model (61), where however at least one quantity required for the inversion, such as the Hessian of the cost function, is computed in the frequency domain (64). The frequency-domain quantity or quantities may be obtained at only a few discrete frequencies (62), preferably low frequencies, and may be computed on a coarse spatial grid, thus saving computing time with minimal loss in accuracy. For example, the simulations of predicted data and the broadband gradient of the objective function may be computed in the time domain (67), and the Hessian matrix, approximated by its diagonal, may be computed in the frequency domain. It may be preferable to use time-domain and the frequency-domain solvers that employ different numerical schemes, such as finite-difference method, one-way wave equation, finite-element method (63).
Abstract:
The invention is a method using a computer to construct a grid representative of the distribution of a physical property of an underground formation having application to petroleum reservoir development. A set of initial grid cells MI containing at least one conditioning point PC is defined. Each cell MI is then visited and assigned a value representative of the property using a filling method. At least one unvisited cell (MNV1, MNV2) adjacent to at least one already visited cell MV is then identified, and the number N of adjacent cells MV is determined for each cell (MNV1, MNV2). Each cell (MNV1, MNV2) is then visited and filled according to the decreasing values of N. The stages of identification and filling of cells (MNV1, MNV2) are repeated until each cell of the grid has been visited.