geostatspy

You can compute experimental variograms and perform 2D simple or ordinary kriging interpolation in Python using numpy and pandas datasets, applying optional search radii and data conditioning to generate spatial estimates on grids.

Sequential Gaussian simulation (SGSIM) generates multiple spatial realizations to quantify uncertainty in geological datasets, complementing kriging by providing probability distributions rather than single interpolated estimates for reserves assessment.

The framework supports 2D simple and ordinary kriging on grids with 3D extensions, enabling reservoir modeling workflows that integrate numpy arrays and pandas dataframes for porosity field characterization and spatial estimation.

Directional variogram computation and variogram modeling are supported alongside isotropic analysis, allowing you to fit models against field data and characterize spatial continuity across multiple azimuth directions in geological datasets.

Normal score transforms and back-transform utilities are included for robust statistics and data normalization, ensuring your spatial data meets distributional assumptions before variogram modeling, kriging, and SGSIM workflows.

Combine kriging interpolation with sequential Gaussian simulation and declustering to generate multiple realizations of porosity fields, enabling uncertainty quantification and probabilistic reserves assessment from spatial geological datasets.