SeReMpy: Seismic reservoir modeling Python library

Abstract

Seismic reservoir characterization is a subfield of geophysics that combines seismic and rock-physics modeling with mathematical inverse theory to predict reservoir variables from the measured seismic data. An open-source comprehensive modeling library that includes the main concepts and tools is still missing. We have developed a Python library called SeReMpy with state-of-the-art of seismic reservoir modeling for reservoir properties characterization using seismic and rock-physics models and Bayesian inverse theory. The most innovative component of the library is the Bayesian seismic and rock-physics inversion to predict the spatial distribution of petrophysical and elastic properties from seismic data. The inversion algorithms include Bayesian analytical solutions of the linear-Gaussian inverse problem and Markov chain Monte Carlo (MCMC) numerical methods for nonlinear problems. The library includes four modules: geostatistics, rock physics, facies, and inversion, as well as several scripts with illustrative examples and applications. We illustrate the use of the functions of the module and develop codes for practical inversion problems using synthetic and real data. The applications include a rock-physics model for the prediction of elastic properties and facies using well-log data, a geostatistical simulation of continuous and discrete properties using well logs, a geostatistical interpolation and simulation of 2D maps of temperature, an elastic inversion of partial stacked seismograms with Bayesian linearized amplitude-variation-with-offset inversion, a rock-physics inversion of partial stacked seismograms with MCMC methods, and a 2D seismic inversion.