SeReMpy: Seismic Reservoir Modeling Python library

Seismic reservoir characterization is a subfield of geophysics that combines seismic and rock physics modeling with mathematical inverse theory to predict the reservoir variables from the measured seismic data. An open-source comprehensive modeling library that includes the main concepts and tools is still missing. We present a Python library named SeReMpy with the 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 non-linear problems. The library includes four modules: geostatistics, rock physics, facies, and inversion, as well as several scripts with illustrative examples and applications. We present a detailed description of the scripts that illustrate the use of the functions of module and describe how to apply the codes to 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 two-dimensional maps of temperature, an elastic inversion of partial stacked seismograms with Bayesian linearized AVO inversion, a rock physics inversion of partial stacked seismograms with McMC methods, and a two-dimensional seismic inversion.

About the Inverse Theory and the Non-Unicity of the Film Thickness: Novel Approach Generating Equivalent Micro-Grooves and Roughness

Since the 1960s, all studies have assumed that a film thickness “h” provides a unique pressure field “p” by resolving the Reynolds equation. However, it is relevant to investigate the film thickness unicity under a given hydrodynamic pressure within the inverse theory. This paper presents a new approach to deduce from an initial film thickness a widespread number of thicknesses providing the same hydrodynamic pressure under a specific condition of gradient pressure. For this purpose, three steps were presented: 1) computing the hydrodynamic pressure from an initial film thickness by resolving the Reynolds equation with Gümbel’s cavitation model, 2) using a new algorithm to generate a second film thickness, 3) comparing and validating the hydrodynamic pressure produced by both thicknesses with the modified Reynolds equation. Throughout three surface finishes: the macro-shaped, micro-textured, and rough surfaces, it has been demonstrated that under a specific hydrodynamic pressure gradient, several film thicknesses could generate the same pressure field with a slight difference by considering cavitation. Besides, this paper confirms also that different ratios of the averaged film thickness by the root mean square (RMS) similar hydrodynamic pressure could be generated, thereby the deficiency of this ratio to define the lubrication regime as commonly known with Patir and Cheng theory.