Modeling source rock micro-structural and elastic changes during thermal maturation
Keywords: Geophysics.

I first present a mathematical model that predicts the microcrack growth and pore pressure change during the organic-rich source rock thermal maturation. This model is primarily based on elasticity and the principle of linear elastic fracture mechanics. By running the simulation under laboratory conditions, I obtain similar fracture surface areas to those measured by Kobchenko et al. (2014). Secondary porosity generated by crack opening is significant under laboratory conditions: the induced crack volume can accommodate several hundred times the initial pore volume. In contrast, under geological conditions, no significant secondary porosity is generated through crack opening. If the kerogen-to-hydrocarbon density ratio is sufficiently high, overpressure can be built up more quickly, and microcracks can propagate at earlier maturation stage. However, the extent of propagation and resulting apertures are much smaller than those generated under laboratory settings because of the overburden effect. Following the single crack model, I then introduce an improved rock physics modeling workflow using the effective medium theory to quantify the source rock elastic property evolution as the rock matures. The source rock system is divided into an inorganic background matrix containing minerals and water- or air-filled pores, and organics-filled inclusions. The differential effective medium algorithm is used twice in building the source rock system: it is first used to build an effective medium for the organic part, and then is used to build an effective medium for the source rock system. I make use of the experimental data, in particular the data of yields collected from the hydrous pyrolysis experiments to constrain the inputs to the rock physics models. The modeling results show that the thermal maturation first results in the reduction of the P- and S-wave moduli in the organic mixture; the most pronounced reduction occurs in the oil window corresponding to a vitrinite reflectance range of 0.6-0.8%. The sensitivity analysis further shows that good measurements of the elastic properties of the solid organic components (kerogen and bitumen) are more critical than those of the fluids for achieving accurate elastic constant predictions for the organic mixture. From the source rock system perspective, the thermal maturation results in enhanced anisotropy and reduced elastic stiffness. The organic inclusions with lower aspect ratios result in more significant reduction in the elastic stiffness, and more significant increase in the anisotropy. The elastic stiffness components of the source rock that have either the particle motion or the associated wave propagation direction parallel to the axis of the rotational symmetry of the inclusions are more sensitive to the elastic property change within the organic part. Last but not the least, I introduce an improved deep learning based framework to classify seismic facies from three-dimensional (3D) seismic volumes. By fine-tuning the convolutional neural network (CNN) hyper-parameters based off the "MalenoV" repository (Ildstad and Bormann, 2017), I obtain a better architecture named the modified LeNet-5. I also implement a "sparse sampling" scheme to pre-process the input data, which allows the input data size to be much smaller while maintaining a relatively large receptive field. The improved 3D CNN framework significantly improves the training and validation accuracy in the early training stage. Specifically, the combined use of the modified LeNet-5 and the sparse sampling scheme attains the best metrics in terms of the test accuracy. I conclude that the 3D CNN framework is very promising in making geologically reasonable and consistent predictions for seismic facies based on 3D seismic volumes.