Digital Core Modeling And Numerical Study Of Electrical Conductivity Of Shale Gas Reservoir Rock

As a huge potential unconventional energy resource, shale gas has been a solution ofthe shortage of energy supply. Resistivity well logging is one of the primary way of shalegas exploration and evaluation, while the conductive properties of the rock is thefoundation of resistivity well logging. Because shale reservoirs have the proerties of lowporosity and permeability, anisotropy and complicated structure and components, theconventional petrophysics experiments are difficult to be done. Numerical simulationbased on digital cores could be the new way to study shale reservoir petrophysicalproperties. Due to the difficulty of shale resistivity experiments, in this study thenumerical simulation study results were compared to resistivity well logging data.The digital core models of shale gas reservoirs were built and the conductivity of themodels were calculated by numerical method in this thesis. After selecting the appropriatecomponents, by using the improved MCMC method, the initial3D digital core models ofall components were reconstructed through a2D X-Ray energy spectrum slice image of ashale rock, and then initial3D digital cores of different components were combined intothe final model by using the nested combination method which was presented in this thesis.And the effect conductivity of the models were calculated by using FEM method.The final3D digital core model of shale reservoir rock had a reasonable scale andcomplete component information and it could be the foundation for the micro-scalenumerical simulation of shale reservoir rock restistivity property.By calculating the effective resistivity of the3D digital core model of shale reservoirrock, it was revealed that the conductivity properties of horizontal and vertical directionsin shale reservoirs had huge differences, and there were also some differences between thehorizontal directions. So the shale reservoir had highly anisotropic conductivitycharacteristics. Under conditions of different formation water salinity, the digital coremodel of shale reservoir rock had different formation factors, and the formation factorsand porosity didn’t show a liner relationship in the double logarithmic coordinates. SoArchie formula wasn’t appropriate in shale reservoirs. Through the approximate fit offormation factors and porosities, we can see that in shale reservoirs, the “a” was with greatvalue,“m” was with very small value, and they had huge difference with them inconventional reservoirs. Such results were related with the additional conductivity ofpyrite and layered clay mineral in shale reservoirs. By numerical simulation on models of different micro-component volume levels andconductivity characteristics, the results showed that the formation resistivity decreaseswith the increase of clay mineral content and the cation exchange capacity (CEC) of clay.The formation resistivity also decreases with the increase of pyrite content. The resultsshowed the formation resistivity was not sensitive to the solid organic matters but to thegas in the pores with the assumption that the solid organic matters are in the matrix but notin the pore space. So it can be deduced the solid organic matters and gas are both filled inthe pores and cause the resistivity of the shale reservoir rising.