A Study on the Coupling Relationship Between Pyrite Occurrence Morphologies in Shale and Resistivity, and the Quantitative Calculation Method of Its Content Based on Numerical Simulation

Pyrite is widely developed in shale formations with diverse occurrence morphologies and relatively low contents. However, it exerts a significant impact on the electrical properties of reservoirs and severely restricts the accuracy of reservoir evaluation. Based on computed tomography (CT) scanning technology, combined with mathematical morphology methods and finite element simulation techniques, this study focuses on the coupling relationship between pyrite occurrence states and reservoir resistivity, along with the quantitative calculation method of pyrite content. The results indicate that: (1) The influence of pyrite occurrence morphologies on the electrical conductivity of shale varies remarkably. Dispersed pyrite particles exert a weak interference on reservoir resistivity, and the rock-electrical relationship conforms to Archie's law. In contrast, massive and banded pyrite have a more prominent impact on resistivity, their conductive contribution exceeds that of pore water. This renders the traditional Archie's law inapplicable. Furthermore, under the condition of the same content, banded pyrite exerts the most significant influence on reservoir resistivity. (2) Three parameters, namely the Pyrite Resistivity Ratio (PRR), the Conductive Path Influence Characterization (VPYRZ), and the Pyrite Occurrence Index (PYI), are constructed to quantitatively describe the nonlinear influence of pyrite with different occurrence morphologies on reservoir resistivity. Combined with scanning electron microscopy (SEM) data, the PYI thresholds for different occurrence morphologies are determined as follows: dispersed pyrite (< 7.8623), massive pyrite (7.8632–16.986), and banded pyrite (>16.986). On this basis, a quantitative calculation model for pyrite content is established. (3) The verification results of actual well logs show that the calculated results of the proposed model are in high agreement with the formation element logging data, and the accuracy meets the practical requirements of reservoir evaluation.