Application of Multifractal Theory for Determination of Fluid Movability of Coal-Measure Sedimentary Rocks Using Nuclear Magnetic Resonance (NMR)

A precise evaluation of the fluid movability of coal sedimentary rock is crucial to the effective and secure utilization of coal measures gas reserves. Furthermore, its complex pore structure and diverse mineral components impact the flow properties of fluids in pore structures, causing accurate evaluation of fluid mobility to be extremely challenging. Nuclear magnetic resonance (NMR) technology is currently a prevalent technique to assess unconventional reservoirs due to its capacity to acquire abundant reservoir physical property data and determine fluid details. The free-fluid volume index (FFI) is a crucial factor in assessing fluid movability in the application of NMR technology, which can only be derived through intricate NMR saturation and centrifugation experiments This research utilized nuclear magnetic resonance (NMR) tests on 13 classic coal-measure sedimentary rock samples of three lithologies to reveal the FFI value. Moreover, the association between mineral components, pore structure parameters, and FFI was then extensively analyzed, and a prediction model for FFI was constructed. The results indicate that the T-2 spectra of sandstone and shale own a bimodal distribution, with the principal point between 0.1 and 10 ms and the secondary peak between 10 and 100 ms. The majority of the T-2 spectra of mudstone samples provide a unimodal distribution, with the main peak distribution range spanning between 0.1 and 10 ms, demonstrating that the most of the experimental samples are micropores and transition pores. The calculated results of the FFI range from 7.65% to 18.36%, and depict evident multifractal properties. Porosity, the content of kaolinite, multifractal dimension (D-q), and the FFI are linearly positively correlated. In contrast, the content of chlorite, illite, multifractal dimension subtraction (D-min - D-max), multifractal dimension proportion (D-min/D-max), and singularity strength (& UDelta;& alpha;) possess a negative linear correlation with the FFI, which can be further used for modeling. On the basis of the aforementioned influencing factors and the FFI experimental values of eight core samples, an FFI prediction model was constructed through multiple linear regression analysis. The accuracy of the prediction model was validated by utilizing this approach to five samples not included in the model development. It was revealed that the prediction model produced accurate predictions, and the research findings may serve as a guide for the classification and estimation of fluid types in coal reservoirs.