Summary
Nanopore geometry and mineralogy are key parameters for effective hydrocarbon exploration and production in unconventional reservoirs. This study describes an approach to evaluate relationships between low-frequency complex resistivity spectra (CRS), nanopore geometry, and mineralogy to use CRS to provide estimates of reservoir parameters concerning hydrocarbon saturation, storage, and producibility.
For this purpose, the frequency dispersion of CRS was analyzed in 56 mudrock core plugs from the Vaca Muerta Formation (VMF) (Jurassic/Cretaceous) in Argentina, along with cementation factors (m), carbonate content (CO3), and total organic carbon (TOC). To quantify the nanoporosity, a subset of 23 samples was milled with broad ion beam (BIB) and imaged with scanning electron microscopy (SEM); the image grids of these samples were stitched together into high-resolution BIB-SEM mosaics and analyzed with digital image analysis (DIA) techniques.
Results show that porosity is the dominant control on electrical properties in the mudrocks analyzed as part of this study. There is no conclusive evidence that pore geometry influences the electrical properties in the analyzed mudrocks. Pore-geometry parameters [dominant pore size (DOMsize) and perimeter over area (PoA)] do not correlate with electrical properties. Instead, mineralogy shows a first-order correlation with electrical properties, where cementation exponents are higher in rocks with high TOC and low CO3 content.
CRS can be used to estimate porosity and cementation factors with high correlation coefficients of R2 = 0.71 and R2 = 0.95, respectively. Estimates of the 2D interfacial surface area (ISA2D), which is a function of both pore geometry and porosity, achieve an R2 = 0.59. The results of this study suggest that low-frequency dielectric rock properties, if measured downhole, could be useful to identify primary producing intervals in unconventional reservoirs, and to accurately determine cementation factors independent of formation fluids and porosity.
Permeability models largely rely on core measurements as input. The propagation of these models beyond the cored interval is often by use of the empirical porosity-permeability relationship. The problem is that porosity itself does not contain information about the pore geometry which controls permeability, hence these relationships carry high uncertainty in uncored intervals and nearby wells. Dielectric dispersion, on the other hand, is inherently linked to the pore geometry since it is sensitive to charge build up at the rock-fluid interfaces of the interconnected pore network through the Maxwell-Wagner effect. We aim to utilize this connection between pore geometry and dielectric dispersion to predict permeability using a core-data trained supervised machine learning model on dielectric dispersion wireline logging arrays. It builds upon a previous single-well study (Norbisrath, 2018) where the main concern was the repeatability in other wells, which is now addressed here. The study area is the Johan Sverdrup field on the Norwegian Continental Shelf. Data consists of core plug permeabilities and dielectric dispersion wireline logs from five wells. Capturing the dielectric frequency dispersion involves determining the slope of both attenuation and phase shift measurements made at different frequencies and transmitter-receiver spacings (feature engineering). The model will be trained on a subset of the core data (supervised machine learning), and subsequently propagated along the entire logged interval, as well as to the test well which was not part of the training set. Hyperparameter tuning will be used to optimize the model, and cross-validation used to prevent overfitting. Preliminary results show that the dielectric dispersion logging data contains enough information about the pore geometry to accurately describe and predict core plug permeabilities, not only in the same well but also in nearby wells that were not used in training the model. Correlation coefficients between estimated and predicted core permeability values are around R = 0.8. Given additional training input data and ground truthing in other wells, the described method could potentially reduce the need for coring when dielectric dispersion wireline logs are run. In the future we aim to explore the possibility of using dielectric dispersion data from LWD (Logging While Drilling) resistivity propagation tools as input for our permeability predictions. This would greatly enhance formation evaluation since these data are readily available in thousands of wells and are generally acquired in every new well. A model trained on a large amount of existing core data could enable real time permeability predictions from LWD tools.
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