Multiscale Formation Evaluation and Rock Types Identification in the Middle Bakken Formation
Authors:   University of Wyoming, Mellal Ilyas , Malki Mohamed Lamine ,   University of Wyoming, Latrach Abdeldjalil ,   University of Wyoming, Ameur-Zaimeche Ouafi ,   Université of Kasdi Merbah Ouargla, Bakelli Omar ,   University of North Dakota
Published: September 2024

Multiple challenges are associated when characterizing complex reservoirs using well logs. The Middle Bakken Member (MBM), as a tight and multimineral reservoir with a low-resistivity pay, presents several petrophysical challenges. The objective of the paper is to characterize the MBM rock properties from pore to log scale using thin sections, routine and special core analysis, conventional and advanced well logs, and machine-learning algorithms. For a detailed understanding of the MBM rock properties, we used a complex petrophysical workflow by integrating quad-combo logs along with advanced logs, including electron capture spectroscopy and nuclear magnetic resonance (NMR), using both deterministic and probabilistic methods. Bakken minerals and fluids volumes were estimated by combining quad-combo logs along with elemental dry-weight fractions and calibrated with the X-ray diffraction (XRD) and routine core analysis data. Porosity, clay-bound water, and movable fluids were estimated using NMR. Due to the low-resistivity reading in the reservoir and the inaccuracy of the water saturation and permeability estimated from well logs, thin sections from different depths of the MBM were analyzed to understand the pore types, cementing materials, and grain packing. Finally, machine-learning algorithms were applied in a total of 1,503 Dean-Stark water saturation samples using the triple-combo logs to estimate water saturation and generalize the results in the North Dakota portion of the MBM. We found that from the multimineral model of the evaluated wells that the MBM is composed of six main minerals, where quartz and dolomite are the main minerals of the reservoir. The model provided an accurate prediction of the minerals compared to the XRD analysis, except for some scattered XRD points, which are assumed to be due to the laminations that exist in the reservoir. Archie derivatives were tested in the Bakken using constant Archie parameters to estimate water saturation; the results showed a disagreement with the Dean-Stark water saturation. By analyzing thin sections, we found that three minerals act as a cementing material, and their volume varies along the reservoir, which makes the estimation of the cementing exponent constant along the reservoir interval a wrong approach. Machine-learning algorithms were employed to predict water saturation from the triple-combo logs. Several models were compared: linear regression, ridge regression, support vector machines, neural networks with backpropagation, and decision trees. For the comparison, both correlation coefficient and mean squared error were used. Decision trees proved to be the most reliable model, with an R2 of 0.87. The decision trees model gave almost perfect predictions, except for a few outliers that lowered the correlation coefficient. This relatively simple model proved to be a powerful tool for predicting the water saturation in the MBM. Machine-learning-based models proved to be a robust tool for water saturation prediction in the MBM and outperformed the classical physical and analytical models. Furthermore, the used decision-trees-based model is a white box model whose parameters can be extracted and interpreted.