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The net-effective stress is a fundamental physical property that undergoes dynamic changes in response to variations in pore pressure during production and injection activities. Petrophysical properties, including porosity, permeability, and wave velocities, play a critical role and exhibit strong dependence on the mechanical stress state of the formation. The Williston basin's Bakken Formation represents a significant reservoir of hydrocarbons within the United States. To investigate this formation, we extracted core plugs from three distinct Bakken members, namely Upper Bakken, Middle Bakken, and Lower Bakken. Subsequently, we conducted a series of measurements of ultrasonic compressional and shear wave velocities, as well as pulse decay permeabilities using nitrogen, under various confining pressures employing the Autolab-1500 apparatus. Our experimental observations revealed that the ultrasonic wave velocities and permeability display a significant sensitivity to stress changes. We investigated existing empirical relationships on velocity-effective stress, compressional-shear wave velocities, and permeability-effective stress, and proposed the best models and associated fitting parameters applicable to the current datasets. In conjunction with the acquired datasets, these models have considerable potential for use in time-lapse seismic monitoring and the study of production decline behavior. The best fitting models can be used to forecast the petrophysical and geomechanical property changes as the reservoir pore pressure is depleted due to the production, which is critical to the production forecast for unconventional reservoirs. [ABSTRACT FROM AUTHOR]

Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.

Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance. [ABSTRACT FROM AUTHOR]

The Williston Basin of central North America contains prodigious quantities of coal and oil. Could the antediluvian biosphere have provided this carbon mass, or did it require many millions of years to accumulate? In this concluding part, we focus on fossil fuels. As we have seen many times in many places, what appear to be strong arguments against the Biblical view of Earth history turn out to be strong arguments against uniformitarianism. [ABSTRACT FROM AUTHOR]

Early development of the Bakken Petroleum System recognized the significance of source rock maturation and the onset of oil generation in determining the geographic limits of production. The process of oil generation kinetics within source rocks involves intricate chemical reactions, where the nature of organic matter and thermal history influence the production of petroleum products. While simple models work well for some kerogen types (Type I), they fail for others (Type II and III), revealing the limitations of assuming invariant kinetic properties. This study explores the complexity of oil generation kinetics and its significance, particularly in the context of the Bakken Formation. Various methods have been used to identify the boundaries of oil sources, yet many are inductive. This research introduces a novel method to distinguish between catagenesis and diagenesis in Bakken's organic matter. A common linear relationship between activation energy (Ea) and frequency factor (ln(A)) appears in kinetic studies of source rocks, often attributed to statistical errors. This study investigates these relationships, revealing non-statistical compensation effects. Furthermore, extended kinetic models uncover a range of behaviors, highlighting the complexity of oil generation pathways. Notably, a kinetic compensation effect (KCE) is observed in the Bakken Formation, closely resembling findings for Type II kerogen. These compensation effects are not statistically forced, emphasizing their physiochemical origins. This research sheds light on the diverse KCEs within the Bakken Formation through a combination of experimental maturation, core analyses, and kinetic modeling. The study suggests that provenance, particularly for Type II kerogen, plays a crucial role in these effects. The data reveal multiple KCE patterns, including linear, inclined arch, and random clusters, with some transitioning between patterns during the oil generation process. These findings underscore the complexity of oil generation kinetics and its dependency on organic provenance. They also provide valuable insights for understanding oil reservoirs in the Bakken Formation and beyond. • This study explores oil generation kinetics in the Bakken Formation. • The study details the presence of a non-statistical kinetic compensation effect. • Extended kinetics methods are applied. • The data reveal multiple KCE patterns of experimental and naturally matured samples. [ABSTRACT FROM AUTHOR]

In this study, non-isothermal thermogravimetric analysis was conducted on three samples taken from the Bakken Formation at one heating rate (10 °C/min). Based on the TGA thermograms, various intervals of weight loss were identified. The differences in weight loss, peak positions, and shapes of the samples' thermograms were attributed to the variations in the composition (quantities of silicates and clay minerals), TOC content, and thermal maturity of the kerogen. Moreover, it was observed that the sample with the highest thermal maturity had the highest number of aromatic rings in its structure. XRD analysis demonstrated that the sample with the lowest silicate concentration and the highest clay mineral content lost the most weight during TGA analysis. Furthermore, CHNS elemental analysis indicated that samples with higher thermal maturity levels have higher amounts of C and H elements and lower content of N and S heteroatoms. Ultimately, our results were corroborated by HRTEM (High Resolution Transmission Electron Microscopy) analysis of p-kerogens, thus confirming the abundance of aromatic rings in the samples and delineating the relationship between aromaticity and thermal maturity. • Non-isothermal TGA was conducted on the Bakken samples. • Various intervals of weight loss and peaks, referring to the composition of TOC, and thermal maturity were identified. • HRTEM and CHNS, confirmed abundance of aromatics showing the relationship between aromaticity and maturity as revealed by TGA. [ABSTRACT FROM AUTHOR]