APPLICATIONS OF MACHINE LEARNING METHODS IN THE GENERATION OF SUBSURFACE MEASUREMENTS

Machine learning methods have been used in the Oil and Gas industry for about thirty years. Applications range from interpretations of geophysical, well and seismic responses, identification of minerals, analysis of rock samples and cores, fluid properties characterization, formation damage control, risk analysis, to well control (Alegre, 1991). In my thesis, I apply various machine learning methods for generating three well logs in shale formations, namely Nuclear Magnetic Resonance (NMR) T2 log, Dielectric Dispersion (DD) logs, and sonic travel time logs. NMR log acquired in geological formations contains information related to fluid-filled pore volume, fluid phase distribution, and fluid mobility. Raw NMR responses of the formation are inverted to generate the NMR T2 distribution responses in the geological formation, which is further processed to compute the effective porosity, permeability, bound fluid volume, and irreducible saturation of the formation under investigation. I developed two neural-network models that process conventional, easy-to-acquire logs to generate the in-situ NMR T2 distribution along 300-feet depth interval of a shale reservoir in Bakken Petroleum System (BPS). Following that, we generated DD logs. DD logs acquired in subsurface geological formations generally comprise conductivity (σ) and relative permittivity (ε_r) measurements at 4 discrete frequencies in the range of 10 MHz to 1 GHz. Acquisition of DD logs in subsurface formation is operationally challenging and requires hard-to-deploy infrastructure. I developed three supervised neural-network-based predictive methods to process conventional, easy-to-acquire subsurface logs for generating the 8 DD logs acquired at 4 frequencies. These predictive methods will improve reservoir characterization in the absence of DD logging tool. The predictive methods are tested in three wells intersecting organic-rich shale formations of Permian Basin (PB) and Bakken Shale (BS). Finally, we generated compressional and shear travel time logs (DTC and DTS, respectively) acquired using sonic logging tools. DTC and DTS logs are used to estimate connected porosity, bulk modulus, shear modulus, Young’s modulus, Poisson’s ratio, brittleness coefficient, and Biot’s constant for purposes of geomechanical characterization. Six shallow learning models, namely Ordinary Least Squares (OLS), Partial Least Squares (PLS), Least Absolute Shrinkage and Selection Operator (LASSO), ElasticNet, Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) models, suitable for function approximation problems, are trained and tested to predict DTC and DTS logs. 8481 observations along 4240-feet depth interval of a shale reservoir in Permian Basin (PB) are available for the proposed data-driven application. ANN model performs the best among the six models. Generation of NMR T2 is the computationally most challenging and we had the least amount for data from 220-feet depth interval that made the task even more challenging; nonetheless, we obtained prediction performance of 0.85 in terms of R2. On the other hand, the generation of dielectric permittivity and conductivity dispersion logs was slightly lower in terms of computational cost as compared to NMR T2 generation, we had data from 2200-feet depth interval, and prediction performance for this log generation task was 0.79 in terms of R2 in average. Generation of DTC and DTS logs is computationally easiest among the three tasks, we had data from 4240-feet depth interval, and the prediction performance was 0.86 in terms of R2 in average

Advances in Monitoring Dynamic Hydrologic Conditions in the Vadose Zone through Automated High-Resolution Ground-Penetrating Radar Images and Analysis

This body of research focuses on resolving physical and hydrological heterogeneities in the subsurface with ground-penetrating radar (GPR). Essentially, there are two facets of this research centered on the goal of improving the collective understanding of unsaturated flow processes: i) modifications to commercially available equipment to optimize hydrologic value of the data and ii) the development of novel methods for data interpretation and analysis in a hydrologic context given the increased hydrologic value of the data. Regarding modifications to equipment, automation of GPR data collection substantially enhances our ability to measure changes in the hydrologic state of the subsurface at high spatial and temporal resolution (Chapter 1). Additionally, automated collection shows promise for quick high-resolution mapping of dangerous subsurface targets, like unexploded ordinance, that may have alternate signals depending on the hydrologic environment (Chapter 5). Regarding novel methods for data inversion, dispersive GPR data collected during infiltration can constrain important information about the local 1D distribution of water in waveguide layers (Chapters 2 and 3), however, more data is required for reliably analyzing complicated patterns produced by the wetting of the soil. In this regard, data collected in 2D and 3D geometries can further illustrate evidence of heterogeneous flow, while maintaining the content for resolving wave velocities and therefore, water content. This enables the use of algorithms like reflection tomography, which show the ability of the GPR data to independently resolve water content distribution in homogeneous soils (Chapter 5). In conclusion, automation enables the non-invasive study of highly dynamic hydrologic processes by providing the high resolution data required to interpret and resolve spatial and temporal wetting patterns associated with heterogeneous flow. By automating the data collection, it also allows for the novel application of established GPR data algorithms to new hydrogeophysical problems. This allows us to collect and invert GPR data in a way that has the potential to separate the geophysical data inversion from our ideas about the subsurface; a way to remove ancillary information, e.g. prior information or parameter constraints, from the geophysical inversion process