The Kribi-Campo sub-basin is the northernmost of a series of Aptian basins along the coast of West Africa. These extensional basins developed as a result of the northward progressive rifting of South America from West Africa, initiated c. 130 Ma ago. Post-rift sediments of the Kribi-Campo sub -basin contain several regional unconformities and changes in basin-fill architecture that record regional tectonic events. The tectono-stratigraphic evolution and plumbing system has been investigated using a high-quality 3D seismic reflection dataset acquired to image the deep-water Cretaceous-to-Present-day post-rift sediments. The study area is located c. 40 km offshore Cameroon in 600 to 2000 m present-day water depth, with full 3D seismic coverage of 1500 km2, extending down to 6.5 seconds Two-Way Travel time. In the late Cretaceous the basin developed as a result of tectonism related to movement of the Kribi Fracture Zone (KFZ), which reactivated in the late Albian and early Senonian. This led to inversion of the early syn-rift section overlying the KFZ to the southeast. Two main fault-sets - N30 and N120 - developed in the center and south of the basin. These normal faults propagated from the syn-rift sequences: the N120 faults die out in the early post-rift sequence (Albian time) whilst N30 faults tend to be associated with the development of a number of fault-related folds in the late Cretaceous post-rift sequence, and have a significant control on later deposition. The basin is filled by Upper Cretaceous to Recent sediments that onlap the margin. Seismic facies analysis and correlation to analogue sections suggest the fill is predominantly fine-grained sediments. The interval also contains discrete large scale channels and fans whose location and geometry were controlled by the KFZ and fault-related folds. These are interpreted to contain coarser clastics. Subsequently, during the Cenozoic, the basin experienced several tectonic events caused by reactivation of the KFZ. During the Cenozoic, deposition was characterized by Mass Transport Complexes (MTCs), polygonal faulting, channels, fans and fan-lobes, and aggradational gullies. The main sediment feeder systems were, at various times, from the east, southeast and northeast. The plumbing system shows the effects of an interplay of stratigraphic and structural elements that control fluid flow in the subsurface. Evidence for effective fluid migration includes the occurrence of widespread gas-hydrate-related Bottom Simulating Reflections (BSRs) 104 - 250 m below the seabed (covering an area of c. 350 km2, in water depths of 940 m - 1750 m), pipes and pockmarks. Focused fluid flow pathways have been mapped and observed to root from two fan-lobe systems in the Mid-Miocene and Pliocene stratigraphic intervals. They terminate near, or on, the modern seafloor. It is interpreted that overpressure occurred following hydrocarbon generation, either sourced from biogenic degradation of shallow organic rich mudstone, or from effective migration from a thermally mature source rock at depth. This latter supports the possibility also of hydrocarbon charged reservoirs at depth. Theoretical thermal and pressure conditions for gas hydrate stability provide an opportunity to estimate the shallow geothermal gradient. Variations in the BSR indicate an active plumbing system and local thermal gradient anomalies are detected within gullies and along vertically stacked channels or pipes. The shallow subsurface thermal gradient is calculated to be 0.052 oC m-1. With future drilling planned in the basin, this study also documents potential drilling hazards in the form of shallow gas and possible remobilised sands linked with interconnected and steeply dipping sand bodies.

Gas hydrate is a potential energy source that has recently been the subject of much academic and industrial research. The search for deep-water gas hydrate involves many challenges that are especially apparent in the northwestern Gulf of Mexico, where the sub-seafloor is a complex structure of shallow salt diapirs and sheets underlying heavily deformed shallow sediments and surrounding diverse minibasins. Here, we consider the effect these structural factors have on gas hydrate occurrence in Garden Banks and Keathley Canyon blocks of the Gulf of Mexico. This was accomplished by first mapping the salt and shallow deformation structures throughout the region using a 2D grid of seismic reflection data. In addition, major deep-rooted faults and shallow-rooted faults were mapped throughout the area. A shallow sediment deformation map was generated that defined areas of significant faulting. We then quantified the thermal impact of shallow salt to better estimate the gas hydrate stability zone (GHSZ) thickness. The predicted base of the GHSZ was compared to the seismic data, which showed evidence for bottom simulating reflectors and gas chimneys. These BSRs and gas chimneys were used to ground-truth the calculated depth of the base of GHSZ. Finally, the calculated GHSZ thickness was used to estimate the volume of the gas hydrate reservoir in the area after determining the most reasonable gas hydrate concentrations in sediments within the GHSZ. An estimate of 5.5 trillion cubic meters of pure hydrate methane in Garden Banks and Keathley Canyon was obtained.

In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contributions of this thesis. First, we present a thorough analysis of using state-of-the-art machine learning models to predict a subsurface geothermal parameter, temperature-at-depth, using a rich geo-spatial dataset across the Appalachian Basin. Specifically, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree-based models (e.g., XGBoost and Random Forest). We found that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. We also ran our model on a fine spatial grid to provide 2D continuous temperature maps at three different depths using the XGBoost model, which can be used to locate prospective geothermally active regions. Second, we develop a physics-guided machine learning model for predicting subsurface temperatures that not only uses surface temperature, thermal conductivity coefficient, and depth as input parameters, but also the heat-flux parameter that is known to be a potent indicator of temperature-at-depth values according to physics knowledge of geothermal energy problems. Since, there is no independent easy-to-use method for observing heat-flux directly or inferring it from other observed variables. We develop an innovative approach to take into account heat-flux parameters through a physics-guided clustering-regression model. Specifically, the bottom-hole temperature data is initially clustered into multiple groups based on the heat-flux parameter using Gaussian mixture model (GMM). This is followed by training neural network regression models using the data within each constant heat-flux region. Finally, a KNN classifier is trained for cluster membership prediction. Our preliminary results indicate that our proposed approach results in lower errors as the number of clusters increases because the heat-flux parameter is indirectly accounted for in the machine learning model. / Master of Science / Machine learning and artificial intelligence have transformed many research fields and industries. In this thesis, we investigate the applicability of machine learning and data-driven approaches in the field of geothermal energy exploration. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models, we show that machine learning can provide viable alternative solutions for geothermal energy mapping. First, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree based models (e.g., XGBoost and Random Forest). We find that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. Accuracy measures show that machine learning models are at par with physics-based models and can even outperform the thermal conductivity model. Second, we incorporate the thermal conductivity theory with machine learning and propose an innovative clustering-regression approach in the emerging area of physics-guided machine learning that results in a smaller error than black-box machine learning methods.

Cette thèse cible l'étude de la structure thermique de la croûte supérieure (<10km) dans les arcs magmatiques continentaux, et son influence sur l'enregistrement thermochronologique de leur exhumation et de leur évolution topographique. Nous portons notre regard sur deux chaînes de montagne appartenant aux Cordillères Américaines : Les Cascades Nord (USA) et la zone de faille Motagua (Guatemala). L'approche utilisée est axée sur l'utilisation de la thermochronologie (U-Th-Sm)/He sur apatite et zircon, couplée avec la modélisation numérique de la structure thermique de la croûte. Nous mettons en évidence la variabilité à la fois spatiale et temporelle du gradient géothermique, et attirons l'attention du lecteur sur l'importance de prendre en compte la multitude des processus géologiques perturbant la structure thermique dans les chaînes de type cordillère, c'est à dire formées lors de la subduction océanique sous un continent. / This thesis focuses on the influence of the dynamic thermal structure of the upper crust (<10km) on the thermochronologic record of the exhumational and topographic history of magmatic continental arcs. Two mountain belts from the American Cordillera are studied: the North Cascades (USA) and the Motagua fault zone (Guatemala). I use a combined approach coupling apatite and zircon (U-Th-Sm)/He thermochronology and thermo-kinematic numerical modelling. This study highlights the temporal and spatial variability of the geothermal gradient and the importance to take into account the different geological processes that perturb the thermal structure of Cordilleran-type mountain belts (i.e. mountain belts related to oceanic subduction underneath a continent).