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The exploitation of unconventional reservoirs goes hand in hand with the practice of hydraulic fracturing and, with an ever increasing demand in energy, this practice is set to experience significant growth in the coming years. Sophisticated analytic models are needed to accurately describe fluid flow in a hydraulic fracture and the problem has been approached from different directions in the past 3 decades -starting with the use of line-source functions for the infinite conductivity case, followed by the application of Laplace Transforms and the Boundary-Element Method for the finite-conductivity case. This topic remains an active area of research and, for the more complicated physical scenarios such as multiple transverse fractures in ultra-tight reservoirs, answers are presently being sought.Fractal theory has been successfully applied to pressure transient testing, albeit with an emphasis on the effects of natural fractures in pressure-rate behavior. In this work, we begin by performing a rigorous analytical and numerical study of the Fractal Diffusivity Equation and we show that it is more fundamental than the classic linear and radial diffusivity equations. Subsequently, we combine the Fractal Diffusivity Equation with the Trilinear Flow Model, culminating in a new semi-analytic solution for flow in a finite-conductivity vertical fracture which we name the "Fractal-Fracture Solution". This new solution is instantaneous and has an overall accuracy of 99.7%, thus making it comparable to the Trilinear Pseudoradial Solution for practical purposes. It may be used for pressure transient testing and reservoir characterization of hydrocarbon reservoirs being produced by a vertically fractured well. Additionally, this is the first time that fractal theory is used in fluid flow in porous media to address a problem not related to iv reservoir heterogeneity. Ultimately, this work is a demonstration of the untapped potential of fractal theory; our approach is very flexible and we believe that the same methodology may be extended to develop new reservoir flow solutions for pressing problems that the industry currently faces. v DEDICATIONThis thesis is dedicated to my parents, Fernando and Rosa. The effort that went into this thesis is a reflection of your unconditional love and support.In any field, find the strangest thing and then explore it.-John Archibald WheelerNo man should escape our universities without knowing how little he knows. ACKNOWLEDGEMENTSIt is difficult for me to imagine a more privileged set of circumstances under which to do one's graduate studies. The fact of the matter is that for the past 2 years I have been paid to learn from some of the brightest minds in petroleum engineering. I would like to thank Drs. Thomas A. Blasingame and George J. Moridis for believing in me, welcoming me in their research group and for funding my studies.Wherever my career takes me in the future, I will always hold them in the highest esteem.

As a result of the massive digitization of healthcare data, healthcare databases are expanding in size. Social determinants of health( SDoH) are data that describe the conditions under which people were born, lived, worked, developed, and aged. They have been incorporated into this expanding multidimensionality. The increase in SDoH variables paints a more accurate picture of the factors that may be affecting the patient's recovery during treatment. This article will look at various scenarios for collecting SDoH digitally as well as potential technologies in order to enable ubiquitous, continuous, and secure patient monitoring. The inclusion and importance of data for real-world evidence will also be covered, along with the various ways that digital SDoH can be useful allies to shed light on phenomena and processes like database bias, healthcare structural issues, health networks quality measurement, treatments outcome analysis and clinical trial design.

The field of digital pathology produces a large number of images associated with patient metadata that are the raw material of computational pathology. The process of making images available with adequate privacy and data protection considerations takes a long time. Given that the Ethereum network associated with InterPlanetary File System (IPFS) promotes the exchange of information in a secure, private and decentralized manner, this association could be an important partner between digital and computational pathology. Therefore, here we propose and discuss a prototype with the aforementioned parts and the addition of neural compression, as an essential information preservation step. This prototype could constitute a link for the exchange of information in a secure way, providing transparency and reliability to the chain and empowering the field of manufacturing artificial vision solutions for the medical field