I thank my advisors, Marcelo and Ruy, for all the support, ideas, and complete availability for meetings regarding this project or any other subject. To my friends from "Favelinha", this was only possible because we were all together! I thank CNPq, PUC-Rio and Vinci Partners for all the financial support. Finally, I thank ANBIMA for their financial support through the "XIII Prêmio ANBIMA de Mercado de Capitais".

Measurement While Drilling (MWD) is a technology for assessing rock mass conditions by collecting and analyzing data of mechanical drilling variables while the system operates. Nowadays, typical MWD systems rely on physical sensors directly installed on the drill rig. Sensors used in this context must be designed and conditioned for operating in harsh conditions, imposing trade-offs between the complexity, cost, and reliability of the measurement system. This paper presents a methodology for integrating physics-based observers into an MWD system as an alternative to complement or replace traditional physical sensors. The proposed observers leverage mathematical models of the drill’s electrical motor and its interaction with dynamic loads to estimate the bit speed and torque in a Down-the-Hole rig using current and voltage measurements taken from the motor power line. Experiments using data collected from four test samples with different rock strengths show a consistent correlation between the rate of penetration and specific energy derived from the observed drilling variables with the ones obtained from standardized tests of uniaxial compressive strength. The simplicity of the setup and results validate the feasibility of the proposed approach to be evaluated as an alternative to reduce the complexity and increase the reliability of MWD systems.

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.

In rotary-percussion drilling, the impact frequency is a crucial variable that is closely linked to operational factors that determine the efficacy of the drilling process, such as the rate of penetration, bit wear, and rock mass characteristics. Typical identification methods rely on complex simulation models or the analysis of different sensor signals installed on specially adapted setups, which are difficult to be implemented in the field. This paper presents a novel study where the impact frequency is identified by motor current signature analysis (MCSA) applied to an induction motor driving a DTH drilling setup. The analysis of the case study begins with the definition of characteristic drilling stages where the pressure and sound signals allow the detection of an impact frequency of 14.10 Hz, which is then used as a reference to validate three MCSA identification approaches. As a result of the analysis, the envelope approach is the most robust for nearly real-time implementations considering its simplicity and range of coverage. Experimental results provide evidence about the feasibility of the proposed MCSA methods to be integrated into Measurement-While-Drilling (MWD) systems to improve drilling condition monitoring and rock mass characterization.

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