High-Pressure, High-Temperature (HPHT) pressure gauges are commonly used in oil wells for pressure transient analysis. Mathematical models are used to relate input perturbation (e.g., flow rate transients) with output responses (e.g., pressure transients), and subsequently, solve an inverse problem that infers reservoir parameters. The indispensable use of pressure data in well testing motivates continued improvement in the accuracy (quality), sampling rate (quantity), and autonomy (lifetime) of pressure gauges. This body of work presents improvements in three areas of high-pressure, high-temperature quartz memory gauge technology: calibration accuracy, multi-tool signal alignment, and tool autonomy estimation. The discussion introduces the response surface methodology used to calibrate gauges, develops accuracy and autonomy estimates based on controlled tests, and where applicable, relies on field gauge drill stem test data to validate accuracy predictions. Specific contributions of this work include: - Application of the unpaired sample t-test, a first in quartz sensor calibration, which resulted in reduction of uncertainty in gauge metrology by a factor of 2.25, and an improvement in absolute and relative tool accuracies of 33% and 56%, accordingly. Greater accuracy yields more reliable data and a more sensitive characterization of well parameters. - Post-processing of measurements from 2+ tools using a dynamic time warp algorithm that mitigates gauge clock drifts. Where manual alignment methods account only for linear shifts, the dynamic algorithm elastically corrects nonlinear misalignments accumulated throughout a job with an accuracy that is limited only by the clock's time resolution. - Empirical modeling of tool autonomy based on gauge selection, battery pack, sampling mode, and average well temperature. A first of its kind, the model distills autonomy into two independent parameters, each a function of the same two orthogonal factors: battery power capacity and gauge current consumption as functions of sampling mode and well temperature -- a premise that, for 3+ gauge and battery models, reduces the design of future autonomy experiments by at least a factor of 1.5.

Le rythme impressionnant auquel les technologies de séquençage progressent est alimenté par leur promesse de révolutionner les soins de santé et la recherche biomédicale. Le séquençage par nanopores est devenu une technologie attrayante pour résoudre des lacunes des technologies précédentes, mais aussi pour élargir nos connaissances sur le transcriptome en générant des lectures longues qui simplifient l’assemblage et la détection de grandes variations structurelles. Au cours du processus de séquençage, les nanopores mesurent les signaux de courant électrique représentant les bases (A, C, G, T) qui se déplacent à travers chaque nanopore. Tous les nanopores produisent simultanément des signaux qui peuvent être analysés en temps réel et traduits en bases par le processus d’appel de bases. Malgré la réduction du coût de séquençage et la portabilité des séquenceurs, le taux d’erreur de l’appel de base entrave leur mise en oeuvre dans la recherche biomédicale. Le but de ce mémoire est de classifier des séquences d’ARNm individuelles en différents groupes d’isoformes via l’élucidation de motifs communs dans leur signal brut. Nous proposons d’utiliser l’algorithme de déformation temporelle dynamique (DTW) pour l’alignement de séquences combiné à la technologie nanopore afin de contourner directement le processus d’appel de base. Nous avons exploré de nouvelles stratégies pour démontrer l’impact de différents segments du signal sur la classification des signaux. Nous avons effectué des analyses comparatives pour suggérer des paramètres qui augmentent la performance de classification et orientent les analyses futures sur les données brutes du séquençage par nanopores. / The impressive rate at which sequencing technologies are progressing is fueled by their promise to revolutionize healthcare and biomedical research. Nanopore sequencing has become an attractive technology to address shortcomings of previous technologies, but also to expand our knowledge of the transcriptome by generating long reads that simplify assembly and detection of large structural variations. During the sequencing process, the nanopores measure electrical current signals representing the bases (A, C, G, T) moving through each nanopore. All nanopores simultaneously produce signals that can be analyzed in real time and translated into bases by the base calling process. Despite the reduction in sequencing cost and the portability of sequencers, the base call error rate hampers their implementation in biomedical research. The aim of this project is to classify individual mRNA sequences into different groups of isoforms through the elucidation of common motifs in their raw signal. We propose to use the dynamic time warping (DTW) algorithm for sequence alignment combined with nanopore technology to directly bypass the basic calling process. We explored new strategies to demonstrate the impact of different signal segments on signal classification. We performed comparative analyzes to suggest parameters that increase classification performance and guide future analyzes on raw nanopore sequencing data.