VictorJuiz/thesis huggingface dataset:
Interpreting drilling records to predict well success

For the last decade, modern machine learning, especially the deep neural network, has made tremendous impacts to all facets of life. The energy industry is not oblivious to the trend, and machine learning has found its applications in all of kind of operations. Often, the most cost-intensive operation that is carried out in the energy industry is the well drilling operation. This report is an extensive study on the application of deep neural networks in optimizing geothermal well drilling operation, but can be readily applied to oil and gas well due to their similarities.
This study shows that drilling optimization by optimizing rate-of-penetration from past drilling records is a highly delicate process. Not only a sufficient amount of data from multiple sources of information is required, the data also must be properly recorded and standardized, or it must go through preprocessing to filter out invalid entries and to standardize. In this study, a R2 score of 0.53 has been achieved for the rate-of-penetration prediction deep neural network model with random train/validation splitting scheme. However, as the rate-of-penetration prediction model trained with random splitting scheme has limited usage in production, an alternative splitting scheme called well-by-well train/validation splitting is used. With the well-by-well train/validation splitting scheme, the rate-of-penetration prediction deep neural network model was only able to achieve a R2 score of 0.1
Rate-of-penetration is not the only criterion used in drilling optimization. This study also considered optimization by reducing nonproductive times. The most common reasons for nonproductive times are tripping, and the drillers encountering problems. Therefore, having a model that can forecast these nonproductive times is helpful to the overall quest of minimizing the total costs. In this study, machine learning models that can predict potential tripping/problems from drilling records have been developed, and both models have satisfactory accuracy to be used in real life situations.
The problem of incorporating nonnumerical entries in drilling records was also studied in this study. In addition to the standard numerical-type entries, textural-type entries are often found in drilling records. These textural-type entries are often nonstandardized written English remarks/comments from the drillers, which may carry useful information about the condition of the well that does not available elsewhere in the records. However, these remarks/comments require expensive and time-consuming manual data preprocessing in order to incorporate them into machine learning models. Bidirectional Encoder Representations from Transformers (BERT) provides a solution for automating preprocessing of textural data. Using textual information, together with numerical information, has improved the quality of predictions in most cases.

Keywords: Oil well drilling
Geothermal well drilling
Machine learning
Neural networks (Computer science)
Natural language processing
Petroleum engineers
Geothermal engineering
Cluster analysis
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Thesis
Interrogating a new HIV-1 vaccine target in the gp41 C-heptad repeat

HIV-1 infection requires viral and cell membrane fusion, a process which relies on the gp41 N-heptad repeat (NHR) and C-heptad repeat (CHR) of the viral glycoprotein Env. When the CHR adopts an α‑helical conformation, one face is composed of highly conserved residues and is a validated target for HIV-1 inhibition, suggesting that this conserved α-helical face might be an effective vaccine target. No neutralizing antibodies targeting the highly conserved α-helical face of the CHR have been described, although an engineered protein targeting this epitope, 5-Helix, shows potent neutralizing activity against multiple viral strains. We have identified the first such examples of neutralizing antibodies against this epitope through parallel antibody selections, an important proof-of-principle for CHR-targeting vaccine efforts. First, we use mutagenesis and selection of a yeast surface-display library to isolate two novel antibodies that target this epitope, A24.3 and A25.2. Surprisingly, the X-ray crystal structure of the A24.3 antigen-binding fragment (Fab) indicates that CHR binding is concomitant with displacement of the variable light domain and is mediated by germline-conserved framework residues on the variable heavy (VH) domain. We next designed a stapled CHR peptide to identify novel antibodies targeting this highly conserved epitope from a ~7.6 x 10^10-member single-chain variable fragment (scFv) library designed by Distributed Bio: the SuperHuman Library 2.0. We identified 52 novel anti-CHR scFv clones, many of which bind multiple CHR peptides representing the consensus sequences of distinct clades of HIV-1. In order to functionally evaluate these novel antibodies, we assessed their viral neutralization activity using Env-pseudotyped lentivirus. We first assessed the efficacy of 5-Helix against a panel of HIV-1 strains representing circulating strains worldwide and found 5-Helix shows broad neutralization activity across viral clades and neutralization tiers. We next found both A2 antibodies and Distributed Bio scFvs neutralize the tier-1 HXB2 strain, the first examples of neutralizing antibodies targeting this conserved epitope in the CHR. Additionally, A25.2 VH antibody shows cross-clade neutralization, inhibiting HIV-1 strains representing diverse CHR sequences. Taken together, these neutralization studies combined with our biochemical and structural characterization provide proof-of-concept that the conserved α-helical face of the CHR could be effectively targeted by antibodies, potentially opening new avenues in vaccine development for HIV-1 and viruses with closely related class-I fusion mechanisms

Keywords: Molecular and Cellular Physiology.
Advisor: Kim Peter
Brünger Axel T.
Jardetzky Theodore
Yeh Ellen
Interspecies organogenesis

Advances in stem cell technology and tissue engineering promise clinical applications. Although much progress is being made, developing whole organs from in vitro cultured cells proves difficult. The spatial and temporal intricacies that occur during organogenesis ensure adequate perfusion, organ size, tissue distribution and function. Since many developmental properties are not cell intrinsic, proper development requires rapidly evolving extrinsic cues that may act locally or in gradients. This environment is difficult to model in vitro. To generate fully functional organs from patient-specific induced pluripotent stem cells (iPSCs), in vivo approaches have been attempted in which an animal is used as a bioreactor. In this approach, development of a target organ is genetically disabled in a host embryo, and injected donor cells (often from a different species) are coaxed into generating the target organ by the surrounding host cells. Although this interspecies developmental niche is likely more natural than an in vitro model, the in vivo approach to organogenesis comes with its own challenges

Keywords: Stem Cell Biology and Regenerative Medicine.
Advisor: Nakauchi Hiromitsu
Red-Horse Kristy
Sebastiano Vittorio
Invariant inference via quantified separation

Quantified first-order formulas, often with quantifier alternations, are increasingly used in the verification of complex systems. While automated theorem provers for first-order logic are becoming more robust, until recently invariant inference tools that handle quantifiers were restricted to purely universal formulas. We define and analyze first-order quantified separators and their application to inferring quantified invariants with alternations. A separator for a given set of positively and negatively labeled structures is a formula that is true on positive structures and false on negative structures. We investigate the problem of finding a separator from the class of formulas in prenex normal form with a bounded number of quantifiers and show this problem is NP-complete by reduction to and from SAT. Based on this computational primitive, we present a new PDR/IC3-based algorithm for finding inductive invariants with quantifier alternations. We tackle scalability issues that arise due to the large search space of quantified invariants by combining a breadth-first search strategy and a new syntactic form for quantifier-free bodies. The breadth-first strategy prevents inductive generalization from getting stuck in regions of the search space that are expensive to search and focuses instead on lemmas that are easy to discover. The new syntactic form is well-suited to lemmas with quantifier alternations by allowing both limited conjunction and disjunction in the quantifier-free body, while carefully controlling the size of the search space. We evaluate both separation itself and our combined invariant inference algorithm on a benchmark of primarily distributed protocols, including complex Paxos variants. We demonstrate separation can be solved in practice, and that our inference algorithm can solve more of the most complicated examples than other state-of-the-art techniques

Advisor: Aiken Alexander
Barrett Clark
Trippel Caroline
Invariants of codimension 2 contact submanifolds

We construct new invariants of codimension 2 contact submanifolds. Our main invariant can be viewed as a deformation of the contact homology algebra of the ambient manifold. We describe various applications of these invariants to contact topology. In particular, we exhibit examples of codimension 2 contact embeddings into overtwisted and tight contact manifolds which are formally isotopic but fail to be isotopic through contact embeddings. We also give new obstructions to certain relative symplectic and Lagrangian cobordisms

Keywords: Mathematics.
Advisor: Eliashberg Y.
Ionel Eleny
Manolescu Ciprian
Investigating Distribution of Talk During Day-Long Naturalistic Recordings in English- and Spanish-Speaking Children

Children vary substantially in their rates of language growth (Fenson et al., 2007). Numerous studies in Western societies have shown that at least some of this variation is attributable to the overall amount of talk that children hear. Caregivers who verbally engage more with their children have children who demonstrate stronger later language skills (Hart & Risley, 1995; Weisleder & Fernald, 2013). However, studies conducted with children living in non-Western societies (e.g.,Casillas et al., 2019) have questioned the universal nature of this relation, showing normative language development even when children hear very little talk. This suggests that features of the language environments that extend beyond the overall amount of talk are also relevant for learning. This study directly contrasts mean overall talk (i.e., Adult Word Count, AWC) with two new measures that sought to capture variation in the moment-to-moment distribution of talk across the day: (1) consistency (i.e., degree of difference between higher and lower periods of talk) and (2) density (i.e., number of consecutive periods of dense talk). Participants were children in English- (n=56) and Spanish-speaking (n=49) families with 18-24-month-old children. Caregiver talk was estimated using day-long naturalistic audio recordings in the home using LENA technology (Xu et al., 2009). Outcome measures were real-time language processing and vocabulary size. Results revealed striking variability among families in all three measures of talk. Children in English-speaking families who experienced higher overall AWC and more consistent talk were more efficient in real-time language processing. However, AWC was the only unique predictor. Surprisingly, in the Spanish-speaking families, no links were observed to vocabulary. However, the number of dense periods of talk predicted later processing skills in Spanish-speaking families. Similar results were obtained after filtering the audio recordings for likely periods of child-directed speech. Future analyses should continue to explore measures of consistency and density by listening to all-day recordings.

Keywords: language learning
language processing
distribution of talk
Thesis
Investigating fracture network creation during hydraulic stimulation in enhanced geothermal systems

During hydraulic stimulation treatment in an enhanced geothermal (EGS) reservoir, it has been suggested that a complex fracture network including both preexisting natural fractures and newly formed fractures is created. In this stimulation mechanism, a fracture propagating from a preexisting natural fracture and the interaction of newly formed fractures and preexisting natural fractures play an important role in the creation of a fracture network. Analyzing the interaction between preexisting fractures and newly formed fractures during hydraulic stimulation is thus necessary to understand the creation of a fracture network. We approached to this research question with laboratory and numerical experiments for an EGS reservoir where large preexisting fractures dominate. Laboratory scale hydraulic fracturing experiments were conducted to investigate how a fracture network is created when a propagating hydraulic fracture and a preexisting fracture interact. The physics-based numerical model developed in this work was used to investigate fracture network creation from a small scale area including a small number of fractures to a reservoir scale with tens of fractures. We analyzed the geological factors that affect the fracture network patterns through the laboratory and numerical experiments. We observed that the stress state and preexisting fracture orientation affect the fracture propagation pattern in the laboratory experiments. The numerical analysis shows that the stress field induced by an upstream hydraulic fracture causes asymmetric distributions of normal and shear stresses along the preexisting fracture when they intersect, which resulted in initiation of a wing crack from the fracture tip on the side with larger angles. The numerical results also showed that the complexity of the created fracture network is affected by the fracture intersection angle, stress state, and injection rates. We reviewed past EGS projects and analyzed the stimulation mechanism during their hydraulic stimulation treatment. This study implies that stimulating a reservoir with poorly oriented preexisting fractures may result a complex and broad shaped fracture network, which would be beneficial for energy recovery

Keywords: Energy Resources Engineering.
Advisor: Horne Roland N.
Kovscek Anthony R. (Anthony Robert)
Tchelepi Hamdi
Investigating the use of NMR to estimate hydraulic conductivity in near-surface materials

Nuclear magnetic resonance (NMR) is a useful tool for characterizing groundwater resources. NMR measures the total amount of water and gives an estimate of the pore size in water containing geological materials. In the last several decades, NMR has been used more often to characterize groundwater resources, but most of these methods are based on relationships developed by the petroleum industry. This thesis studies how NMR can be used to estimate how easily water flows through porous materials. At the field scale, this thesis investigates how NMR can estimate the hydraulic conductivity (K). At the lab scale, this thesis investigates the use of NMR to study fluid flow at the pore scale. At the field scale, borehole NMR logging data was collected at two sites in a glacial aquifer in Wisconsin to investigate how well NMR data can estimate K. This study established NMR as a method for estimating K in a glacial aquifer in Wisconsin. Estimating K with NMR requires empirical calibration to estimate K accurately. How well other NMR calibrations and methods for estimating K is determined, and an optimal calibration is presented. At the laboratory scale, different NMR experiments are used to study how easily water flows through a porous material. Computational simulations are used to interpret the laboratory NMR data. The work done in this study demonstrates that certain NMR experiments can estimate parameters related to fluid flow useful for groundwater management. The research presented in this thesis focused on how to best estimate K with NMR, and how to characterize fluid flow at the pore scale. The research done investigating how well NMR estimates K at the field scale will improve how K is estimated with NMR in the future. This thesis also shows that different NMR experiments at the laboratory scale can estimate parameters relevant for groundwater management. This thesis demonstrates that NMR is a valuable tool for characterizing groundwater resources in the near surface.

Keywords: Geophysics.
Advisor: Knight Rosemary (Rosemary Jane)
Nishimura Dwight George
Song Yi-Qiao 
Vanorio Tiziana
Investigation of cell-cycle entry and DNA replication termination using single-cell microscopy

The cell cycle is a highly orchestrated series of cellular events that results in the duplication of DNA and division into two daughter cells. As a critical process during tissue development and homeostasis, the cell cycle is tightly regulated at the cellular, tissue, and organismal levels. In this thesis, we study two cellular processes that are key to cell-cycle regulation: the decision to enter the cell cycle in response to extracellular signals, and the multi-step process that replicates DNA. In the first study, we find that the decision of a newborn cell to proliferate or become quiescent is controlled by the memory of local cell density that is inherited from its mother cell. This memory regulates the expression of the cell-cycle activator cyclin D1 and inhibitor p27. Cyclin D1 and p27 form an activator-inhibitor balance in newborn cells that regulates the proliferation-quiescence decision in a sharp, ultrasensitive manner. We further show that competing cell density and mitogen signals are funneled into the cyclin D1-p27 balance through ERK signaling. We propose that memory of cell density, coupled to tight regulation of proliferation by the activator- inhibitor balance, may contribute to robust control of cell density and tissue homeostasis. In the second study, we demonstrate that replisome disassembly, a final step during DNA replication that is generally thought to be functionally downstream of nascent DNA synthesis, is continuously required for efficient DNA synthesis. Failure to disassemble replisomes traps essential replisome components on chromatin, which prevents their recycling into the soluble pool and blocks replication initiation. Cells with defects in replisome disassembly experience slow and incomplete DNA replication, G2/M checkpoint activation, and cell-cycle arrest. We also show that LRR1, which encodes a key component of the E3 ligase CRL2-LRR1 that initiates replisome disassembly, is an essential gene in actively dividing human cells. Thus we propose that inhibiting CRL2-LRR1 activity may be a potential target for cancer therapy, which acts by blocking the recycling of key replication factors that are in high demand in cancer cells due to the higher replicative activity in many cancers

Keywords: Chemical and Systems Biology.
Advisor: Ferrell James E.
Meyer Tobias
Brandman Onn
Wysocka Joanna
Investigation of condensed matter systems with resonant inelastic X-ray scattering

One of the key ideas in the condensed matter physics at a low energy scale is to treat an interacting many-body electron system in terms of weakly interacting emergent particles, namely elementary excitations. Resonant inelastic X-ray scattering (RIXS) has emerged as a powerful tool to study elementary excitations, thanks to the exciting instrumentation improvement and the advent of X-ray synchrotron radiation facilities. By tuning the incident X-ray photon energy to the resonance condition of a specific element, RIXS can resonantly enhance the scattering cross section between the incident photon and the elementary excitation in the material, enabling element-specific measurements. In this dissertation, we investigated magnetic excitations in the recently discovered superconducting infinite-layer nickelate thin films. Besides a similar crystal structure, the infinite-layer nickelate is nominally isoelectronic to the high temperature cuprate superconductors. At this early stage, it is important to experimentally study the similarities and differences between cuprates and nickelates, particularly the respective electronic and magnetic structures. Here, by utilizing the high-resolution RIXS, we found that the parent compound of the infinite-layer nickelate is not a charge-transfer material, like cuprates. Instead, it is probably closer to a Mott compound with an additional coupling with rare-earth metallic states. Furthermore, we also found a branch of dispersive magnetic excitations with a bandwidth of approximately 200 meV in the parent compound nickelates. Although these modes are significantly damped, they are reminiscent of the spin-wave of strongly coupled, anti-ferromagnetically aligned spins on a square lattice. Upon doping, the spectral weight and energy decrease slightly, while the modes become overdamped. The dispersion, doping dependence, damping effect of the magnetic excitation, and the added complexity due to the presence of the rare-earth metallic pockets, together with its comparison with the magnetic excitation in cuprates, will be discussed

Keywords: Physics.
Advisor: Shen Zhi-Xun
Devereaux Thomas Peter
Lee Young Sang
Lee Wei-Sheng