Application of statistical learning models to predict and optimize rate of penetration of drilling

Modeling the rate of penetration of the drill bit has been essential to optimizing drilling operations. Optimization of drilling – a cost intensive operation in the oil and gas industry– is essential, especially during downturns in the oil and gas industry. This thesis evaluates the use of statistical learning models to predict and optimize ROP in drilling operations. Statistical Learning Models can range from simple models (linear regression) to complex models (random forests). A range of statistical learning models have been evaluated in this thesis in order to determine an optimum method for prediction of rate of penetration (ROP) in drilling. Linear techniques such as regression have been used to predict ROP. Special linear regression models such as lasso and ridge regression have been evaluated. Dimension reduction techniques like principal components regression are evaluated for ROP prediction. Non-linear algorithms like trees have been introduced to address the low accuracy of linear models. Trees suffer from low accuracy and high variance. Trees are bootstrapped and averaged to create the random forests algorithm. Random forests algorithm is a powerful algorithm which predicts ROP with high accuracy. A parametric study was used to determine the ideal training sets for ROP prediction. It was conclude that data within a formation forms the best training set for ROP prediction. Parametric analysis of the length of the training set revealed that 20% of the formation interval depth was enough to train an accurate predictor for ROP. The ROP model built using statistical learning models were then used as an equation to optimize ROP. An optimization algorithm was used to compute ideal values of input feature to improve ROP in the test set. Surface controllable input features were varied in an effort to improve ROP. ROP was improved to save a predicted total of 22 hours of active drilling time using this method. This thesis introduces statistical learning techniques for predicting and optimizing ROP during drilling. These methods use input data to model ROP. Input features (surface parameters which are controllable on the rig) are then changed to optimize ROP. This methodology can be utilized for reducing nonproductive time (NPT) in drilling, and applied to optimize drilling procedures.Petroleum and Geosystems Engineerin

Kodaikanal Digitized White-light Data Archive (1921-2011): Analysis of various solar cycle features

Long-term sunspot observations are key to understand and predict the solar activities and its effects on the space weather.Consistent observations which are crucial for long-term variations studies,are generally not available due to upgradation/modifications of observatories over the course of time. We present the data for a period of 90 years acquired from persistent observation at the Kodaikanal observatory in India. We use an advanced semi-automated algorithm to detect the sunspots form each calibrated white-light image. Area, longitude and latitude of each of the detected sunspots are derived. Implementation of a semi-automated method is very necessary in such studies as it minimizes the human bias in the detection procedure. Daily, monthly and yearly sunspot area variations obtained from the Kodaikanal, compared well with the Greenwich sunspot area data. We find an exponentially decaying distribution for the individual sunspot area for each of the solar cycles. Analyzing the histograms of the latitudinal distribution of the detected sunspots, we find Gaussian distributions, in both the hemispheres, with the centers at $\sim$15$^{\circ}$ latitude. The height of the Gaussian distributions are different for the two hemispheres for a particular cycle. Using our data, we show clear presence of Waldmeier effect which correlates the rise time with the cycle amplitude. Using the wavelet analysis, we explored different periodicities of different time scales present in the sunspot area times series.Comment: Accepted for Publication in A&

Inter- and Intra-kingdom Signaling in Bacterial Chemotaxis, Biofilm Formation, and Virulence

Cell-cell communication between bacteria, belonging to the same species or to different species (Intra-kingdom signaling), or communication between bacteria and their animal host (Inter-kingdom signaling) is mediated through different chemical signals that are synthesized and secreted by bacteria or the host and is crucial for the survival of bacteria inside their host. The overall goal of this work was to understand the role of inter- and intra-kingdom signaling in phenotypes such as chemotaxis, colonization and biofilm formation, and virulence that are associated with infections caused by the human gastrointestinal (GI) tract pathogens. A part of our work also aimed at developing microfluidics-based models to study inter- and intra-kingdom signaling in biofilm formation, inhibition, and dispersal. We showed that norepinephrine (NE), an important host signal produced during stress, increases human opportunistic pathogen Pseudomonas aeruginosa growth, motility, attachment, and virulence, and also showed that the actions of NE are mediated primarily through the LasR, and not the RhlR QS system. We investigated the molecular mechanism underlying the chemo-sensing of the intra-kingdom signal autoinducer-2 (AI-2) by pathogens Escherichia coli and Salmonella typhimurium by performing different chemotaxis assays (capillary, microPlug and microFlow assays), and discovered that AI-2 is a potent attractant for E. coli and S. typhimurium, and that the Tsr chemoreceptor and periplasmic AI-2 binding protein LsrB are necessary for sensing AI-2, although uptake of AI-2 into the cytoplasm is not required. We concluded that LsrB, when bound to AI-2, interacts directly with the periplasmic domain of Tsr primarily at the Thr-61 and Asp-63 residues of LsrB, making LsrB the first known periplasmic-protein partner for Tsr. We fabricated a simple user-friendly microfluidic flow cell (microBF) device that can precisely measure the effect of a wide range of concentrations of single or combinations of two or more soluble signals on bacterial biofilm formation and development. We also constructed a synthetic biofilm circuit that utilizes the Hha and BdcA dispersal proteins of E. coli along with a quorum sensing (QS) switch that works based on the accumulation of the signal N-(3-oxo-dodecanoyl)-L-homoserine lactone (3-o-C12HSL) and implemented it in an upgraded �BF device. We showed that a QS system may be utilized with biofilm dispersal proteins to control consortial biofilm formation by removing an existing biofilm and then removing the biofilm that displaced the first one. These types of synthetic QS circuits may be used to pattern biofilms by facilitating the re-use of platforms and to create sophisticated reactor systems that will be used to form bio-refineries

End-to-end drilling optimization using machine learning

Drilling costs occupy a significant portion of oil and gas project’s budget. Optimization of drilling - increasing speed, reducing vibrations, and minimizing borehole instability - can lead to significant savings and hence have been extensively studied. Currently, most drilling optimization tools (or models) only tackle a single drilling metric: they seek to optimize either the rate of penetration (ROP), torque on bit (TOB), mechanical specific energy (MSE) or drilling vibrations. Models are often built independent of other metrics (without coupling) and do not accurately represent downhole conditions since drilling metrics are interrelated. This may lead to over or underestimation of the metric optimized which can severely reduce the effect of optimization. The objective of this dissertation is to introduce techniques, strategies, and algorithms that can be used to build a fully coupled drilling optimization model. Drilling optimization is studied by first optimizing ROP– where models for ROP prediction and inference are constructed using machine learning. Strategies and algorithms for determining optimal drilling parameters using ROP models are discussed. The unique problem posed by data-driven models are solved using meta-heuristic algorithms. A coupled model is constructed by building ROP, TOB, and MSE models conjointly using the random forests algorithm. Drilling vibrations – axial, lateral, and torsional – are modeled using a machine learning classification algorithm. This classification algorithm used to restrict the optimization space, ensuring that optimal parameters do not induce vibrations ahead of the bit. This model is used to investigate the effect of optimizing ROP and MSE on field data. A workflow is introduced linking all the aforementioned models into an end-to-end drilling optimization tool. The tool can be used as a recommendation system where field-measured data are used to determine and implement optimal drilling parameters ahead of the bit. The dissertation illustrates the use of statistical (or machine) learning techniques to address the problems encountered in drilling optimizationPetroleum and Geosystems Engineerin

Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging

The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.Comment: Accepted to ACL'23 Findings Pape