A Federated Computational Workflow for Analysis of DISKOS Digital Palynological Slides

A novel federated computational workflow for analyzing digital palynological slide images is implemented in this thesis. The slide data files, typically exceeding 3GB, present significant data mobility and computation challenges. The novel distributed computational framework is implemented to address privacy concerns and the challenges associated with moving large data. The idea is to move computational to the data location, optimally utilizing local computational capacity and reducing data movement. Trained deep-learning models deployed in a containerized environment leveraging the Docker technology are integrated in the workflow with a user-friendly interface, and users can run processes with the trained models. The workflow processes include reading slide image files, generating tiled images, and identifying and removing undesirable tiles such as blank tiles. Object detection with the watershed segmentation algorithm identifies tiles with potential microfossils. The identified dinoflagellates are classified with a trained convolution neural network (CNN) model. The classification results are sent to the host and shared with the users. The federated computational approach effectively addresses the challenges related to moving and handling large palynological slide images, creating a more efficient, scalable, and distributed pipeline. Collaborative efforts involving domain experts for model training with more annotated slide images will improve the effectiveness of the workflow

A Federated Computational Workflow for Analysis of DISKOS Digital Palynological Slides.

A novel federated computational workflow for analyzing digital palynological slide images is implemented in this thesis. The slide data files, typically exceeding 3GB, present significant data mobility and computation challenges. The novel distributed computational framework is implemented to address privacy concerns and the challenges associated with moving large data. The idea is to move computational to the data location, optimally utilizing local computational capacity and reducing data movement. Trained deep-learning models deployed in a containerized environment leveraging the Docker technology are integrated in the workflow with a user-friendly interface, and users can run processes with the trained models.\\ The workflow processes include reading slide image files, generating tiled images, and identifying and removing undesirable tiles such as blank tiles. Object detection with the watershed segmentation algorithm identifies tiles with potential microfossils. The identified dinoflagellates are classified with a trained convolution neural network (CNN) model. The classification results are sent to the host and shared with the users. The federated computational approach effectively addresses the challenges related to moving and handling large palynological slide images, creating a more efficient, scalable, and distributed pipeline. Collaborative efforts involving domain experts for model training with more annotated slide images will improve the effectiveness of the workflow

Improved Well Design with Risk and Uncertainty Analysis

PhD thesis in Petroleum engineeringUncertainty and associated risk assessment are frequently applied in many disciplines such as engineering, medicine and economics. Yet this study is limited to a quantitative uncertainty analysis with respect to well design, in view of modeling. Well planning is a complex process involving several physical parameters that are decisive for casing design. Some of the input variables that are subject to randomness are considered uncertain parameters. In addition, tools and mathematical models used for well design do not provide true interpretations of natural phenomena or geological processes. The models, also, are subject to the uncertainties, which may result from the approximate nature of the modeling processes. Therefore, it is important to show how these uncertainties affect the model outputs. This information is critical for decision-making during well planning. Traditionally, deterministic models are used for predicting critical fracturing and collapse pressures required for mud program and casing design. In underbalanced drilling, the operational envelope is predicted based on single-point estimates of pore and collapse pressures. The deterministic method usually neglects the modeling uncertainties. This thesis proposes an improved methodology for well design. The approach considers uncertainties in the input data and identifies the most critical parameters. The input uncertainties—expressed as probability distributions—are propagated by means of Monte Carlo simulation. The intent is to provide a systematic way of weighing the deterministic predictions against the results from the stochastic simulations. With the probabilistic approach, it may be easier for well planners to handle contingent well operations. The work also presents a one-dimensional, two-phase transient model termed the AUSMV scheme. The flow model has some potential that can be relevant to training and academic purposes. The capability of the scheme to simulate highly dynamic phenomena is presented for dual-gradient drilling and underbalanced operations

EOR in chalk: Optimized oil recovery with modified seawater

Master's thesis in Petroleum engineeringThe effect of using seawater or modified seawater as a wettability modifier in chalk has been previously investigated at higher temperatures. This work has shown that “smart water” improves the oil recovery due to wettability alteration at temperatures above 90°C. In this work, the effect of modifying the salinity and the ionic composition of seawater on oil recovery from chalk is studied at lower temperatures, 90 and 70°C.respectively. The chalk cores with 10 % initial water saturation were saturated ,flooded and aged with the synthetic crude oil (with AN= 0.5mgKOH/g oil). Then, the cores were imbibed with different imbibing fluids. The brine VB0S, with no ability to change the wetting condition of the core was used as the reference brine. Seawater was modified by reducing the concentrations of the non-active ions, Na and Cl .Also, the concentration of sulfate which has a catalytic effect on the wettability alteration was increased. The chromatographic wettability test was used to determine the water-wet surface area of the chalk cores after the spontaneous imbibition tests. The experimental work showed improved oil recovery when modified seawater was used both at 90 and 70°C. The surface active ions (SO2 4 - and Ca 2 ) had easier access to the chalk surface by reducing the concentrations of the non-active ions in seawater. Increasing the sulfate concentration, improved both imbibition rate and ultimate recovery. For a slightly water-wet system, “smart water” was able to improve the oil recovery at temperatures down to 70°C. The wettability alteration process was confirmed by the chromatographic wettability tests. The tests indicated that the water-wet surface area in the cores increased with increasing “smartness” of seawater