4,789 research outputs found
Performance analysis of various machine learning algorithms for CO2 leak prediction and characterization in geo-sequestration injection wells
The effective detection and prevention of CO2 leakage in active injection wells are paramount for safe carbon capture and storage (CCS) initiatives. This study assesses five fundamental machine learning algorithms, namely, Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN), for use in developing a robust data-driven model to predict potential CO2 leakage incidents in injection wells. Leveraging wellhead and bottom-hole pressure and temperature data, the models aim to simultaneously predict the location and size of leaks. A representative dataset simulating various leak scenarios in a saline aquifer reservoir was utilized. The findings reveal crucial insights into the relationships between the variables considered and leakage characteristics. With its positive linear correlation with depth of leak, wellhead pressure could be a pivotal indicator of leak location, while the negative linear relationship with well bottom-hole pressure demonstrated the strongest association with leak size. Among the predictive models examined, the highest prediction accuracy was achieved by the KNNR model for both leak localization and sizing. This model displayed exceptional sensitivity to leak size, and was able to identify leak magnitudes representing as little as 0.0158% of the total main flow with relatively high levels of accuracy. Nonetheless, the study underscored that accurate leak sizing posed a greater challenge for the models compared to leak localization. Overall, the findings obtained can provide valuable insights into the development of efficient data-driven well-bore leak detection systems.<br/
Ensemble Kalman inversion of induced polarization data
This paper explores the applicability of Ensemble Kalman Inversion (EKI) with level-set parameterization for solving geophysical inverse problems. In particular, we focus on its extension to induced polarization (IP) data with uncertainty quantification. IP data may provide rich information on characteristics of geological materials due to its sensitivity to characteristics of the pore-grain interface. In many IP studies, different geological units are juxtaposed and the goal is to delineate these units and obtain estimates of unit properties with uncertainty bounds. Conventional inversion of IP data does not resolve well sharp interfaces and tends to reduce and smooth resistivity variations, while not readily providing uncertainty estimates. Recently, it has been shown for DC resistivity that EKI is an efficient solver for inverse problems which provides uncertainty quantification, and its combination with level set parameterization can delineate arbitrary interfaces well. In this contribution, we demonstrate the extension of EKI to IP data using a sequential approach, where the mean field obtained from DC resistivity inversion is used as input for a separate phase angle inversion. We illustrate our workflow using a series of synthetic and field examples. Variations with uncertainty bounds in both DC resistivity and phase angles are recovered by EKI, which provides useful information for hydrogeological site characterization. While phase angles are less well-resolved than DC resistivity, partly due to their smaller range and higher percentage data errors, it complements DC resistivity for site characterization. Overall, EKI with level set parameterization provides a practical approach forward for efficient hydrogeophysical imaging under uncertainty
Impact of diagenesis on the pore evolution and sealing capacity of carbonate cap rocks in the Tarim Basin, China
Analyzing the pore structure and sealing efficiency of carbonate cap rocks is essential to assess their ability to retain hydrocarbons in reservoirs and minimize leaking risks. In this contribution, the impact of diagenesis on the cap rocks' sealing capacity is studied in terms of their pore structure by analyzing rock samples from Ordovician carbonate reservoirs (Tarim Basin). Four lithology types are recognized: highly compacted, peloidal packstone-grainstone; highly cemented, intraclastic-oolitic-bioclastic grainstone; peloidal dolomitic limestone; and incipiently dolomitized, peloidal packstone-grainstone. The pore types of cap rocks include microfractures, intercrystalline pores, intergranular pores, and dissolution vugs. The pore structure of these cap rocks was heterogeneously modified by six diagenetic processes, including calcite cementation, dissolution, mechanical and chemical compaction, dolomitization, and calcitization (dedolomitization). Three situations affect the rocks' sealing capacity: (1) grainstone cap rocks present high sealing capacity in cases where compaction preceded cementation; (2) residual microfractures connecting adjacent pores result in low sealing capacity; and (3) increasing grain size in grainstones results in a larger proportion of intergranular pores being cemented. Four classes of cap rocks have been defined according to the lithology, pore structures, diagenetic alterations, and sealing performance. Class I cap rocks present the best sealing capacity because they underwent intense mechanical compaction, abundant chemical compaction, and calcite cementation, which contributed to the heterogeneous pore structures with poor pore connectivity. A four-stage, conceptual model of pore evolution of cap rocks is presented to reveal how the diagenetic evolution of cap rocks determines the heterogeneity of their sealing capacity in carbonate reservoirs.</p
An uncertainty-based quality evaluation tool for nanoindentation systems
Instrumented Indentation Test (IIT) is a nonconventional mechanical tests allowing multi-scale mechanical characterisation. It is employed for research and quality control in strategic manufacturing fields for developing edge technologies. The state-of-the-art lacks a robust methodology to assess quality of indentations and benchmark indentation devices. This is limiting the application of IIT for specifying and verifying tolerances. This work proposes an uncertainty-based quality evaluation tool for IIT. A non-parametric uncertainty evaluation of calibration contribution is proposed. The method shows the statistical significance of indentation sets modelled by the bootstrap samples. The uncertainty is then propagated according to the law of uncertainty propagation for the evaluation of mechanical characteristics. The methodology is applied to five case studies. Results show that the uncertainty evaluation model can achieve robust and sensitive quantification of the indentation results and system quality, thus providing a useful practical tool for industrial and academic practitioners within a metrological framework
Flow characterization of compressible particulate biomass materials
Biomass materials like trees and crops can be converted to biochemical products and have been considered as one of the most promising alternatives for energy and fuels due to their abundance and easy access. However, the commercialization of bioenergy has been significantly constrained by severe issues during the handling of particulate biomass materials, manifested as unstable flow and jamming in handling equipment such as hoppers, feeders, or conveyors. Solving these issues centers on the mechanistic understanding of the flowability of milled biomass materials and their rheological and constitutive behaviors in various industrial equipment.
This thesis investigates the flow behavior of milled woody biomass across multiple scales and flow regimes. The study experimentally quantifies the mechanical and rheological properties of particulate biomass at particle, meso, and industrial scales, complemented by FEM simulations of biomass flow through hoppers and inclined planes at meso and industrial scales. The jamming physics of woody particles in wedge-shaped hoppers is analyzed in consideration of hopper geometry, particle density, packing, and surcharge. With these results, parameters governing the arching, mass flow, and funnel flow of milled biomass through industry hoppers are identified. These findings enable the design and optimization of industry hoppers for the efficient handling of milled woody biomass. In addition, the constitutive model characterizing the flow of milled woody biomass at both quasi-static and dynamic flow regimes is formulated and validated against laboratory data. In the end, the impacts of moisture content on the mechanical and flow behavior of milled woody biomass are evaluated. This study promotes the fundamental understanding of the flow physics of milled biomass materials across various scales, fosters high-fidelity numerical prediction models of the constitutive responses of compressible particulate biomass, and enables the development of the next-generation high-efficiency biomass handling equipment to reduce the cost and increase the safety of feedstock processing.Ph.D
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
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