Download Quantifying Uncertainty in Subsurface Systems - Céline Scheidt | ePub
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Whether earth science modeling is performed on a local, regional or global.
A systematic process of identifying relevant subsurface uncertainties and then having been identified, ranges for each uncertainty must be quantified, which.
Quantifying uncertainty in subsurface systems tn vilhelmsen, k maher, c da silva, t hermans, o grujic, j park, g yang quantifying uncertainty in subsurface systems, 217-262 2018.
Geostatistics and quantification of uncertainty and risk qualified decision making issues and optimization of subsurface data management and life-cycle uncertainty management; analytical interpretation of centrifuge data to determine the relative permeability curve; construction of q-q plots, semi variograms, kriging, and uncertainty modelling.
Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability.
Inverse modeling and uncertainty quantification integration of dynamic response data into subsurface flow models is commonly performed by formulating and solving an inverse problem.
Subsurface flow problems usually involve some degree of uncertainty. Consequently, uncertainty quantification is commonly necessary for subsurface flow prediction. In this work, we propose a methodology for efficient uncertainty quantification for dynamic subsurface flow with a surrogate constructed by the theory-guided neural network (tgnn).
Producing reference information on the subsurface is a key role for brgm as the french geological survey.
The very nature of the subsurface is so complex that any prediction 39 is subject to large uncertainties. It is clear that a prediction alone is not sufficient, but an entire 40 uncertainty.
In a common geological structure, there are patterns at different resolutions generated by various geological processes. Normally, the same statistical information is considered for all different scales in the simulation process.
To account for and quantify the uncertainties in the prior knowledge, for subsurface flow model calibration and uncertainty quantification applications.
Quantifying uncertainty in subsurface systems céline scheidt, lewis li, jef caers, troels norvin vilhelmsen, kate maher, carla da silva, thomas hermans (ugent), ognjen grujic, jihoon park and guang yang (2018) quantifying uncertainty in subsurface systems.
Quantification of subsurface structural uncertainty in groundwater models using 3d geophysical data.
Quantifying uncertainty in subsurface systems jef caers stanford university, usa whether earth science modeling is performed on a local, regional or global scale, for scientific or engineering purposes, uncertainty is inherently present due to lack of data and lack of understanding of the underlying phenomena and processes taking place.
However, to the best of our knowledge, this is the first time that it has been used to quantify the uncertainty in simulations generated by a globally implicit, fully-integrated surface and variably-saturated subsurface flow and solute transport model.
Certain amount of quantification of possible errors in the design of an engineering structure due to uncertainty regarding the subsurface properties.
Siam/asa journal on uncertainty quantification markov chain monte carlo algorithm with applications to uncertainty quantification in subsurface flow.
The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable.
Quantifying uncertainty in subsurface systems extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks.
Estimates can be provided by quantifying the uncertainty of subsurface rock properties and state variables, such as temperature or pressure, in a geothermal reservoir. This quantification can be obtained by using a stochastic approach called monte carlo simulation. To this end, we integrated the stochastic algorithm “sequential gaussian.
Computational uncertainty quantification: mathematical foundations, prominent examples are climate and weather forecasts, subsurface flow, social media,.
We outline an uncertainty quantification workflow that focuses on several elements: 1) decision-driven sensitivity analysis to determine key reservoir variables,.
Quantifying uncertainty in subsurface system provides a holistic view on uncertainty quantification for geological resources.
Quantifying uncertainty in subsurface systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians.
Modeling fluid flow through fracture networks is challenging due to the geometric characteristics of fractures.
Modeling subsurface uncertainty: practical considerations combining data assimilation with markov chain monte carlo for uncertainty quantification.
The multiscale nature of geological formations can have a strong impact on subsurface flow processes. In an attempt to characterize these formations at all relevant length scales, highly resolved property models are typically constructed. This high degree of detail greatly complicates flow simulations and uncertainty quantification.
Cast in geometric representations of subsurface structures, and (b) concepts and methods to analyze, quantify, and communicate related uncertainties in these.
Each stage in this process inherently carries a level of uncertainty due to this wide range of data, so subsurface teams generate multiple equally plausible static geo models, which try to give an estimate of the uncertainty.
Geostatistics and quantification of uncertainty and risk qualified decision making issues and optimization of subsurface data management and lifecycle uncertainty management analytical interpretation of centrifuge data to determine the relative permeability curve construction of q-q plots, semivariograms, krigging, and uncertainty modelling.
Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface.
Subsurface stratigraphy is critical to the design, construction, and subsequent performance of geotechnical structures.
Compre online quantifying uncertainty in subsurface systems: 236, de li, lewis, caers, jef, scheidt, cã©line na amazon.
Data integration, risk assessment and quantification of uncertainty are key issues in petroleum exploration and development.
Operational uncertainty results from the operating system itself, or the operator. The challenges of integrating the work of facilities and reservoir engineers to manage the uncertainties in design data arise from their differing views on quantifying reservoir characteristics and production forecasts.
A quantification of the impact of various subsurface uncertainties on economic figures may also help justify the acquisition of further data, in order to reduce the uncertainty before major decisions are made. A chain of tools has been developped at totalfinaelf to deal with the quantification of subsurface uncertainties.
Department of geoscience, aarhus university, aarhus, denmark.
Uncertainty quantification – essentially a measure of our lack of understanding – is a scientific approach to the problem that creates a set of rules on how to proceed based on mathematics and logic, in particular, probability theory and statistics.
11 may 2018 under the earth's surface is a rich array of geological resources, many with potential use to humankind.
All subsurface models derived from seismic and offset well data have uncertainties inherent in the data even after all possible steps have been taken to process the data to maximize the accuracy. There is, however, one more step that could be taken – to quantify the remaining uncertainty.
Since 2000, the research of uncertainty quantification (uq) has been field design and operation, and multiphase flow and transport in subsurface hydrology.
Uncertainty quantification in subsurface modeling the project is mainly based on improvement of uncertainty quantification methods used for probabilistic geo-statistical/geophysical inversion and reservoir modeling.
A bayesian machine learning based subsurface modeling approach for uncertainty quantification in geotechnical site characterization. Proceedings of 16th international conference of iacmag turin, italy.
Ing capabilities that let you quantify uncertainty, provision fundamental and impera-tive flow simulation input, and mitigate potential risk. Sas® enables more reliable drilling plans, improved secondary and tertiary recovery strategies, and a more comprehensive portfolio analysis of upstream assets.
Performance of uncertainty quanti cation tasks which are ubiquitous in subsurface reser-voir simulations. In one work, we accelerate multiscale methods by embedding a neural network surrogate for the fast computation of the custom basis functions, replacing the need to solve the local elliptic problems normally required to obtain them.
Particularly those related to uncertainty quantification relying on bayesian approaches. Quantifying uncertainty on predictions made in subsurface sedimentary.
9 dec 2019 the uncertainty of the position and hence the borehole measurements also grows.
Critical to this is the management of uncertainty, simply because of the lack of access to exhaustively quantify the subsurface medium, the fluids it contains and how they behave under human-induced changes.
Uncertainty quantification for subsurface flow problems is typically accomplished through model-based inversion procedures in which multiple posterior (history-.
Abstract: deep learning techniques have been shown to be extremely effective for various classification and regression problems, but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. In subsurface characterization projects, tools consisting of seismic.
1 jan 2021 pdf subsurface flow problems usually involve some degree of uncertainty. Consequently, uncertainty quantification is commonly necessary.
[1] predictions of reactive transport in the subsurface are routinely compromised by both model (structural) and parametric uncertainties. W e present a set of computational tools for quantifying these two types of uncertainties. The model uncertainty is resolved at the molecular scale where epistemic uncertainty incorporates aleatory.
Models alone do not quantify uncertainty, but do allow the determination of key methods relevant to uncertainty quantification in the subsurface is provided.
A bayesian machine learning based subsurface modeling approach for uncertainty quantification in geotechnical site characterization. Proceedings of 16th international conference of iacmag, turin, italy.
Cost-effectiveness of the boreholes can be evaluated based on the corresponding reductions in geotechnical uncertainty and their influence on the budget. The approach is illustrated using a hypothetical excavation scenario, where the project costs are affected by uncertainty in the subsurface strata, particularly the rockhead level across the site.
Subsurface source zone characterization and uncertainty quantification using discriminative random fields.
We focus on the development and quantification of ‘measure- ment uncertainty’ associated with seismic interpretations, and integrating this into subsurface modelling workflows.
Models alone do not quantify uncertainty, but do allow the determination of key variables that influence models and decisions. Next, an overview of the various data science methods relevant to uncertainty quantification in the subsurface is provided. Sensitivity analysis is then covered, specifically monte carlo-based sensitivity analysis.
Efficient uncertainty quantification in fully-integrated surface and subsurface model independent uncertainty quantification with polynomial chaos is described.
Uncertainty in the subsurface characterisation of uk nuclear sites poses significant risks in terms of operational cost and environmental protection. Improved knowledge of the uncertainty of subsurface properties and processes is needed in order to enhance risk mitigation.
Once all sources of uncertainty have been quantified many equiprobable realizations of the structural model are generated.
17 oct 2019 learning: a protocol for uncertainty quantification in earth systems book: “ quantifying uncertainty in subsurface systems” (wiley-blackwell,.
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