scholarly journals Data assimilation and uncertainty assessment in 3D geological modeling

2017 ◽  
Author(s):  
Daniel Schweizer ◽  
Philipp Blum ◽  
Christoph Butscher
Keyword(s):  
Data Assimilation ◽  
Information Entropy ◽  
Model Uncertainty ◽  
Model Space ◽  
Distance Measures ◽  
Geological Model ◽  
Multiple Model ◽  
Geological Data ◽  
3D Geological Modeling ◽  
Model Geometry

Abstract. The quality of a 3D geological model strongly depends on the type of integrated geological data, their interpretation and associated uncertainties. In order to improve an existing geological model and effectively plan further site investigation, it is of paramount importance to identify existing uncertainties within the model space. Information entropy, a voxel based measure, provides a method for assessing structural uncertainties, comparing multiple model interpretations and tracking changes across consecutively built models. The aim of this study is to evaluate the effect of data assimilation on model uncertainty, model geometry and overall structural understanding. Several geological 3D models of increasing complexity, incorporating different input data categories, were built for the study site Staufen (Germany). We applied the concept of information entropy in order to visualize and quantify changes in uncertainty between these models. Furthermore, we propose two measures, the Jaccard and the City-Block distance, to directly compare dissimilarities between the models. The study shows that different types of geological data have disparate effects on model uncertainty and model geometry. The presented approach using both information entropy and distance measures can be a major help in the optimization of 3D geological models.

scholarly journals Uncertainty assessment in 3-D geological models of increasing complexity

Solid Earth ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 515-530 ◽  
Author(s):  
Daniel Schweizer ◽  
Philipp Blum ◽  
Christoph Butscher
Keyword(s):  
Information Entropy ◽  
Model Uncertainty ◽  
Model Space ◽  
Distance Measures ◽  
Geological Model ◽  
Multiple Model ◽  
Geological Data ◽  
Geological Models ◽  
Different Types ◽  
Model Geometry

Abstract. The quality of a 3-D geological model strongly depends on the type of integrated geological data, their interpretation and associated uncertainties. In order to improve an existing geological model and effectively plan further site investigation, it is of paramount importance to identify existing uncertainties within the model space. Information entropy, a voxel-based measure, provides a method for assessing structural uncertainties, comparing multiple model interpretations and tracking changes across consecutively built models. The aim of this study is to evaluate the effect of data integration (i.e., update of an existing model through successive addition of different types of geological data) on model uncertainty, model geometry and overall structural understanding. Several geological 3-D models of increasing complexity, incorporating different input data categories, were built for the study site Staufen (Germany). We applied the concept of information entropy in order to visualize and quantify changes in uncertainty between these models. Furthermore, we propose two measures, the Jaccard and the city-block distance, to directly compare dissimilarities between the models. The study shows that different types of geological data have disparate effects on model uncertainty and model geometry. The presented approach using both information entropy and distance measures can be a major help in the optimization of 3-D geological models.

scholarly journals Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization

2017 ◽  
Vol 145 (11) ◽  
pp. 4575-4592 ◽  
Author(s):  
Craig H. Bishop ◽  
Jeffrey S. Whitaker ◽  
Lili Lei
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Satellite Data ◽  
Localization Length ◽  
Model Space ◽  
Ensemble Size ◽  
Length Scales ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Expanded Ensemble

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

scholarly journals 3D geological modeling of the Upper Cretaceous reservoirs in GPT oil field, Abu Sennan area, Western Desert, Egypt

2019 ◽  
Vol 10 (2) ◽  
pp. 371-393
Author(s):  
Mohamed F. Abu-Hashish ◽  
Hamdalla A. Wanas ◽  
Emad Madian
Keyword(s):  
Seismic Data ◽  
Upper Cretaceous ◽  
Structural Model ◽  
Oil Field ◽  
Western Desert ◽  
Geological Model ◽  
3D Geological Modeling ◽  
Petrophysical Analysis ◽  
3D Geological Model ◽  
Property Modeling

Abstract This study aims to construct 3D geological model using the integration of seismic data with well log data for reservoir characterization and development of the hydrocarbon potentialities of the Upper Cretaceous reservoirs of GPT oil field. 2D seismic data were used to construct the input interpreted horizon grids and fault polygons. The horizon which cut across the wells was used to perform a comprehensive petrophysical analysis. Structural and property modeling was distributed within the constructed 3D grid using different algorithms. The workflow of the 3D geological model comprises mainly the structural and property modeling. The structural model includes fault framework, pillar girding, skeleton girding, horizon modeling and zonation and layering modeling processes. It shows system of different oriented major and minor faults trending in NE–SW direction. The property modeling process was performed to populate the reservoir facies and petrophysical properties (volume of shale (Vsh), fluid saturations (Sw and Sh), total and effective porosities (Φt and Φe), net to gross thickness and permeability) as extracted from the available petrophysical analysis of wells inside the structural model. The model represents a detailed zonation and layering configuration for the Khoman, Abu Roash and Bahariya formations. The 3D geological model helps in the field development and evaluates the hydrocarbon potentialities and optimizes production of the study area. It can be also used to predict reservoir shape and size, lateral continuity and degree of interconnectivity of the reservoir, as well as its internal heterogeneity.

Research on Constructing 3D Geological Model of the Construction Layers in Daxing New City Area of Beijing

2012 ◽  
Vol 594-597 ◽  
pp. 2897-2901
Author(s):  
Xiao Huang ◽  
Gang Chen ◽  
Zhe Lin Li
Keyword(s):  
High Precision ◽  
Scientific Basis ◽  
Kriging Interpolation ◽  
Geological Model ◽  
Beijing City ◽  
Modeling Tools ◽  
Technical Problems ◽  
3D Geological Modeling ◽  
3D Geological Model ◽  
New City

The new city in Daxing district is the focus of future development in the southern of Beijing city, establishing 3D geological model of its engineering construction layer can provide a scientific basis for urban planning and construction as well as the land’s proper utilization. After collecting the geological drilling data, we establish the geological model based on a 3D geological modeling platform developed by Peking University. The 3D spatial modeling method based on sections is used to analyze two key technical problems about simulation process named high-precision modeling and rapid modeling respectively. In the modeling process, Delaunay triangulation and Kriging interpolation algorithms are used. Furthermore, the manual intervention is achieved through the use of flexible and convenient 3D interactive modeling tools. The application examples indicate that the 3D geological model constructed by this method can better reflect the actual situation, it also possesses the advantages of high precision and fast modeling.

Geodiversity: Exploration of 3D geological model space

2013 ◽  
Vol 594 ◽  
pp. 27-37 ◽  
Author(s):  
M.D. Lindsay ◽  
M.W. Jessell ◽  
L. Ailleres ◽  
S. Perrouty ◽  
E. de Kemp ◽  
...  
Keyword(s):  
Model Space ◽  
Geological Model ◽  
3D Geological Model

scholarly journals Observer-Based Control of LPV Systems with Input Delay and Saturation and Matched Disturbances via a Generalized Sector Condition

2021 ◽  
Vol 2 ◽  
Author(s):  
Saeed Salavati ◽  
Karolos Grigoriadis ◽  
Matthew Franchek
Keyword(s):  
Model Uncertainty ◽  
Closed Loop ◽  
Loop System ◽  
Input Delay ◽  
Multiple Model ◽  
Closed Loop System ◽  
Sector Condition ◽  
Lpv Systems ◽  
Cubature Kalman Filter ◽  
Lpv System

This paper examines the control design for parameter-dependent input-delay linear parameter-varying (LPV) systems with saturation constraints and matched input disturbances. A gain-scheduled dynamic output feedback controller, coupled with a disturbance observer to cancel out input disturbance effects, was augmented with an anti-windup compensator to locally stabilize the input-delay LPV system under saturation, model uncertainty, and exogenous disturbances. Sufficient delay-dependent conditions to asymptotically stabilize the closed-loop system were derived using Lyapunov-Krasovskii functionals and a modified generalized sector condition to address the input saturation nonlinearity. The level of disturbance rejection was characterized via the closed-loop induced L2-norm of the closed-loop system in the form of linear matrix inequality (LMI) constraints. The results are examined in the context of the mean arterial pressure (MAP) control in the clinical resuscitation of critical hypotensive patients. The MAP variation response to the injection of vasopressor drugs was modeled as an LPV system with a varying input delay and was susceptible to model uncertainty and input/output disturbances. A Bayesian filtering method known as the cubature Kalman filter (CKF) was used to estimate the instantaneous values of the parameters. The varying delay was estimated via a multiple-model approach. The proposed input-delay LPV control was validated in closed-loop simulations to demonstrate its merits and capabilities in the presence of drug administration constraints.

scholarly journals Data Assimilation of Volcanic Aerosols using FALL3D+PDAF

2021 ◽  
Author(s):  
Leonardo Mingari ◽  
Arnau Folch ◽  
Andrew T. Prata ◽  
Federica Pardini ◽  
Giovanni Macedonio ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Volcanic Eruptions ◽  
Column Height ◽  
Model Parameters ◽  
Eruption Column ◽  
Mass Loading ◽  
Multiple Model ◽  
Volcanic Aerosols ◽  
Forecast Quality

Abstract. Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on infrastructures, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height, physical properties of particles or meteorological fields, represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based Data Assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and, (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the Local Ensemble Transform Kalman Filter (LETKF) is used to assimilate satellite-retrieved data of column mass loading. We show that this procedure may lead to nonphysical solutions and, consequently, conclude that LETKF is not the best approach for the assimilation of volcanic aerosols. However, we find that a truncated state constructed from the LETKF solution approaches the real solution after a few assimilation cycles, yielding a dramatic improvement of forecast quality when compared to simulations without assimilation.

scholarly journals A Sequential Variational Algorithm for Data Assimilation in Oceanography and Meteorology

2009 ◽  
Vol 137 (1) ◽  
pp. 269-287 ◽  
Author(s):  
Srdjan Dobricic
Keyword(s):  
Data Assimilation ◽  
Computational Cost ◽  
Model Space ◽  
Variational Data Assimilation ◽  
Computer Memory ◽  
Optimal Model ◽  
State Estimate ◽  
Optimal State ◽  
Model State

Abstract This study theoretically establishes a sequential variational (SVAR) method for the data assimilation in oceanography and meteorology defined on the model space. Requiring a significantly smaller amount of computer memory, theoretically SVAR gives the same optimal model state estimate as the four-dimensional variational data assimilation method. Its computational cost is similar to that of the four-dimensional variational data assimilation and representer methods. In addition to the optimal state estimates, SVAR computes error covariances at the end of the assimilation window. These advantageous properties of the new algorithm are obtained by combining the sequential methodology with suitable definitions of several new l2 norms, which implicitly provide required estimates.

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