4,836 research outputs found

    Imaging of a fluid injection process using geophysical data - A didactic example

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    In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

    Evaluating Global Warming Potentials as Historical Temperature Proxies: an application of ACC2 Inverse Calculation

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    Global Warming Potentials (GWPs) are evaluated as proxies of the historical temperature by applying them to convert historical CH4 and N2O emissions to equivalent CO2 emissions. Our GWP analysis is based on the historical Earth system evolution obtained from the inverse calculation for the Aggregated Carbon Cycle, Atmospheric Cycle, and Climate Model (ACC2). Indices higher than the Kyoto GWPs are required to reproduce the historical temperature. The GWP for N2O, in particular, does not approximate the historical temperature with any time horizon because the GWP definition and calculations assume a background system different from the ACC2 inversion results. In addition, indices have to be progressively updated upon the acquisition of new measurements and/or the change in our understanding on the Earth system processes.global warming potentials

    Application of physical properties measurements to lithological prediction and constrained inversion of potential field data, Victoria Property, Sudbury, Canada.

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    In recent years the number of near-surface deposits has decreased significantly; consequently, exploration companies are transitioning from surface-based exploration to subsurface exploration. Geophysical methods are an important tool to explore below the surface. The physical property data are numerical data derived from geophysical measurements that can be analyzed to extract patterns to illustrate how these measurements vary in different geological units. Having knowledge of links between physical properties and geology is potentially useful to obtain more precise understanding of subsurface geology. Firstly, down-hole density, gamma radioactivity, and magnetic susceptibility measurements in five drillholes at the Victoria property, Sudbury, Ontario were analyzed to identify a meaningful pattern of variations in physical property measurements. The measurements grouped into distinct clusters identified by the fuzzy k-means algorithm, which are termed ‘physical log units’. There was a meaningful spatial and statistical correlation between these physical log units and lithological units (or groups of lithological units), as classified by the geologist. The existence of these relationships suggests that it might be possible to train a classifier to produce an inferred function quantifying this link, which can be used to predict lithological units and physical units based on physical property data. A neural network was trained from the lithological information from one hole, and was applied on a new hole with 64% of the rock types being correctly classified when compared with those logged by geologists. This misclassification can occur as a result of overlap between physical properties of rock types. However, the predictive accuracy in the training process rose to 95% when the network was trained to classify the physical log units (which group together the units with overlapping properties). Secondly, lithological prediction based on down-hole physical property measurements was extended from the borehole to three-dimensional space at the Victoria property. Density and magnetic susceptibility models were produced by geologically constrained inversion of gravity and magnetic field data, and a neural network was trained to predict lithological units from the two physical properties measured in seven holes. Then, the trained network was applied on the 3D distribution of the two physical properties derived from the inversion models to produce a 3D litho-prediction model. The lithologies used were simplified to remove potential ambiguities due to overlap of physical properties. The 3D model obtained was consistent with the geophysical data and resulted in a more holistic understanding of the subsurface lithology. Finally, to extract more information from geophysical logs, the density and gamma-ray response logs were analyzed to detect boundaries between lithological units. A derivative method was successfully applied on the down-hole logs, and picked the boundaries between rock types identified by geologists as well as additional information describing variation of physical properties within and between layers not identified by the geologist.Doctor of Philosophy (PhD) in Mineral Deposits and Precambrian Geolog

    fem and ann combined approach for predicting pressure source parameters at etna volcano

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    Abstract. A hybrid approach for forward and inverse geophysical modeling, based on Artificial Neural Networks (ANN) and Finite Element Method (FEM), is proposed in order to properly identify the parameters of volcanic pressure sources from geophysical observations at ground surface. The neural network is trained and tested with a set of patterns obtained by the solutions of numerical models based on FEM. The geophysical changes caused by magmatic pressure sources were computed developing a 3-D FEM model with the aim to include the effects of topography and medium heterogeneities at Etna volcano. ANNs are used to interpolate the complex non linear relation between geophysical observations and source parameters both for forward and inverse modeling. The results show that the combination of neural networks and FEM is a powerful tool for a straightforward and accurate estimation of source parameters in volcanic regions

    FEM and ANN combined approach for predicting pressure source

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    A hybrid approach for forward and inverse geophysical modeling, based on Artificial Neural Networks (ANN) and Finite Element Method (FEM), is proposed in order to properly identify the parameters of volcanic pressure sources from geophysical observations at ground surface. The neural network is trained and tested with a set of patterns obtained by the solutions of numerical models based on FEM. The geophysical changes caused by magmatic pressure sources were computed developing a 3-D FEM model with the aim to include the effects of topography and medium heterogeneities at Etna volcano. ANNs are used to interpolate the complex non linear relation between geophysical observations and source parameters both for forward and inverse modeling. The results show that the combination of neural networks and FEM is a powerful tool for a straightforward and accurate estimation of source parameters in volcanic regions
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