A novel methodological approach for land subsidence prediction through data assimilation techniques

Computational Geosciences - Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and...     

Robust numerical implementation of a 3D rate-dependent model for reservoir geomechanical simulations

A 3D elasto-plastic rate-dependent model for rock mechanics is formulated and implemented into a Finite Element (FE) numerical code. The model is based on the approach proposed by Vermeer and Neher (A soft soil model that accounts for creep. In: Proceedings of the International Symposium “Beyond 2000 in Computational Geotechnics,” pages 249-261, 1999). An original strain-driven algorithm with an Inexact Newton iterative scheme is used to compute the state variables for a given strain increment.The model is validated against laboratory measurements, checked on a simplified test case, and used to simulate land subsidence due to groundwater and hydrocarbon production. The numerical results prove computationally effective and robust, thus allowing for the use of the model on real complex geological settings.     

New paleomagnetic pole and magnetostratigraphy of Faroe Islands flood volcanics, North Atlantic igneous province

A paleomagnetic sampling was carried out along four sections (altogether 86 lava flows, 548 samples) in the North Atlantic Igneous Province outcropping in Faroe Islands, Denmark. The four polarity zones in the 700-m-thick exposed part of the Faroes lower formation can be correlated with the geomagnetic polarity time scale as C26n-C25r-C25n-C24r. The seven lava flows erupted during C25n indicate a very low eruption rate in the upper part of the Faroes lower formation of ∼1/70 kyr. The Faroes middle and upper formations (composite thickness ∼2300 m) are all reversely magnetized corresponding to C24r. The eruption rate at the onset of middle formation volcanism was very high as evidenced by several thick lava sequences recording essentially spot readings of the paleomagnetic field. The shift in eruption rate between the Faroes lower and middle formations and evidence that onset of the Faroes middle formation volcanism took place in C24r are of particular importance, placing onset of middle formation volcanism in close temporal relation to North Atlantic continental break-up and the late Paleocene thermal maximum. After grouping flows recording the same field directions, we obtained 43 independent readings of the paleomagnetic field, yielding a paleomagnetic pole with coordinates 71.4°N, 154.7°E (A₉₅=6.0°, K=14, N=43); age 55-58 Ma. The pole is supported by a positive reversal test. Paleosecular variation, estimated as the angular standard deviation of the virtual geomagnetic pole distribution 21.7°+3.9°/−2.8°, is close to expected for the given age and paleolatitude. Our new Faroes paleomagnetic pole is statistically different from the majority of previously published poles from the British and Faroes igneous provinces, and we suggest that these older data should be used with care.     

How dynamical models can learn from the data��an example with a simplified ENSO model

Most dynamical models of the natural system contain a number of empirical parameters which reflect our limited understanding of the simulated system or describe unresolved subgrid-scale processes. While the parameterizations basically introduce some uncertainty to the model results, they also hold the prospect of tuning the model. In general, a deterministic tuning is related to an inversion of the model which is often impossible or requires considerable computing effort for most climate models. Another way to adjust the model parameters to a specific observed process is stochastic fitting where a set of parameters and model output are taken as random variables. Here, we present a dynamical?Cstatistical approach with a simplified model of the El Ni?o?CSouthern Oscillation (ENSO) cycle whose parameters are adjusted to simulated and observed data by means of Bayesian statistics. As ENSO model, we employ the Schop?CSuarez delay oscillator model. Monte Carlo experiments highlight the large sensitivity of the model results to varied model parameters and initial values. The statistical adjustment is done by Bayesian model averaging of the Monte Carlo experiments. Applying the method to simulated data, the posterior ensemble mean is much closer to the reference data than the prior ensemble mean. The learning effect of the model is evident in the leading empirical orthogonal functions and statistically significant in the mean state. When the method is applied to the observed ENSO time series, the ENSO model in its classical setup is not able to account for the temporally varying periodicity of the observed ENSO phenomenon. An improved setup with continuous adjustment periods and extended parameter range is developed in order to allow the model to learn from the data gradually. The improved setup leads to promising results during the twentieth century and even a weak forecast skill over 6?months. Thus, the described method offers a promising tool for data assimilation in dynamical weather and climate models. However, the simplified ENSO model is barely appropriate for operational ENSO forecasts owing to its limited physical complexity.     

Parallel inexact constraint preconditioning for ill-conditioned consolidation problems

Constraint preconditioners have proved very efficient for the solution of ill-conditioned finite element (FE) coupled consolidation problems in a sequential computing environment. Their implementation on parallel computers, however, is not straightforward because of their inherent sequentiality. The present paper describes a novel parallel inexact constraint preconditioner (ParICP) for the efficient solution of linear algebraic systems arising from the FE discretization of the coupled poro-elasticity equations. The ParICP implementation is based on the use of the block factorized sparse approximate inverse incomplete Cholesky preconditioner, which is a very recent and effective development for the parallel preconditioning of symmetric positive definite matrices. The ParICP performance is experimented with in real 3D coupled consolidation problems, proving a scalable and efficient implementation of the constraint preconditioning for high-performance computing. ParICP appears to be a very robust algorithm for solving ill-conditioned large-size coupled models in a parallel computing environment.     

The Copernican principle in compact space–times

Copernicus realized that we are not at the centre of the Universe. A universe made finite by topological identifications introduces a new Copernican consideration: while we may not be at the geometric centre of the Universe, some galaxy could be. A finite universe also picks out a preferred frame: the frame in which the universe is smallest. Although we are not likely to be at the centre of the Universe, we must live in the preferred frame (if we are at rest with respect to the cosmological expansion). We show that the preferred topological frame must also be the comoving frame in a homogeneous and isotropic cosmological space–time. Some implications of topologically identifying time are also discussed.     

Correction to: A novel methodological approach for land subsidence prediction through data assimilation techniques

Computational Geosciences - A Correction to this paper has been published: https://doi.org/10.1007/s10596-021-10079-6     

A two‐invariant pseudoelastic model for reservoir compaction

Predicting the deformations of deep reservoirs due to fluid withdrawal/injection is a challenging task that could have important environmental, social, and economical impacts. Finite element models, if endowed with an appropriate constitutive law, represent a useful tool for computing the displacements, the deformations, and the stress distributions in reservoir applications. Several studies show that hypoelastic laws, based on a stress‐dependent vertical compressibility, are able to provide accurate results, confirmed by in situ and satellite measurements. On the other hand, such laws present some weaknesses related to the numerical implementation, in particular due to the nonsymmetry of the tangent operator. This paper presents a new constitutive model based on 2 invariants (the mean normal and deviatoric stresses), characterized by a variable pressure‐dependent bulk modulus K. This constitutive law allows for overcoming most shortcomings of the hypoelastic law, although preserving the same accuracy, reliability, and ease of use and calibration. This paper presents a procedure to identify the parameters of the new model, starting from the typically available data on the vertical compressibility. Numerical results show a good agreement between the 2 laws, suggesting the proposed approach as a valid alternative in reservoir applications.