On parameter bias in earthquake sequence models using data assimilation

The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate and shear stress, in the presence of a bias in the friction...

Options for the Implementations of Data Assimilation for Geotechnics

Data assimilation methods have been implemented on a slope stability problem, and the performance of different constitutive models and data assimilation schemes has been investigated. In the first part, a data assimilation scheme called the ensemble Kalman filter (EnKF) is implemented using a finite element model (FEM) and its performance with...

Estimating the occurrence of slow slip events and earthquakes with an ensemble Kalman filter

Our ability to forecast earthquakes and slow slip events is hampered by limited information on the current state of stress on faults. Ensemble data assimilation methods permit estimating the state by combining physics-based models and observations, while considering their uncertainties. We use an ensemble Kalman filter (EnKF) to estimate...

On the use of different data assimilation schemes in a fully coupled hydro-mechanical slope stability analysis

Different data assimilation schemes such as the ensemble Kalman filter (EnKF), ensemble smoother (ES) and ensemble smoother with multiple data assimilation (ESMDA) are implemented in a hydro-mechanical slope stability analysis. For a synthetic case, these schemes assimilate displacements at the crest and the slope to estimate strength and...

Improved Understanding of Naturally Fractured Reservoirs Using Data Assimilation

Naturally fractured reservoirs can pose challenges for energy operations such as hydrocarbon production, CO2 storage, and geothermal energy production. Fluid flow in these reservoirs is greatly affected by fracture properties such as orientation and aperture, whose magnitude is mainly influenced by the stresses on the reservoir rocks. Simulating...

An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation

This work demonstrates the efficiency of using iterative ensemble smoothers to estimate the parameters of an SEIR model. We have extended a standard SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible...

On the use of different constitutive models in data assimilation for slope stability

A recursive ensemble Kalman filter (EnKF) is used as the data assimilation scheme to estimate strength and stiffness parameters simultaneously for a fully coupled hydro-mechanical slope stability analysis. Two different constitutive models are used in the hydro-mechanical model: the Mohr-Coulomb (MC) model and the Hardening Soil (HS) model....

Quantifying the contribution of heat recharge from confining layers to geothermal resources

Geothermal operations are expanding and increasingly contributing to the current energy supply. Assessing the long-term operable lifetime of these projects is complicated as the reservoirs they produce from are often deep and subsurface properties are uncertain and spatially variable. The minimum lifetime of a geothermal project usually...

Economic and fault stability analysis of geothermal field development in direct-use hydrothermal reservoirs

The installed capacity of geothermal systems for direct use of heat is increasing worldwide. As their number and density is increasing, the their interaction with subsurface faults becomes more important as they could lead to safety risks from induced seismicity. Assessment and management of such risks is essential for the further development...

Seismic inversion with deep learning: A proposal for litho-type classification

This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data:...

Probabilistic Estimation of Reservoir Parameters Using the Complementary Nature of Seismic and mCSEM Data

Considering the steadily declining prices in the oil and gas industry, nowadays, the requirement for geophysical information becomes more important in order to get the most out of available reservoirs. Electromagnetic measurements could complement seismic data where it lacks information. The EM response to fluid fill complements the resolving...

Insights From Closed-Form Expressions for Injection- and Production-Induced Stresses in Displaced Faults

We consider fluid-induced seismicity and present closed-form expressions for the elastic displacements, strains, and stresses resulting from injection into or production from a reservoir with displaced faults. We apply classic inclusion theory to two-dimensional finite-width and infinite-width reservoir models. First, we simplify the fault model...

Quantification of coarsening effect on response uncertainty in reservoir simulation

In this study, an attempt is made to better understand the effect coarsening of the parameter space has on the uncertainty representation of the response. Firstly, an HF ensemble of channelized reservoir models is constructed using a Multi-Point Statistic (MPS) approach. Several levels of coarsening are generated using a flow-based upscaling...

On estimating geomechanical parameters from surface deformation with a particle method: Data assimilation for subsidence monitoring (PPT)

Sea-Level Data Assimilation for Estimating Salinity Variability in the Tropical Pacific

Hypersonic airbreathing reusable advanced launcher: A feasibility study