Parameter estimation is an inverse problem which is crucial to reliable groundwater model
predictions and management. Numerous techniques have been developed to address this …

The advent of fast sensing technologies allow for real-time model updates in many
applications where the model parameters are uncertain. Once the observations are …

Continuous integration of real-time logging-while-drilling data into a subsurface model with
relevant geologic uncertainties enables strategic geosteering, a field-level optimization of …

Subsurface flow models are inherently imperfect, and the associated model error can lead to
unreliable flow predictions. In this work, we introduce treatments for model error in a data …

Abstract The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman
filter for high dimensional linear Gaussian state space models. EnKF methods have also …

The real-time interpretation of the logging-while-drilling data allows us to estimate the
positions and properties of the geological layers in an anisotropic subsurface environment …

Time-lapse seismic data is becoming a common information source in reservoir model
calibration workflows to improve production forecasts. The standard process compares a …

All models are imperfect, so it is important to consider uncertainty in their predictions. When
calibrating models to measured data, uncertainty in the model can be simultaneously …

Calibrating statistical models to data can be a challenging task, particularly when the model
is difficult or time consuming to evaluate. Methods that infer the parameters of these models …

Water scarcity and low irrigation efficiency are the main factors causing yield losses in spring
wheat in arid areas. To propose optimised irrigation scheduling for spring wheat in the arid …