An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge

In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariancematrix estimation. Our filter implementation combines information brought by an ensemble of model realizations, and thatbased on our prior knowledge about the dynamical system of interest. We perform the combination of both...

Forecasting PM10 and PM2.5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation

A data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of PM10 and PM2.5 in a densely populated urban valley of the tropical Andes. The Aburrá Valley in Colombia was used as a case study, given data availability and current environmental issues related to population...

Deep learning history matching for real time production forecasting

The forecasting of gas production from mature gas wells, due to their complex end-of-life behaviour, is challenging and often associated with uncertainties (both measurements and modelling uncertainties). Yet, having good forecasts are crucial for operational decision making. In this paper, we present a purely black-box based approach, which...

A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)

In the past years, many applications of historymatching methods in general and ensemble Kalman filter in particular have been proposed, especially in order to estimate fields that provide uncertainty in the stochastic process defined by the dynamical system of hydrocarbon recovery. Such fields can be permeability fields or porosity fields, but...

Seismic history matching of fluid fronts using the ensemble kalman filter

Time-lapse seismic data provide information on the dynamics of multiphase reservoir fluid flow in places where no production data from wells are available. This information, in principle, could be used to estimate unknown reservoir properties. However, the amount, resolution, and character of the data have long posed significant challenges for...

Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter

The Ensemble Kalman Filter (EnKF), in combination with travel-time parameterization, provides a robust and flexible method for quantitative multi-model history matching to time-lapse seismic data. A disadvantage of the parameterization in terms of travel-times is that it requires simulation of models beyond the update time. A new distance...

Dynamic bayesian networks as a possible alternative to the ensemble kalman filter for parameter estimation in reservoir engineering

The objective of reservoir engineering is to optimize hydrocarbon recovery. One of the most common and efficient recovery processes is water injection. The water is pumped into the reservoir in injection wells in order to push the oil trapped in the porous media towards the production wells. The movement of the water front depends on the...

Integrated workflow for computer assisted history matching on a channelized reservoir

Increasingly computer assisted techniques are used for history matching reservoir models. Such methods will become indispensable in view of the increasing amount of information generated by intelligent wells, in which case manual interpretation becomes too time consuming. Also, with the increasing possibilities for controlling a reservoir, a...

Ensemble-based conditioning of reservoir models to seismic data

While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in incorporating 3D or time-lapse seismic data into...

A case study of the history matching of a sector of the nome field using the ensemble Kalman filter

In the history matching process reservoir parameters are estimated so they can be further used in a simulator to reproduce the past behaviour of the reservoir. During the last two decades the methodology evolved from manual methods to computer assisted procedures which can handle larger amounts of data. Now, when the computational power has...

Performance of the ensemble Kalman filter outside of existing wells for a channelized reservoir

The ensemble Kalman filter (EnKF) appears to give good results for matching production data at existing wells. However, the predictive power of these models outside of the existing wells is much more uncertain. In this paper, for a channelized reservoir for five different cases with different levels of information the production history is...

Soft sensing for two-phase flow using an ensemble Kalman filter

A new approach for real-time monitoring of horizontal wells, which is based on data assimilation concepts, is presented. Such methodology can be used when the direct measurement of multiphase flow rates is unfeasible or even unavailable. The real-time estimator proposed is an ensemble Kalman filter employing a dynamic model of the pipe flow and...

An iterative ensemble Kalman filter for reservoir engineering applications

The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the distribution's normality. Besides, it is based on the...

A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe

Fine particulate matter (PM) is relevant for human health and its components are associated with climate effects. The performance of chemistry transport models for PM, its components and precursor gases is relatively poor. The use of these models to assess the state of the atmosphere can be strengthened using data assimilation. This study...

Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale

Two different data assimilation techniques have been applied to assess exceedances of the daily and annual mean limit values for PM10 on the regional scale in Europe. The two methods include a statistical interpolation method (SI), based on residual kriging after linear regression of the model, and Ensemble Kalman filtering (EnKF). Both methods...

History matching using a multiscale Ensemble Kalman Filter

Since the first version of Kalman Filter was introduced in 1960 it received a lot of attention in mathematical and engineering world. There are many successful successors like for example Ensemble Kalman Filter (Evensen 1996) which has been applied also for reservoir engineering problems. The method proposed in [Zhou et al. 2007] draws together...