The ensemble kalman filter in reservoir engineering software

Methods of data assimilation are established in physical sciences and engineering for the merging of. Assessing the performance of the ensemble kalman filter. It has been proven that enkf is an efficient method for data assimilation, uncertainty assessment, and large scale problems in many engineering fields. The enkf originated as a version of the kalman filter for large problems essentially, the covariance matrix is replaced by the sample covariance, and it is now an important data assimilation component. The ensemble kalman filter enkf has been successfully applied in petroleum engineering during the past few years to constrain reservoir models to production or seismic data. The algorithm used in this code is referenced from the following. The importance of ensemble design behnam jafarpour, dennis b. This sequential assimilation method provides a set of updated static variables.

Ensemble kalman filter enkf is a new method for history matching that has received a lot of attention the last couple of years. The progress in data assimilation is because of both increased computational power and the introduction of techniques that are capable of handling large amounts of data and more severe nonlinearities. Keywords reservoir engineering history matching permeability ensemble kalman. Reservoir characterization is a process to create reliable reservoir models by integrating static and dynamic data available. Reservoir characterization using a streamlineassisted. The ensemble kalman filter enkf, a parameter estimation approach using the realtime dts temperature observations is proposed for automatic history matching and quantitative reservoir characterization of sagd reservoirs. Fast linearized forecasts for subsurface flow data. Understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the. In this case, since the rock property fields are highly nongaussian, a method that combines vectorbased levelset parameterization technique and ensemble kalman filter enkf or estimating fracture distributions is developed. Continuous reservoir model updating using an ensemble kalman filter with a streamlinebased covariance localization.

An iterative ensemble kalman filter for reservoir engineering applications. A regularizing iterative ensemble kalman method for pde. The intention of this paper is to demonstrate the use of the ensemble kalman filter enkf. History matching of petroleum reservoir models by the. Sullivan, uncertainty quanti cation, springer, heidelberg, 2015. Ensemblebased uncertainty quantification method for. Evensen 1 to recover the spatial distribution of the permeability of the rock matrix in a geothermal reservoir in combination with a numerical simulator of hydrothermal flow. Dynamic bayesian networks as a possible alternative to the ensemble kalman filter for parameter estimation in reservoir engineering anca haneaa. Jan mandel february 2007, updated january 2009 abstract the ensemble kalman. It is most practical for applications with unknown reservoir geometry, heterogeneous parameter distribution and when uncertainty needs to be quantified. This program uses the ensemble kalman filter to estimate a systems state. After the characterization, we can have better prediction of reservoir future performances. What is the ensemble kalman filter and how well does it work.

In this particular scientific application, the underlying software package is already parallelized using the sharedmemory programming paradigm open. Estimation of geothermal reservoir properties using the. Information on the permeability at depth is important for the exploitation of geothermal reservoirs. Summary this paper reports the use of the ensemble kalman filter enkf forautomatic history matching. Estimating reservoir permeabilities with the ensemble.

Create scripts with code, output, and formatted text in a single executable document. An iterative ensemble kalman filter for reservoir engineering applications article pdf available in computational geosciences 2. The ensemble kalman filter enkf is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. A distributedmemory parallelization of a sharedmemory. Model advance if posterior ensemble at time t 1 is t 1,n, n 1, n, advance each member to time t. Enkf is a monte carlo approximation of linear kalman lter, in which ensemble. The enkf is based on the simpler kalman filter kalman 1960. Using dynamic bayesian networks to implement feedback in a management risk model for the oil industry. This list is curated from repositories that make our lives as reservoir engineers, hackers and data wranglers easier or just more awesome. Continuous reservoir modeling updating by integrating. For fractured reservoirs, the connectivity of fracture distributions is crucial for predicting production characteristics. Ensemble methods present a practical framework for parameter estimation, performance prediction, and uncertainty quantification in subsurface flow and transport modeling.

The enkf algorithms have been adopted in many real applications such as oceanography 46, weather prediction 47, tsunami prediction 48, reservoir engineering 49 and hy drology 50. An introduction to ensemble kalman filtering jeffrey anderson ncar data assimilation research section dares ams nwpwaf, 26 june, 2007. Vadapalli r, luo p, kim t, kumar a and siddiqui s demonstration of gridenabled ensemble kalman filter data assimilation methodology for reservoir characterization proceedings of the 15th acm mardi gras conference. The ensemble kalman filter enkf has attracted attention as a useful method for solving the history matching problem. Ensemble kalman filter in matlab download free open. The ensemble kalman filter is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. Enkf is related to the particle filter but the enkf makes the assumption that all probability distributions involved are gaussian. Kalman filter, reservoir engineering, uncertainty quantification, multiscale data 1. First, shift ensemble to have exact mean of posterior. Open reservoir engineering is even more awesome, so we made a list.

Department of energy and resources engineering, college of engineering, peking university, beijing, china. Dynamic bayesian networks as a possible alternative to the. In this paper, ensemble kalman filter with preservation of facies ratio and discrete cosine transformation is proposed for. S gillijns et al what is the ensemble kalman filter and how well does it work. The aim of this paper is to focus on one of these techniques, the ensemble kalman filter enkf. Conclusion the ensemble kalman filter method is a powerful tool for parameter estimation in geothermal reservoirs when temporal data is available. Ensemble kalman filter can update reservoir models in real time and assess uncertainty by multiple models. We consider the parallelization of an inverse problem in the field of geothermal reservoir engineering. Ensemble kalman filter enkf is a recursive data process algorithm that uses continuous updating. Estimating reservoir permeabilities with the ensemble kalman filter. The codes used in several of the data assimilation experiments are available on a web page. A multimodel data assimilation framework via the ensemble kalman filter. The enkf has been introduced to petroleum science recently lorentzen et al.

The following matlab project contains the source code and matlab examples used for ensemble kalman filter. The ensemble kalman filter approximates a nonlinear system by forming an ensemble, such as through the unscented transformation see, for. A multimodel data assimilation framework via the ensemble. Introduction to ensemble kalman filters and the data. Enkf is a monte carlo method in which an ensembleof reservoir models is used. Dynamic bayesian networks as a possible alternative to the ensemble kalman filter for parameter estimation in reservoir engineering. The ensemble kalman filter in reservoir engineeringa. A distributedmemory parallelization of a sharedmemory parallel ensemble kalman filter. History matching of the punqs3 reservoir model using the. Introduction the principal objective of data assimilation methods 1 is to combine the information provided by measured data and a numerical forecast model to obtain an improved estimate of the system state and parameters.

Characterization of a channelized reservoir is difficult because of irregular channel pattern and connectivity. Observations can be temperature, hydraulic head during pumping tests, and chemical concentrations from tracer experiments at a few boreholes. The ensemble kalman filter enkf, on the other hand, is the preferred method for nonlinear and highdimensional models 46. The ensemble kalman filter enkf is tool for property estimation if temporal observations are available. Pdf an iterative ensemble kalman filter for reservoir. Meanwhile, traditional enkf requires assumption of the distributions normality. Adler a and lionheart w r b 2006 uses and abuses of eidors. The ensemble transform kalman filter etkf method is a natural extension of the concept of breeding and, as shown by wang and bishop, can be used to generate ensemble perturbations that can potentially ameliorate these shortcomings. The study has been focused on examining the usage and the applicability of ensemble kalman filtering techniques to the history matching procedures. The program supports reducedorder data assimilation methods, as well as ensemble assimilation approaches such as. Introduction to ensemble kalman filters and the data assimilation research testbed jeffrey anderson, tim hoar, nancy collins. The kalman filter is an efficient recursive filter that estimates the state of a linear dynamical system from a series of noisy measurements.

The focus on ensemble methods, such as the ensemble kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Aquifer characterization of gas reservoirs using ensemble. In accordance with the awesome manifesto, we add awesome repositories. Continuous reservoir model updating using an ensemble. Mclaughlin department of civil and environmental engineering massachusetts institute of technology cambridge, ma corresponding author behnam jafarpour massachusetts institute of technology. The university of tulsa petroleum reservoir exploitation projects, tuprep, is a cooperative industryuniversity research project organized to address basic and applied research needs of the petroleum industry in reservoir characterization, well testing and reservoir simulation. Motivation for the ensemble kalman filter enkf the kalman filter assumes the evolution model and observation operator is linear. The enkf originated as a version of the kalman filter for large problems, and it is now an important data assimilation component of ensemble forecasting. These facts may cause problems when filter is used in reservoir applications and result in sampling error. The program is multigrid finite differences or finite elements, multialgebra plugin analysis kernels, multimodel simple standardized interface.

The ensemble kalman filter uses two types of measurements in its updates of state and input replicates. This method is sequential and continuously update the reservoir model states saturations, pressures etc. The enkf is based on the simpler kalman filter, which is an efficient. The ensemble kalman filter enkf is often applied nowadays to solving such a problem. Continuous reservoir modeling updating by integrating experimental data using an ensemble kalman filter a dissertation submitted to the graduate faculty of the. Read ensemble kalman filter for automatic history matching of geologic facies, journal of petroleum science and engineering on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Besides, it is based on the linear update of the analysis equations. Ensemble kalman filter enkf is one of the most powerful methods with. In particular, the ensemble kalman filter enkf has received significant attention for its promising performance in calibrating heterogeneous subsurface flow models. However, the model can be used to forecast reservoir behavior only if it has been.