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

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

Hanea, A.
Hanea, R.
Zilko, A.

2012

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 reservoir characteristics. To avoid an early water breakthrough in the oil production wells, a good knowledge of the subsurface is imperative. The properties of the rock, e.g. porosity and permeability, are therefore important for oil extraction since they influence the ability of fluids to flow through the reservoir. They represent unknown parameters that need to be estimated when simulating a reservoir. In this paper the authors are concerned with estimating the permeability field of a reservoir. To characterise the fluid flow into the reservoir we use a two phase (oil-water) 2D °ow model which can be represented as a system of coupled nonlinear partial differential equations which cannot be solved analytically. Consequently, we build a state- space model for the reservoir. There are many ways of representing state-space models, one of the most common being the Kalman Filter (KF) model and its variants, e.g. En(semble)KF. A more general representation is a dynamic Bayesian network. Recently, the performance of the EnKF and that of a non-parametric BN were investigated and compared in a twin experiment for permeability estimation [1]. The NPBN approach proved a promising alternative to the EnKF. Yet a fair number of open questions emerged from the comparison of the two methods. Moreover, in all investigations the assumptions of the EnKF method were used in the NPBN approach for a proper comparison. In this paper we try to answer some of the questions left open, and extend the initial work by making more realistic assumptions. Copyright © (2012) by IAPSAM & ESRA.

Earth & Environment
PG - Petroleum Geosciences
EELS - Earth, Environmental and Life Sciences
Geological Survey Netherlands
Geosciences
Energy / Geological Survey Netherlands
Ensemble kalman filter
Non parametric bayesian networks
Parameter estimation
Reservoir engineering
Dynamic Bayesian networks
Ensemble Kalman Filter
Flowthrough
Hydrocarbon recovery
Injection wells
Non-parametric
Non-parametric Bayesian
Nonlinear partial differential equations
Oil extraction
Oil production wells
Oil-water
Permeability estimation
Permeability fields
Production wells
Recovery process
Reservoir characteristic
Reservoir engineering
Space models
State-space models
Twin experiments
Two phase
Water breakthrough
Water front
Bayesian networks
Flow of fluids
Hydrocarbons
Kalman filters
Oil wells
Parameter estimation
Partial differential equations
Porous materials
Safety engineering
Wells
Petroleum reservoir engineering

http://resolver.tudelft.nl/uuid:0dbc1501-edfa-4e3f-92ab-751f0d8e0912

470039

9781622764365

11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012, 25 June 2012 through 29 June 2012, Helsinki, 729-738

Sponsors: Radiation and Nuclear Safety Authority (STUK); VTT Technical Research Centre of Finland; Aalto University; Fortum Corporation; Teollisuuden Voima Oyj (TVO)

conference paper