History Matching Channelized Reservoirs Using the Ensemble Kalman Filter

Abstract

Even though the ensemble Kalman filter (EnKF) is widely used, history matching reservoirs with facies description has proven to be a major challenge. A preferred technique for estimating largescale facies fields within the petroleum industry is still missing. In this paper, we present a new approach to this problem. Instead of applying the EnKF directly to facies realizations, the approach applies a transformation of facies fields to a specific level-set function, representing distances between facies types. This ensures better agreement with the EnKF Gaussianity assumptions, and the method always returns facies realizations with geological authenticity. The method also offers large flexibility in generating the initial ensemble, which can be performed using any geostatistical tool. Furthermore, no modifications of the standard EnKF equations are needed.The methodology is evaluated on two synthetic examples with increasing complexity. In both examples, we consider reservoirs with channel structure. The results presented show that the updated models give large improvements in matching the measurements, and the uncertainty of the models is decreased. Further, recovery of the true petrophysical parameters is highly dependent on sufficient information in the measurements, but in one of the examples considered we are able to completely recover the true channel structure. Additional improvements in the quality of the updated facies fields are obtained by proper handling of the distances close to the reservoir boundaries, and by conditioning on specific statistical measures to better preserve prior information about channel properties. Wikipedia. 2010. Level set method (revised 5 September 2010), http:// en.wikipedia.org/wiki/Level set method.

Recent progress on reservoir history matching: a review

“…Sarma and Chen [146] combined EnKF with kernel methods where standard EnKF is applied in the feature space and the updated non-Gaussian field is retrieved by solving pre-image problem. Lorentzen et al [112] used the level method with EnKF to update channel fields. They proposed a technique to avoid spurious boundary effects and investigated the benefit to the updates from additional hard data on the characteristic of channels.…”

Uncertainty Quantification of Channelized Reservoir Using Ensemble Smoother with Selective Measurement Data

“…In recent years, channelized reservoirs, which have bimodal distribution are characterized by EnKF (Jafarpour and McLaughlin, 2009b;Lorentzen et al, 2009;Sarma and Chen, 2009;Wang et al, 2009). They coupled the standard EnKF with typical methods such as kernel transform, discrete cosine transform, and level-set function.…”

Facies estimation through data assimilation and structure parameterization

“…Earlier work within sequential data assimilation for facies fields, seeking to overcome this challenge, include [2,12,18,19,23,24,31,33,37]. These approaches are briefly reviewed in the introduction section in [25,26].…”