Reservoir Characterization of a Fractured Reservoir Using Automatic History Matching

1993 ◽  
Author(s):  
R.A.W. Smith ◽  
T.B. Tan
Keyword(s):  
History Matching ◽  
Reservoir Characterization ◽  
Fractured Reservoir ◽  
Automatic History Matching

Crosswell seismic study in a seismically poor data area

Geophysics ◽  
1995 ◽  
Vol 60 (3) ◽  
pp. 758-764 ◽  
Author(s):  
Doo Sung Lee ◽  
Spyros K. Lazaratos ◽  
Arthur F. Walden
Keyword(s):  
Reservoir Characterization ◽  
Poor Quality ◽  
Mapping Technique ◽  
Fractured Reservoir ◽  
Traveltime Tomography ◽  
Brittle Rocks ◽  
Major Controlling Factor ◽  
Reflection Image ◽  
Quality Surface

Crosswell traveltime tomography and reflection imaging assisted a reservoir characterization effort in an area of poor‐quality surface seismic data. Both the tomogram and the reflection image proved useful in the description of the fractured reservoir interval. The velocity tomogram shows that: (1) the vertical resolution was sufficient to identify and characterize a 50-ft (15 m) thick lithological unit of brittle rocks, which was the most important interval for the characterization of this fractured reservoir; (2) different lithological units present sufficient velocity contrast to be identifiable on the tomogram; and (3) the tomogram velocity is higher than the sonic velocity implying that the rocks in the interwell area may be anisotropic. Correlation of the lithologies with the tomogram implies that the major controlling factor of the anisotropy is the shale content in the formation. The crosswell reflection image, generated by a VSP‐CDP mapping technique, defines the fractured reservoir interval in terms of high‐frequency reflections. The lateral resolution of this reflection image is difficult to define because the survey coverage is nonuniform as a result of the receiver spacing being much larger than the source spacing. The dips of the reflections do not quite agree with the dips that are inferred from well log ties. We believe this disagreement is a result of the anisotropy of the medium and the use of an isotropic imaging algorithm. Improved data acquisition (finer spatial sampling) that would allow better wavefield separation techniques to be used would probably have produced higher quality crosswell reflection images.

Assessing the Uncertainty in Reservoir Description and Performance Predictions With the Ensemble Kalman Filter

SPE Journal ◽  
2007 ◽  
Vol 12 (03) ◽  
pp. 382-391 ◽  
Author(s):  
Mohammad Zafari ◽  
Albert Coburn Reynolds
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Reservoir Characterization ◽  
Reservoir Performance ◽  
Reservoir Models ◽  
Performance Predictions ◽  
And Performance ◽  
Reservoir Description

Summary Recently, the ensemble Kalman Filter (EnKF) has gained popularity in atmospheric science for the assimilation of data and the assessment of uncertainty in forecasts for complex, large-scale problems. A handful of papers have discussed reservoir characterization applications of the EnKF, which can easily and quickly be coupled with any reservoir simulator. Neither adjoint code nor specific knowledge of simulator numerics is required for implementation of the EnKF. Moreover, data are assimilated (matched) as they become available; a suite of plausible reservoir models (the ensemble, set of ensemble members or suite or realizations) is continuously updated to honor data without rematching data assimilated previously. Because of these features, the method is far more efficient for history matching dynamic data than automatic history matching based on optimization algorithms. Moreover, the set of realizations provides a way to evaluate the uncertainty in reservoir description and performance predictions. Here we establish a firm theoretical relation between randomized maximum likelihood and the ensemble Kalman filter. Although we have previously generated reservoir characterization examples where the method worked well, here we also provide examples where the performance of EnKF does not provide a reliable characterization of uncertainty. Introduction Our main interest is in characterizing the uncertainty in reservoir description and reservoir performance predictions in order to optimize reservoir management. To do so, we wish to generate a suite of plausible reservoir models (realizations) that are consistent with all information and data. If the set of models is obtained by correctly sampling the pdf, then the set of models give a characterization of the uncertainty in the reservoir model. Thus, by predicting future reservoir performance with each of the realizations, and calculating statistics on the set of outcomes, one can evaluate the uncertainty in reservoir performance predictions.

Sign in / Sign up

Export Citation Format

Share Document