Screening Geostatistical Realizations for SAGD Reservoir Simulation

Economic feasibility of bitumen recovery using the steam-assisted gravity drainage (SAGD) process strongly depends on the heterogeneities that exist in the petrophysical properties of the reservoir. Petrophysical properties and geological features are normally estimated based on known well data and modeled using geostatistical techniques. However, the statistical nature of the geostatistical methods yields many different possible distributions of reservoir properties. As these static models have the same spatial continuity and honor known data, they are called realizations. Flow simulation of these realizations results in translation of dissimilarities in the static models into a range of dynamic performance responses such as the fluid production rate, cumulative production, and recovery. One option to capture the entire range of dynamic responses is to conduct numerical simulation on all the realizations. Clearly, this is a laborious and impractical task particularly for SAGD, which is a computationally burdensome and complicated modeling process. Another alternative would be to screen and rank realizations prior to detailed flow simulation. In this research, some of the published connectivity-based ranking methods were applied to synthetic SAGD datasets. After testing the applicability and effectiveness of the existing methods, a new ranking measure based on the static reservoir properties was introduced. The method established a reliable measure to successfully rank realizations based on both cumulative oil production and steam injection. The ranking method was further validated by using two realistic SAGD datasets from the fields located in the northern Alberta with one of the datasets containing multiple well pairs in the model. Despite the extent and complexities of the real models, the method was demonstrated to be strongly correlated to SAGD reservoir performance indicators. Moreover, the correlation was stable over time as long as gravity drainage was dominated. The proposed method is robust in identifying extreme performing realizations, can be easily applied to large models, and runs much faster than the existing in-house ranking methods. It can categorize realizations with similar performance characteristics into one group and discern groups that exhibit different responses. In this way, a few representative realizations from each category can be selected for detailed flow simulation studies. This reduces the uncertainty assessment time for SAGD process from months to just a few days.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Chemical and Petroleum Engineering

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2014

Author dc:creator

• Majdi Yazdi, Mehdi

Advisor dc:contributor.advisor

• Jensen, Jerry Lee

• Engineering--Petroleum

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/27717

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:11023/1485