Integration of 4D Seismic in Steam-Assisted-Gravity-Drainage Reservoir Characterization

Hadavand, Mostafa (University of Alberta) | Carmichael, Paul (ConocoPhillips Canada) | Dalir, Ali (ConocoPhillips Canada) | Rodriguez, Maximo (ConocoPhillips Canada) | Silva, Diogo F. S. (University of Alberta) | Deutsch, Clayton Vernon (University of Alberta)

OnePetro 

Mostafa Hadavand, University of Alberta; Paul Carmichael, Ali Dalir, and Maximo Rodriguez, ConocoPhillips Canada; and Diogo F. S. Silva and Clayton V. Deutsch, University of Alberta Summary 4D seismic is one of the main sources of dynamic data for heavy-oil-reservoir monitoring and management. Thus, the large-scale nature of fluid flow within the reservoir can be evaluated through information provided by 4D-seismic data. Such information may be described as anomalies in fluid flow that can be inferred from the unusual patterns in variations of a seismic attribute. During steam-assisted gravity drainage (SAGD), the steam-chamber propagation is fairly clear from 4D-seismic data mainly because of changes in reservoir conditions caused by steam injection and bitumen production. Anomalies in the propagation of the steam chamber reflect the quality of fluid flow within the reservoir. A practical methodology is implemented for integration of 4D seismic into SAGD reservoir characterization for the Surmont project. Introduction One of the main objectives in petroleum-reservoir modeling is to predict the future performance of the reservoir under a recovery process. It is not possible to establish the true spatial distribution of reservoir properties using limited data. Thus, the modeling process is ill-posed and subject to uncertainty (Pyrcz and Deutsch 2014). Geostatistical simulation provides a framework to quantify geological uncertainty that is represented by multiple equally probable realizations of the reservoir model. The uncertainty can be reduced by integration of all available sources of data, including static and dynamic (time-variant) data. However, each source of data provides information at different scales and levels of precision. Although there are well-established geostatistical techniques to generate stochastic realizations of the reservoir conditioned to static data, such as local measurements from wells and 2D/3D-seismic data, effective integration of dynamic data remains a major challenge. Time-lapse seismic, or 4D seismic, is one of the main dynamic sources of data for heavy-oil-reservoir monitoring and management. It contains valuable information regarding fluid movement, temperature, pressure buildup, and quality of fluid flow within the reservoir during a recovery process (Lumley and Behrens 1998; Gosselin et al. 2001). For SAGD, the evolution of the steam chamber over time is fairly clear in 4D-seismic images.