--> Enhance Reservoir Knowledge through “Big-Loop” Sensitivity Screening

AAPG Annual Convention and Exhibition

Datapages, Inc.Print this page

Enhance Reservoir Knowledge through “Big-Loop” Sensitivity Screening

Abstract

The building of a reliable and up-to-date reservoir model can be a challenging task as the limited available data often allows various interpretations and commonly requires many manual modeling steps. In addition, examining the possible geological scenarios and the uncertainty span of key parameters of the model is essential to optimize well planning, drainage strategy and reservoir forecasting. A method to estimate the likelihood and impact of input constraints on the model is to utilize production profiles and history data in the screening process. The lack of both methodology and tools for integration of data into a consistent and automated modeling chain, however, makes such analyses difficult in practice. To enable a consistent, repeatable and updateable model chain from depth conversion to flow simulation, an integrated cross-disciplinary approach to reservoir modeling is required. The objective of our work has been to develop and demonstrate such an integrated workflow that enables fast screening of sensitive key parameters and geological scenarios to improve reservoir understanding and history matching. The principal objective of the modeling engine is to create an ensemble of equiprobable realizations while keeping consistency between the static and dynamic properties. Hence, production profiles, simulated 4D time-shifts and summary maps (e.g. oil in-place, facies probability, etc.) can be generated for each realization and compared to history and observed data as well as to each other to examine the likelihood and impact of the various input constraints on the models. To facilitate an efficient use of multiple realizations each modeling step is run automatically as a batch process by an in-house developed workflow manager. For this paper the proposed integrated workflow will be demonstrated using examples of sub-surface reservoirs. Several uncertain input constraints are examined to enhance our reservoir understanding through consistent big-loop sensitivity screening of the e.g.: 1) impact of structural uncertainty on both faults and horizons, 2) initial locations of the OWC, 3) geobodies direction and continuity, 4) facies probability cubes, 5) communication between zones, 5) facies volume fractions, 6) 4D time-shifts effects, and 7) fault seal.