Yeh, T. (Shell International E&P) | Uvieghara, T. (Shell Nigeria E&P Co. Ltd.) | Jennings, J. W. (Shell International E&P.) | Chen, C. (Shell International E&P.) | Alpak, F. O. (Shell International E&P.) | Tendo, F. (Shell Nigeria E&P Co. Ltd.)
Reliable reservoir uncertainty estimation is crucial to understanding its valuation and making robust decisions. Conventional practices where history matching and production forecast are performed on selected high-mid-low cases do not provide a reliable estimation of forecast uncertainty. This is typically reflected in a narrow range for ultimate recovery (UR) or net present value (NPV) predictions. In order to capture the inherent subsurface uncertainty, it is necessary to use an ensemble of models which spans the full uncertainty space.
The Probabilistic History Matching (PHM) workflow is an ensemble-based workflow, aimed to improve forecasting uncertainty estimation. One of the biggest challenges is that it typically requires a large ensemble size to span the uncertainty space due to the limited information (e.g. core data or well logs). This requirement may render the Assisted History Matching (AHM) exercise infeasible when computational resources are relatively limited. Therefore, it is necessary to reduce the number of models to a manageable size prior to performing AHM. Here we implement the Dynamic Fingerprinting workflow, to effectively select a set of representative models from the ensemble while preserving the uncertainty of the variables of interest. In this methodology, time-of-flight (TOF) and drainage time (DRT) information, which are direct estimates of swept and undrained volumes are used to characterize each model. A small subset of models is then selected based on their dissimilarity in flow pattern and used for AHM/forecasting.
The workflow was applied to a deepwater West Africa reservoir. An ensemble of 810 models was generated to represent the subsurface uncertainty. Ten models which were highly dissimilar in flow response were selected from the ensemble for AHM and estimating forecast uncertainties. The AHM was performed using an Experiment Design (ED) - Response Surface Modeling (RSM) - Markov-Chain Monte-Carlo (MCMC) workflow. For validation purposes, a different AHM workflow was performed on each of the 810 models using a derivative-free optimization algorithm. The comparison between the results supports the choice of the representatives from the Dynamic Fingerprinting work flow as well as the history matching conclusions from the ED-RSM-MCMC workflow.