The reservoir model-based forecast uncertainty reduction requires the integration of multiple sources of information. Among them, production data are of great value. For this reason, a methodology able to manage them within the history matching process to improve the model calibration process is highly recommended. The scope of the activity is then to set up a new workflow able to fully integrate Production Data Analysis (PDA) with an Ensemble History Matching (ENHM) workflow.
PDA outcomes represent evidence highlighted by the whole production history based on the collection, analysis, and integration of all available geological and dynamic data, such as injector-producer connections. A set of alternative realizations ("ensemble") needs to be created representing all the relevant uncertainties. Ensemble Screening is necessary to eliminate the non-PDA compliant realizations; comparing streamlines generated on the ensemble with the PDA outcomes and eliminating the non-representative realizations. Ensemble diagnostic tools can help to discriminate the ensemble consistency with the basic reservoir facts coming from PDA and which parameters or assumptions in the ensemble creation need to be revised because of the non-compatibility from a statistical point of view (like conflicting or insufficient parameterizations). The ensemble will be matched through the ENHM iterative process. The proposed workflow uses then the Fluid Path Conceptual Model (FPCM) derived from PDA, as a key driver to localize the model updates performed by the iterative ensemble process.
The proposed workflow allows obtaining a set of realizations representative of both the main geological and dynamical features of the field. This in turn will result in a higher predictive quality of the model-based forecasts. The performed tests allow us to conclude that PDA outcomes provide significant information regarding the fluid communications that can improve the ensemble reservoir parameterization reducing the reservoir uncertainties. Ensemble distance computation based on streamline attributes, like Time of Flight and streamline normalized fraction, can find similarities among realizations reflecting the connectivity patterns relevant to the PDA perspective. The evidence highlighted from PDA can be used as firm input in the ensemble realizations generation also impacting fundamental steps, such as the geological setup. Moreover, PDA can help to identify the main uncertainty parameters characterizing the field and suggests a reasonable range of variability to be considered within the ensemble approach.
Multiple ensemble diagnostic tools were developed to check the ensemble quality against PDA outcomes using different streamline attributes as a distance. Diagnostic tools, moreover, allow to identify a reduced number of model realizations representative of the ensemble variability on which run the forecast. The advantages of the proposed workflow can balance the unavoidable additional time with respect to standard ensemble history matching for its practical realizations on field cases, especially with many data and high model complexity.