Reservoir, Production and Operations Engineers utilize integrated production models (IPM) to understand and optimize performance in reservoirs, wells and pipeline networks. This often requires an initial calibration followed by subsequent updates.

IPM calibration may offer several challenges since it involves non-unique solutions for reservoirs, wells and pipeline network parameters. In addition, suspect results can occur from manual data entry, uncertainties in subsurface parameters, subjective interpretation and poor quality data. These challenges are exacerbated when the user needs to repeat the procedure periodically (e.g. every month) or when new alternative scenarios require subsequent evaluation.

In this work, a consistent and systematic procedure was developed to assist in the calibration of an IPM under the presence of uncertainties. The procedure was successfully applied to a subsea Gulf of Mexico (GOM) field which consists of several reservoirs, wells, sub-sea pipeline network and surface equipment.

The IPM calibration is based on a workflow engine that adjusts IPM parameters to minimize an error function. The workflow engine is used to couple the IPM to a spreadsheet and an optimizer. The spreadsheet computes an objective function (weighted error function of measured vs. simulated results for each IPM variable) and the optimizer follows a gradient-free taboo and scatter search algorithm. Finally, IPM parameters (e.g. reservoir tank pressures, well productivity indexes (PI), pipeline internal diameter, roughness, equivalent choke diameter and choke correction) are selected from the objective function global minimum.

The calibration procedure was also tested by different users leading to similar results. The workflow engine permitted the evaluation of thousands of IPM scenarios in a few days, which would be impossible to do manually. The calibrated IPM predicted field production rates within 4.2% of the actual performance. The procedure developed in this work can be extended to deal with any combination of well, reservoir and pipeline models.

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