Tuning of oil well models with production data reconciliation

The daily optimization of oil production systems relies on models to simulate phenomena of interest, such as fluid flow in wells and risers. Keeping models up to date is no easy task, due to the complex nature of the processes, and the need of human intervention to tune simulators. This work contributes by taking advantage of real-time measurements, to adjust process parameters that play a key role in steady-state simulators, e.g. the basic sediment and water and gas-oil ratio, and thereby optimize production for the prevailing conditions. The methodology consists of: (i) a strategy to identify steady-state of process variables (i.e., flows and pressures); (ii) an optimization formulation to adjust the key parameters such that the predicted total flows from the wells match the measured platform streams, while honoring pressure and flow measurements at critical points. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The daily optimization of oil production systems relies on models that simulate fluid flow in wells and risers. This study introduces a methodology using real-time measurements to adjust key parameters and optimize production for current conditions. The strategy involves identifying steady-state process variables and optimizing parameters to match predicted total flows with measured streams.