Description
Production models may offer several challenges since they involve data entry subject to errors and reservoir parameter uncertainties. These challenges exacerbate when user needs to take decisions on a continuous basis and whenever a new alternative scenario needs to be evaluated.
In this effort, we present a workflow to externally manipulate integrated production model parameters (PI, Pres, Equivalent choke ID, Choke Correction) to automate the history match tuning process to reduce the error between actual field measurements and simulated integrated production model composed of several reservoir tanks, wells and surface pipeline network. A “Tabu Scatter Search” optimizer engine selected the best iteration parameters in accordance to the objective function.
Lowest uncertainty model parameters were selected from the objective function global minimum; e.g. well productivity indexes, reservoir tanks pressures, equivalent choke diameter and choke correction parameters where determined as a result of this workflow. Tornado plots and IPM performance curves under uncertainty where generated as a result of this application as well. On a similar workflow, decision parameters such as well alignment, choke position and other constraints are used to maximize system’s performance.
In this effort, we present a workflow to externally manipulate integrated production model parameters (PI, Pres, Equivalent choke ID, Choke Correction) to automate the history match tuning process to reduce the error between actual field measurements and simulated integrated production model composed of several reservoir tanks, wells and surface pipeline network. A “Tabu Scatter Search” optimizer engine selected the best iteration parameters in accordance to the objective function.
Lowest uncertainty model parameters were selected from the objective function global minimum; e.g. well productivity indexes, reservoir tanks pressures, equivalent choke diameter and choke correction parameters where determined as a result of this workflow. Tornado plots and IPM performance curves under uncertainty where generated as a result of this application as well. On a similar workflow, decision parameters such as well alignment, choke position and other constraints are used to maximize system’s performance.