A new method of data quality control in production data using the capacitance-resistance model

• Main Author: Cao, Fei, active 21st century
• Format: Others
• Language: English
• Published: 2011
• Subjects: Data quality; Quality control; CRM model; Capacitance resistance model; Reservoir simulation models; Production data; Injection
• Online Access: http://hdl.handle.net/2152/ETD-UT-2011-08-4311

Production data are the most abundant data in the field. However, they can often be of poor quality because of undocumented operational problems, or changes in operating conditions, or even recording mistakes (Nobakht et al. 2009). If this poor quality or inconsistency is not recognized as such, it can be misinterpreted as a reservoir issue other than the data quality problem that it is. Thus quality control of production data is a crucial and necessary step that must precede any further interpretation using the production data. To restore production data, we propose to use the capacitance resistance model (CRM) to realize data reconciliation. CRM is a simple reservoir simulation model that characterizes the connectivity between injectors and producers using only production and injection rate data. Because the CRM model is based on the continuity equation, it can be used to analyze the production corresponding to the injection signal in the reservoir. The problematic production data are then put into the CRM model directly and the resulting CRM output parameters are used to evaluate what the correct production response would be under current injection scheme. We also make sensitivity analysis based on synthetic fields, which are heterogeneous ideal reservoir models with imposed geology and well features in Eclipse. The aim is to show how bad data could be misleading and the best way to restore the production data. Using the CRM model itself to control data quality is a novel method to obtain clean production data. We can then apply the new clean production data in reservoir simulators or any other processes where production data quality matters. This data quality control process can help better understand the reservoir, analyze its behavior in a more ensured way and make more reliable decisions. === text