Practical Considerations when Using Capacitance Resistance Modelling CRM for Waterflood Optimization

Abstract

Capacitance resistance modeling (CRM) is a data-driven analytical technique for waterflood optimization developed in the early 2000s. The popular implementation uses only production/injection data as input and makes simplifying assumptions of pressure maintenance and injection being the primary driver of production. While these assumptions make CRM a quick plug & play type of technique that can easily be replicated between assets they also lead to major pitfalls, as these assumptions are often invalid. This study explores these pitfalls and discusses workarounds and mitigations to improve the reliability of CRM. CRM was used as a waterflood optimization technique for 3 onshore oil fields, each having 100s of active wells, multiple stacked reservoirs, and over 15 years of pattern waterflood development. The CRM algorithm was implemented in Python and consists of 4 modules: 1) Connectivity solver module – where connectivity between injectors and producers is quantified using a 2 year history match period, 2) Fractional Flow solver module – where oil rates are established as a function of injection rates, 3) Verification module – which is a blind test to assess history match quality, 4) Waterflood optimizer module – which redistributes water between injectors, subject to facility constraints and estimates potential oil gain. Additionally, CRM results were interpreted and validated using an integrated visualization dashboard. The two main issues encountered while using CRM in this study are 1) poor history match (HM) and 2) very high run time in the order of tens of hours due to the large number of wells. Poor HM was attributed to significant noise in the production data, aquifer support contributing to production, well interventions such as water shut-offs, re-perforation, etc. contributing to oil production. These issues were mitigated, and HM was improved using data cleaning techniques such as smoothening, outlier removal, and the usage of pseudo aquifer injectors for material balance. However, these techniques are not foolproof due to the nature of CRM which relies only on trends between producers and injectors for waterflood optimization. Runtime however was reduced to a couple of hours by breaking up the reservoir into sectors and using parallelization.