Abstract
With the advances in data-driven methods, they have become more widely-used in analysis, predictive modeling, control and optimization of several processes. Yet, as it is a relatively new area in petroleum industry with promising features, the industry overall is still skeptical on use of data-driven methods as it is a data-based solution rather than traditional physics-based solutions. In this sense, in order to shed light on the background and applications in this area, this study comparatively evaluates one of the methods used in waterflood surveillance and optimization called capacitance-resistance model illustrated on two types of mature fields with high and low-perm characteristics.
Data-driven methods serve as a robust tool to turn data into knowledge. Historical data generally has not been used in an effective way in analyzing processes due to lack of a well-organized data where there is a huge potential of turning terrabytes of data into knowledge. A capacitance-resistance model is built to identify the well connectivities between the wells and then carry that knowledge to better reservoir management through optimization of injection and production in two different sets of data.
In CRM modeling, analysis of injection/production data at associated injectors and producers reveals the connectivities and further optimization leads to optimum injection values. Steps and the methodology of building a CRM model using real data is illustrated to exemplify the whole process in a comparative way between two mature reservoirs. We introduce the concept of application of spatial constraints in terms of injection-producer maximum influence radius to accelerate and improve the solution where knowledge of radius of influence for an injector is known by historical data and experience.
The theoretical and practical information is supported with mature field examples to investigate the factors affecting the performance of vertical wells in tight and intermediate-permeability reservoirs along with the outline of the major challenges and how to solve them. This study also illustrates the challenges of application of CRM on a tight reservoir in the order of 0.1md and comparison of the application of the method on a more intermediate-perm reservoir. Field data used in this study is from publicly available, open access source, Division of Oil, Gas & Geothermal Resources (DOGGR) website - http://www.conservation.ca.gov/dog
