This paper revisits classic flood surveillance methods applied to injection-production data, and how such methods can be improved using modern streamline-based calculations. Classic surveillance relied on fixed patterns and geometric based well-rate allocation factors (WAF's). Here we compare conclusions about pattern performance from classic surveillance calculations against surveillance results using flow-based WAF's as computed from a streamline surveillance model. We show that very different conclusions on pattern performance can be reached. Rather then fixed patterns, we also introduce injector centered patterns as the elementary surveillance unit with offset producers being those that an injector is connected to at any time event. Injector centered varying patterns give a better measure of an injector's true effectiveness since all offset oil it is responsible for is counted, rather than just production within a pre-defined pattern. Here too we show that decisions about how to manage a flood can be quite different than decisions reached using a fixed pattern analysis.

In the second part of this paper we illustrate how much data is required to build a relevant surveillance model. We compare WAF's and offset oil production as computed from full history-matched flow simulation models against surveillance models for several field cases. Our comparisons show that as long as offset well rates are a function of neighboring well rates, as is typical in waterfloods, then only first order flow effects are required in a surveillance model. First order flow effects required would include, well locations, historical rates, gross geological bodies and major flow barriers. In other words, because well rates are already a reflection of geology, simply setting up a surveillance model with the proper geological connectedness will lead to streamline models that give similar inter-well fluxes, as more complex history matched simulation models.

You can access this article if you purchase or spend a download.