Natural and Conventional Tracers for Improving Reservoir Models Using the EnKF Approach

摘要

Natural tracers (geochemical and isotopic variations in injected and formation waters) are a mostly unused source of information in reservoir modeling. On the other hand, conventional interwell tracer tests are an established method to identify flow patterns. However, they are typically underexploited, and tracer-test evaluations are often performed in a qualitative manner and are rarely compared systematically to simulation results. To integrate natural-and conventional-tracer data in a reservoir-modeling workflow, we use the ensemble Kalman filter (EnKF), which has recently gained popularity as a method for history matching. The EnKF includes online update of parameters and the dynamical states. An ensemble of model representations is used to represent the model uncertainty. In this paper, we include conventional water tracers as well as natural tracers (i.e., geochemical variations) in the EnKF approach. The methodology is demonstrated by estimating permeability and porosity fields in a synthetic field case based on a real North Sea field example. The results show that conventional tracers and geochemical variations yield additional improvement in the estimates and that the EnKF approach is well suited as a tool to include in this process. The principal benefit from the methodology is improved models and forecasts from reservoir simulations, through optimal use of conventional and natural tracers. Some of the natural-tracer data (e.g., scale-forming ions and toxic compounds) are monitored for other purposes, and exploiting such data can yield significant reservoir-model improvement at a small cost.