Trapping structure of a conceptual grid

Vertically segregated CO₂ is found as a thin layer under the formation's top surface (or caprock). Some time after the end of injection, CO₂ becomes driven by buoyancy forces rather than by pressure. As such, the shape of the top surface greatly impacts long-term migration.

Here, we show a conceptual grid (left) and associated top surface (right). The local maxima of the undulating top surface create structural traps. Each trap has an associated catchment region, and are connected to other traps by "rivers". More details on the trapping structure can be found here.

Forecasting structural trapping

The middle figure shows a simulated volume of CO₂ after it has been injected into the formation. By continuing to simulate for 2000 years post-injection, we create an inventory that shows how much CO₂ becomes structurally trapped, leaks from the formation boundary, or remains as a free (mobile) plume over time.

From this trapping inventory (right), we see that about 60% of the injected CO₂ becomes structurally trapped, while the remaining 40% has either exited the formation or remains as a free moving plume. (Residual trapping was not considered in this example.) If we were to simulate a longer time horizon, we will find the free plume wedge gets reduced to nothing as it ultimately spills out of the formation.

We also perform a forecast at each simulation time step, to predict the amount that will be structurally trapped, and this forecast converges to the simulated solution after about 500 years. This means the forecast is able to predict the eventual fate of CO₂, and we don't need to simulate such a long time horizon to be able to capture the permantely stored volumes. 

Using forecast for optimizing the injection rate

In order to maximize the amount of CO₂ trapped within a formation, while penalizing the amount of leakage, we use a nonlinear optimization algorithm. The initial injection rate is computed based on the capacity of the traps that are upsteam from the well. In general, the optimized injection rate is higher than the initial rate because we end up exploiting more structural trapping (and/or other forms of trapping mechanisms) than initially estimated.

We obtain three different optimal solutions for Well 1's injection rate, which were found by simulating for three different time horizons: 500 years, 3000 years, and infinite time via forecasting. With a 500 year time horizon, the optimization algorithm correctly suggests the injection rate can be higher than the initial, however 500 years is not long enough to capture the long-term migration towards the formation boundary. A 3000 year time horizon suggests a lower rate, since more long-term leakage is captured and thus appropriately minimized. However, the infinite time horizon suggests a slightly lower rate, which implies that more than 3000 years is required to simulate the full extent of long-term leakage as part of the optimal solution.