As polymer injection has not reached the same maturity as waterflooding, implementing polymer injection projects at field scale requires a workflow comprising screening of the portfolio of an organization for oil fields potentially amenable for polymer injection, laboratory and field testing followed by sector- and field implementation and roll-out in the portfolio.
Going through the workflow, not only the subsurface uncertainty is reduced but also the knowledge about the cost structure and operating capabilities of the organization improved.
Analyzing the economics of polymer injection projects shows that costs can be split into polymer injector-producer (polymer pattern) dependent and independent costs. Knowing these costs, a Minimum Economic Number of Patterns (MENP) is defined to achieve Net Present Value zero. This number is used to determine a Minimum Economic Field Size (MEFS) for polymer injection which is taken into account in the screening of the portfolio.
Defining a robustness criterion for economics, the minimum number of patterns for polymer injection meeting this criterion is calculated. This criterion is applied to generate a diagram allowing for screening of fields for polymer economics using pattern dependent and pattern independent costs and Utility Factor.
The cost structure reveals how the NPV of polymer projects changes with number of patterns, incremental oil and injectivity. Injectivity is of particular importance as it determines the Chemical Affected Reservoir Volume (CARV) or speed of production.
A sensitivity analysis of the NPV showed that for the cost structure used here, in addition to the polymer costs, the well costs are important for the economics of a full-field polymer injection project.
The polymer pilot project performed in the 8 TH reservoir of the Matzen field showed encouraging incremental oil production. To further improve the understanding of recovery effects resulting from polymer injection, an extension of the pilot is planned by adding a second polymer injector.
Forecasting of the incremental oil production needs to take the uncertainty of the geological models and dynamic parameters into account. We propose a workflow which comprises a geological sensitivity and clustering step followed by a dynamic calibration step for decreasing the objective function to improve the reliability of a probabilistic forecast of the incremental oil recovery.
For the geological sensitivity, hundreds of geological realizations were generated taking the uncertainty in the correlation of the sand and shale layers, logs, cores and geological facies into account. The simulated tracer response was used as dissimilarity distance to classify the geological realizations. Clustering was then applied to select 70 representative realizations (centroids) from a total of 800 to use in the full-physics dynamic simulation.
In the dynamic simulation, an objective function comprising liquid rate and tracer concentration of the back-produced fluids was introduced.
To further improve the calibration, the P50 value of incremental oil production as derived from simulation was compared with the incremental oil production determined from Decline Curve Analysis from the wells surrounding the polymer injection well. The mismatch between the P50 and the Decline Curve Analysis was improved by adjusting polymer viscosity.
The calibrated models were then used to for a probabilistic forecast of incremental oil due to an additional polymer injector and to estimate the expected polymer concentration at the producing wells.