A well-designed pilot is instrumental in reducing uncertainty for the full-field implementation of improved oil recovery (IOR) operations. Traditional model-based approaches for brown-field pilot analysis can be computationally expensive as it involves probabilistic history matching first to historical field data and then to probabilistic pilot data. This paper proposes a practical approach that combines reservoir simulations and data analytics to quantify the effectiveness of brown-field pilot projects.
In our approach, an ensemble of simulations are first performed on models based on prior distributions of subsurface uncertainties and then results for simulated historical data, simulated pilot data and ob jective functions are assembled into a database. The distribution of simulated pilot data and ob jective functions are then conditioned to actual field data using the Data-Space Inversion (DSI) technique, which circumvents the difficulties of traditional history matching. The samples from DSI, conditioned to the observed historical data, are next processed using the Ensemble Variance Analysis (EVA) method to quantify the expected uncertainty reduction of ob jective functions given the pilot data, which provides a metric to ob jectively measure the effectiveness of the pilot and compare the effectiveness of different pilot measurements and designs. Finally, the conditioned samples from DSI can also be used with the classification and regression tree (CART) method to construct signpost trees, which provides an intuitive interpretation of pilot data in terms of implications for ob jective functions.
We demonstrate the practical usefulness of the proposed approach through an application to a brown-field naturally fractured reservoir (NFR) to quantify the expected uncertainty reduction and Value of Information (VOI) of a waterflood pilot following more than 10 years of primary depletion. NFRs are notoriously hard to history match due to their extreme heterogeneity and difficult parameterization; the additional need for pilot analysis in this case further compounds the problem. Using the proposed approach, the effectiveness of a pilot can be evaluated, and signposts can be constructed without explicitly history matching the simulation model. This allows ob jective and efficient comparison of different pilot design alternatives and intuitive interpretation of pilot outcomes. We stress that the only input to the workflow is a reasonably sized ensemble of prior simulations runs (about 200 in this case), i.e., the difficult and tedious task of creating history-matched models is avoided. Once the simulation database is assembled, the data analytics workflow, which entails DSI, EVA, and CART, can be completed within minutes.
To the best of our knowledge, this is the first time the DSI-EVA-CART workflow is proposed and applied to a field case. It is one of the few pilot-evaluation methods that is computationally efficient for practical cases. We expect it to be useful for engineers designing IOR pilot for brown fields with complex reservoir models.
Due to numerical difficulties in conducting high fidelity simulation of recovery mechanisms in complex natural fracture systems, there are no published studies that address the impact of preserving details of the fracture networks. We used highly refined grids to conduct fine scale simulations of various recovery mechanisms in different complex fracture settings and compared the results to those obtained on simplified dual porosity dual permeability (DPDK) representations created by applying a consistent upscaling procedure.
Our study considers densely connected, sparsely connected, and isolated fracture networks that are extracted from a field-scale fractured carbonate reservoir model. Discrete fracture-matrix (DFM) models were constructed using an unstructured grid with refinement of the matrix rock near fractures. High-resolution simulations of spontaneous imbibition, gravity drainage, and viscous displacement recovery mechanisms were conducted on these DFM models. We also built equivalent DPDK models by using single phase flow-based upscaling and actual fracture geometry and distribution. The recovery mechanisms were simulated on these DPDK models and compared to high-resolution DFM models.
The fine scale simulations revealed that lateral viscous displacement recovery depends on the details of the fracture networks and can be significantly higher than those predicted from equivalent DPDK models. The DPDK models all predict the same recovery. For spontaneous imbibition, both fine scale and equivalent DPDK models show dependence on fracture geometry, but the DPDK models predict much higher rates. Fine scale and equivalent DPDK models agree reasonably for gravity drainage. These findings are explained by analyzing the matrix-fracture flows, and implications on efforts to improve shape factors in DPDK models and upscaling efforts in DFM models are discussed.