In this paper we investigate truly multi-D upwind schemes for simulating adverse mobility ratio displacements in porous media. Due to an underlying physical instability at the simulation scale, numerical results are highly sensitive to discretization errors and hence the orientation of the underlying computational grid. We use modified equations analysis to predict preferred flow angles on structured grids for several popular methods and present a conservative, multi-D framework for designing positive upwind schemes for general velocity fields. After placing the common schemes in this framework, we go on to develop a novel scheme with "minimal" constant transverse (cross-wind) diffusion. Results for miscible gas injection into homogeneous and heterogeneous media demonstrate that truly multi-D schemes, and in particular our new scheme, greatly reduce grid orientation effects and numerical biasing as compared to dimensional upwinding.

Effective workflows for translating earth models into simulation models require grids that preserve geologic accuracy, offer flexible resolution control, integrate tightly with upscaling, and can be generated easily. Corner-point grids and pillar-based unstructured grids fail to satisfy these objectives; hence, a truly 3D unstructured approach is required. This paper describes unstructured cut-cell gridding tools that address these needs and improve the integration of our overall reservoir modeling workflows. The construction of simulation grids begins with the geologic model: a numerical representation of the reservoir structure, stratigraphy, and properties. Our gridding utilizes a geochronological (GeoChron) map from physical coordinates to an unfaulted and unfolded depositional coordinate system. The mapping is represented implicitly on a tetrahedral mesh that conforms to faults, and it facilitates accurate geostatistical modeling of static depositional properties. In the simplest use case, we create an explicit representation of the geologic model as an unstructured polyhedral grid. Away from faults and other discontinuities, the cells are hexahedral, highly orthogonal, and arranged in a structured manner. Geometric cutting operations create general polyhedra adjacent to faults and explicit contact polygons across faults. The conversion of implicit models to explicit grids is conceptually straightforward, but the implementation is nontrivial due to the limitations of finite precision arithmetic and the need to remove small cells formed in the cutting process. In practice, simulation grids are often constructed at coarser resolutions than earth models. Our implementation of local grid coarsening and refinement exploits the flexibility of unstructured grids to minimize upscaling errors and preserve critical geologic features. Because the simulation grid and the geologic model are constructed using the same mapping, fine cells can be nested exactly inside coarse cells. Therefore, flow-based upscaling can be applied efficiently without resampling onto temporary local grids. This paper describes algorithms and data structures for constructing, storing, and simulating cut-cell grids. Examples illustrate accurate modeling of normal faults, y-faults, overturned layers, and complex stratigraphy. Flow results, including a field sector model, show the suitability of cut-cell grids for simulation.

New developments in unstructured aggregation-based upscaling are presented that improve the flexibility of coarsening designs and enable a more integrated reservoir simulation workflow. Field cases and synthetic tests demonstrate the advantages of the method compared to legacy upscaling methods and fine scale simulations. Aggregation-based upscaling has recently emerged as a favorable alternative to conventional upscaling methods in reservoir simulation workflows. We outline these developments and describe algorithms used to compute flexible aggregation schemes, coarse transmissibility, and upscaled well indices. The main value additions are, the ability to selectively coarsen and adapt areal and vertical resolution based on geological features, areas of interest, and/or stratigraphic layer metrics resulting in improved accuracy, the improved simplicity and robustness resulting from avoiding the explicit creation of coarse grids and maintaining one grid for earth modeling and reservoir simulation workflows, and the broad applicability to fields modeled by many grid types including unstructured grids and discrete fracture models. The aggregation-based upscaling methodology is tested in the simulation of some synthetic benchmarks, and of full field models. Comparisons are provided to fine scale simulations in each case, and to legacy upscaling simulations, wherever practically feasible. The most important findings are the seamless integration afforded by the new workflow by eliminating the need for the coarse simulation grid, the significant savings in user interaction time and computational time, and the overall improvement in accuracy, when compared to legacy upscaling workflows. This is important because reservoir engineers operate on tight deadlines to complete projects, and because the logistical challenges of handling fine and coarse grids are significant for studies that involve multiple reservoir model realizations.

Natural fracture systems comprise numerous small features and relatively few large ones. At field scale, it is impractical to treat all fractures explicitly. We represent the largest fractures via Embedded Discrete Fracture Modeling (EDFM) and account for smaller ones using a dual-porosity, dual-permeability (DPDK) idealized representation of the fracture network. The hierarchical EDFM+DPDK approach uses consistent discretization schemes and efficiently simulates realistic field cases. Further speed-up can be obtained using aggregation-based upscaling. Capabilities to visualize and post-process simulation results facilitate understanding for effective management of fractured reservoirs. The proposed approach embeds large discrete fractures as EDFM within a DPDK grid (which contains both matrix and idealized fracture continua for smaller fractures), and captures all connections among the triple media. In contrast with existing EDFM formulations, we account for discrete fracture spacing within each matrix cell via a new matrix-fracture transfer term and employ consistent assumptions for classical EDFM and DPDK calculations. In addition, the workflow enables coarse EDFM representations using flow-based cell-aggregation upscaling for computational efficiency, as well as finite-volume tracer-based flux post-processing to analyze production allocation and sweep. Using a synthetic case, we show that the proposed EDFM+DPDK approach provides a close match of simulation results from a reference model that represents all fractures explicitly, while providing runtime speedup. It is also more accurate than previous standard EDFM and DPDK models. We demonstrate that the matrix-fracture transfer function agrees with flow-based upscaling of high-resolution fracture models. Next, the automated workflow is applied to a waterflooding study for a giant carbonate reservoir, with an ensemble of stochastic fracture realizations. The overall workflow provides the computational efficiency needed for performance forecasts in practical field studies, and the 3D visualization allows for the derivation of insights into recovery mechanisms. Finally, we apply a flux post-processing scheme on simulation results to understand expected waterflood performance.

The establishment of development pipelines for innovative technologies is a familiar aspect of digital transformation within the energy industry. One variant relies on innovation teams to rapidly prototype ideas as Proofs-of-Concept (POC) that, when successful, are matured and commercialized as new technical solutions within product lines. However, as the number of ideas grows, diversity of in-scope energy systems broadens, and resources remain constrained, identifying the highest-value ideas aligned with innovation goals and enterprise strategy has become paramount. We outline a prioritization and selection (PAS) approach founded on systems engineering (SE) to manage the work progressed within an innovation team. Specifically, we adapt the tradespace methodology for design selection when stakeholder needs and project constraints create a multi-objective optimization problem. The assessment combines a rigorous stakeholder analysis and surveys to characterize a multi-attribute utility function measuring POC benefit. POC resources are estimated from anticipated duration, development needs, validation requirements, and process change required by the technical solutions. These metrics characterize cost-benefit trade-offs, complemented by innovation measures associated with each POC. The final end-to-end workflow enables innovation idea comparisons with a dashboard to guide POC selection, portfolio shaping, and work prioritization across multiple energy disciplines and industry asset classes.