Thomas, Sunil (Chevron Energy Technology Company) | Du, Song (Chevron Energy Technology Company) | Dufour, Gaelle (Chevron Energy Technology Company) | Mallison, Brad (Chevron Energy Technology Company) | Muron, Pierre (Chevron Energy Technology Company) | Rey, Alvaro (Chevron Energy Technology Company)
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 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.
Hui, Mun-Hong (Chevron Energy Technology Company) | Dufour, Gaelle (Chevron Energy Technology Company) | Vitel, Sarah (Chevron Energy Technology Company) | Muron, Pierre (Chevron Energy Technology Company) | Tavakoli, Reza (Chevron Energy Technology Company) | Rousset, Matthieu (Chevron Energy Technology Company) | Rey, Alvaro (Chevron Energy Technology Company) | Mallison, Bradley (Chevron Energy Technology Company)
Traditionally, fractured reservoir simulations use Dual-Porosity, Dual-Permeability (DPDK) models that can idealize fractures and misrepresent connectivity. The Embedded Discrete Fracture Modeling (EDFM) approach improves flow predictions by integrating a realistic fracture network grid within a structured matrix grid. However, small fracture cells with high conductivity that pose a challenge for simulators can arise and ad hoc strategies to remove them can alter connectivity or fail for field-scale cases. We present a new gridding algorithm that controls the geometry and topology of the fracture network while enforcing a lower bound on the fracture cell sizes. It honors connectivity and systematically removes cells below a chosen fidelity factor. Furthermore, we implemented a flexible grid coarsening framework based on aggregation and flow-based transmissibility upscaling to convert EDFMs to various coarse representations for simulation speedup. Here, we consider pseudo-DPDK (pDPDK) models to evaluate potential DPDK inaccuracies and the impact of strictly honoring EDFM connectivity via Connected Component within Matrix (CCM) models. We combine these components into a practical workflow that can efficiently generate upscaled EDFMs from stochastic realizations of thousands of geologically realistic natural fractures for ensemble applications.
We first consider a simple waterflood example to illustrate our fracture upscaling to obtain coarse (pDPDK and CCM) models. The coarse simulation results show biases consistent with the underlying assumptions (e.g., pDPDK can over-connect fractures). The preservation of fracture connectivity via the CCM aggregation strategy provides better accuracy relative to the fine EDFM forecast while maintaining computational speedup. We then demonstrate the robustness of the proposed EDFM workflow for practical studies through application to an improved oil recovery (IOR) study for a fractured carbonate reservoir. Our automatable workflow enables quick screening of many possibilities since the generation of full-field grids (comprising almost a million cells) and their preprocessing for simulation completes in a few minutes per model. The EDFM simulations, which account for complicated multiphase physics, can be generally performed within hours while coarse simulations are about a few times faster. The comparison of ensemble fine and coarse simulation results shows that on average, a DPDK representation can lead to high upscaling errors in well oil and water production as well as breakthrough time while the use of a more advanced strategy like CCM provides greater accuracy. Finally, we illustrate the use of the Ensemble Smoother with Multiple Data Assimilation (ESMDA) approach to account for field measured data and provide an ensemble of history-matched models with calibrated properties.
Embedded Discrete-Fracture Model (EDFM) is designed to accurately represent realistic hydraulic fracture network (HFN) and provide efficient performance predictions by honoring the fracture topology. Due to the complexity of HFN, the EDFM grid may be computationally inefficient, particularly for field-scale applications with millions of fracture cells. This paper aims at incorporating the Fast Marching Method (FMM) and spectral clustering for fast HFN analysis, simplification and simulation under the framework of EDFM.
HFNs are first generated using a commercial hydraulic fracture simulator. The FMM is used to solve the pressure front propagation using the fracture graph and subsequently the ‘diffusive time of flight’, well and completion index are calculated. The results are used as pre-conditions to split the fracture graph into connected components, which are subsequently partitioned using spectral clustering. The resulting clusters are used for fracture simplification resulting in a significantly lower number of fracture elements for flow simulation. To demonstrate the feasibility of the workflow, we use the Multi-Well Pad pilot model, which is characterized by a complex HFN and a high-resolution matrix system. We investigate the relationship between matrix resolution (characterized by the matrix-fracture size of the reservoir cells) and the ratio of oil and gas production on the field. Our investigation provides an alternative approach to explain the very large Gas Oil Ratio (GOR) reported for this type of reservoirs. The required levels of refinement to correctly represent the observed GOR presents an opportunity to test the efficiency and accuracy of our proposed workflow for HFN simplification. We use the results of the FMM applied to the high-resolution models to find an optimal spectral fracture clustering. The results show that the proposed workflow can achieve massive fracture cells aggregation (with only 1% of the original fracture cell number) while maintaining the accuracy.
This is the first study for analysis, simplification, and simulation of HFN for EDFM using a field scale model. The main contributions are: (i) honor the topology of complex HFNs in EDFM and is able to represent the complex physics observed in the oil and gas shale reservoirs, (ii) HFNs diagnosis without simulation, and (iii) massive fracture aggregation with an error below 5 percent, and speed-up higher than 16 times of the fine scale model.