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.

Sour gas injection operation has been implemented in Tengiz since 2008 and will be expanded as part of a future growth project. Due to limited gas handling capacity, producing wells at high GOR has been a challenge, resulting in potential well shutdowns. The objective of this study was to establish an efficient optimization workflow to improve vertical/areal sweep, thereby maximizing recovery under operation constraints. This will be enabled through conformance control completions that have been installed in many production/injection wells. A Dual-Porosity and Dual-Permeability (DPDK) compositional simulation model with advanced Field Management (FM) logic was used to perform the study. Vertical conformance control was implemented in the model enabling completion control of 4 compartments per well. A model-based optimization workflow was defined to maximize recovery. Objective functions considered were incremental recovery 1) after 5 years, and 2) at the end of concession. Control parameters considered for optimization are 1) injection allocation rate, 2) production allocation rate, 3) vertical completion compartments for injectors and producers. A combination of different optimization techniques e.g., Genetic Algorithm and Machine-Learning sampling method were utilized in an iterative manner. It was quickly realized that due to the number of mixed categorical and continuous control parameters and non-linearity in simulation response, the optimization problem became almost infeasible. In addition, the problem also became more complex with multiple time-varying operational constraints. Parameterization of the control variables, such as schedule and/or FM rules optimization were revisited. One observation from this study was that a hybrid approach of considering schedule-based optimization was the best way to maximize short term objectives while rule-based FM optimization was the best alternative for long term objective function improvement. This hybrid approach helped to improve practicality of applying optimization results into field operational guidelines. Several optimization techniques were tested for the study using both conceptual and full-field Tengiz models, realizing the utility of some techniques that could help in many field control parameters. However, all these optimization techniques required more than 2000 simulation runs to achieve optimal results, which was not practical for the study due to constraints in computational timing. It was observed that limiting control parameters to around 50 helped to achieve optimal results for the objective functions by conducting 500 simulation runs. These limited number of parameters were selected from flow diagnostics and heavy-hitter analyses from the pool of original 800+ control parameters. The novelty of this study includes three folds: 1) The model-based optimization outcome obtained in this study has been implemented in the field operations with observation of increased recovery 2) the hybrid optimization of both schedule and operation rule provided practicality in terms of optimization performance as well as application to the field operation 3) provides lessons learned from the application of optimization techniques ranging from conventional Genetic Algorithm to Machine-Learning supported technique.

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.