We employ the Discrete Element Method to analyze the micromechanical response of numerical analogs of sandstone and granite to unstable failure. Calibrated particle‐based models of sandstone and granite are subjected to biaxial experiments under confining pressures of 0–50 MPa, leading to the development of shear fractures through interactions of microcracks occurring in shear and tensile modes. We document the mode and energy associated with emergent microcracks to analyze fracture growth patterns and quantify fracture energy. Shear fracture growth in our sandstone analog occurs through cooperative interaction between shear and tensile microcracks, with shear microcracks accounting 4–44% of total microcracks and 31–92% of fracture energy. Shear microcracking increases with confining pressure resulting in an increase in fracture energy, and a transition from dilatant to compactant fracture zones in our sandstone models. Shear fracture growth in our granite analog occurs through coalescence of tensile microcracks, which account for 96–98% of total microcracks and 87–93% of fracture energy. Tensile microcracking increases with confining pressure, resulting in an increase in fracture energy and formation of dilatant fracture zones in our granite models. Our simulations show that fracture energy increases with confining pressure, accounting for 10–15% of the total input mechanical energy in sandstone versus 16–47% in granite. We estimate that the work done against friction from intergranular and fracture sliding accounts for 69–86% of total input energy in our sandstone analogs and 46–81% in our granite analogs. Our results indicate that frictional deformation during fracture is a significant component of the energy budget.

We model mudstone permeability during consolidation and grain rotation, and during fluid injection by simulating porous media flow using the lattice Boltzmann method. We define the mudstone structure using clay platelet thickness, aspect ratio, orientation, and pore widths. Over the representative range of clay platelet lengths (0.1–3 μm), aspect ratios (length/thickness = 20–50), and porosities (ϕ = 0.07–0.80) our permeability results match mudstone datasets well. Homogenous kaolinite and smectite models document a log linear decline in vertical permeability from 8.31 × 10−15–6.84 × 10−17 m2 at ϕ = 0.76–0.80 to 6.33 × 10−19–1.30 × 10−23 m2 at ϕ = 0.14–0.16, showing good correlation with experimental data (R2 = 0.42 and 0.56).We employ our methodology to predict the permeability of two natural mudstone samples composed of smectite, illite, and chlorite grains. Over ϕ = 0.32–0.58, the permeability trends of two models replicating the mineralogical composition of the natural mudstone samples match experimental datasets well (R2 = 0.78 and 0.74). We extend our methodology to evaluate how vertical permeability might evolve during microfracture network growth or macrofracture propagation upon fluid injection in compacted mudstone. Fluid injection results in a permeability increase from 1.02 × 10−20 m2 at ϕ = 0.07 to 2.07 × 10−16 m2 at ϕ = 0.29 for growth of a microfracture network, and from 1.02 × 10−20 m2 at ϕ = 0.07 to 1.23 × 10−16 m2 at ϕ = 0.32 for macrofracture propagation. Our results suggest that a distributed microfracture network results in greater permeability during fluid injection in compacted mudstones (ϕ = 0.07–0.32) in comparison to a wide macrofracture. Our modeling approach provides a simple means to estimate permeability during burial and compaction or fluid injection based on knowledge of porosity and mineralogy.

Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources. A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.