Complex fracture networks, comprising hydraulic and preexisting natural fractures, play a pivotal role in characterizing flow dynamics and enhancing hydrocarbon production in low-permeability, low-porosity unconventional reservoirs. These networks function as the primary high-conductivity flow channels, as fluid flow through the matrix is minimal. Accurately representing their spatial distribution necessitates discrete fracture network (DFN) simulation methods, which treat individual fractures as distinct computational elements rather than averaging their properties into matrix grid blocks. This study introduces a comprehensive workflow for DFN generation, meshing, simulation, and result visualization, designed to improve the representation of complex fracture geometries and their impact on reservoir performance. A key innovation is integrating advanced refinement techniques with optimization-based meshing algorithms, creating high-quality unstructured perpendicular bisector grids that accurately capture fracture geometry and connectivity. The workflow is validated through two applications. The first uses an outcrop-based DFN model to investigate the effects of natural fracture properties on production. Sensitivity analyses reveal a positive correlation between natural fracture conductivity and oil production, with fracture permeability having a greater impact than width. The second employs synthetic DFN models to evaluate hydraulic fracturing strategies. Results demonstrate that non-uniform fracture designs consistently outperform uniform designs at the same stimulation cost, emphasizing the importance of tailored stimulation patterns to reduce undepleted regions and maximize stimulated reservoir volume. These findings highlight key factors controlling hydrocarbon recovery in shale plays, the limitations of uniform stimulation strategies in current practices, and advocate for customized approaches to optimize unconventional reservoir development.
A novel and reusable electrochemiluminescence biosensor was developed based on tetrahedral DNA (TDN) signal amplification for ultrasensitive detection of miRNA-27a. The flowered nickel-iron layered double hydroxide@AuNPs (NiFe-LDH@AuNPs) composites increase the amount of hairpin DNA fixed on the electrode. When miRNA is present, TDN-Ru(bpy)32+ acts as an ECL probe, forming a stable sandwich structure with miRNA-27a and hairpin DNA through base complementation pairing, thus achieving miRNA detection. This biosensor has the characteristics of high sensitivity, excellent selectivity, and good reproducibility.
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In this paper, we present a simulation case study of a surfactant huff ’n’ puff pilot in the black oil window of the Eagle Ford (EF) Shale. The target horizontal well, which had been depleted for nearly 8 years, underwent stimulation via a surfactant huff ’n’ puff treatment. The surfactant was selected through laboratory screening using reservoir rock and fluid samples. After a 17-hour injection and a 1-month shut-in period, the well’s production increased fivefold from the baseline oil rate, sustaining incremental oil production for at least 2 years. The surfactant enhances oil recovery by altering rock wettability toward a more water-wet state and moderating oil/water interfacial tension (IFT). This process is modeled by surfactant adsorption in the simulator, indicating the degree of dynamic changes in relative permeability (krl) and capillary pressure (Pc) curves. We propose a comprehensive workflow comprising three stages: development of core-scale and field-scale models, sequential model calibrations, and multiobjective optimization to integrate laboratory measurements and field data from this pilot into multiscale numerical simulations. By matching oil recoveries from imbibition experiments on the core model and field production histories on the field model, krl and Pc profiles of two extreme states, basic reservoir properties, and additional reservoir properties altered during huff ’n’ puff operations are characterized. The matched core model reproduces a 15.1% incremental oil recovery for surfactant-assisted spontaneous imbibition (SASI) process relative to pure brine imbibition process. The matched reservoir model predicts the surfactant huff ’n’ puff treatment increases the oil production by 21.9% relative to water huff ’n’ puff treatment and by 52.9% relative to primary depletion for a 4-year period. The calibrated reservoir model also serves as a base case for optimizing well operation schedules through the implementation of a multiobjective genetic algorithm. The surfactant injection rate, injection time, and well shut-in time of the base case are varied to achieve higher oil production and reduced surfactant usage. Statistical analysis of eight trade-off cases indicates that optimal well operations, compared with existing practices, frequently involve increased injection rates [16.6–18.9 barrels per minute (bpm)], shorter injection periods (10–11.3 hours), and prolonged shut-indurations (49–65 days). This workflow offers valuable insights into surfactant huff ’n’ puff treatments for unconventional reservoirs, thereby facilitating the optimization of well operations and maximizing tertiary oil recovery.
The development of enhanced strategies with excellent biocompatibility is critical for electrochemiluminescence (ECL) imaging of single cells. Here, we report an ECL imaging technique for a single cell membrane protein based on a Co3O4 nanozyme catalytic enhancement strategy. Due to the remarkable catalytic performance of Co3O4 nanozymes, H2O2 can be efficiently decomposed into reactive oxygen radicals, and the reaction with L012 was enhanced, resulting in stronger ECL emission. The anti-carcinoembryonic antigen (CEA) was coupled with nanozyme particles to construct a probe that specifically recognized the overexpressed CEA on the MCF-7 cell membrane. According to the locally enhanced visualized luminescence, the rapid ECL imaging of a single cell membrane protein was eventually realized. Accordingly, Co3O4 nanozymes with highly efficient activity will provide new insights into ECL imaging analysis of more biological small molecules and proteins.
In this review, we summarize and classify the signal output mode of DNA-based ECL biosensors and introduce different immobilization methods of DNA probes on electrodes.
A novel ECL immunosensor was developed for simultaneous determination of multiplex bladder cancer markers. DNA tetrahedra act as capture probes, while Ru-MOF@AuNPs and AuAgNCs act as signal reporters, yielding well-separated signals reflecting NUMA1 and CFHR1 concentrations. This strategy offers a new platform for clinical immunoassays, enabling simultaneous multiplex tumor marker detection.
Physics-informed neural networks (PINNs) integrate physical principles into machine learning, finding wide applications in various scientific and engineering fields. However, solving nonlinear hyperbolic partial differential equations (PDEs) with PINNs presents challenges due to inherent discontinuities in the solutions. This is particularly true for the Buckley–Leverett (B-L) equation, a key model for multiphase fluid flow in porous media. In this paper, we demonstrate that PINNs, in conjunction with Welge's construction, can achieve superior precision in handling the B-L equations in different scenarios including one shock and one rarefaction wave, two shocks connected by a rarefaction wave traveling in the same direction, and two shocks connected by a rarefaction wave traveling in opposite directions. Our approach accounts for variations in fluid mobility, fluid solubility, and gravity effects, with applications in modeling 1D water flooding, polymer flooding, gravitational flow, and CO2 injection into saline aquifers. Additionally, we applied PINNs to inverse problems to estimate multiple PDE parameters from observed data, demonstrating robustness under conditions of slight scarcity and up to 5% impurity of labeled data as well as shortages in collocation data.
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