Search Results (105)

This study proposes a useful multi-criteria optimization approach for defining the proper fabrication factors for the CNC milling process on the inclined surfaces of SKD 11 material. The method is to be used in mold fabrication technology within the field of mechanical engineering. A combination technique of the Taguchi technique (TM), response surface method (RSM), and Lichtenberg optimization algorithm (LA) was proposed to optimize the fabrication factors for enriching the superiority attributes. In the first stage, several initial experiments of the fabricating parameters were generated by the TM. Secondly, the mathematical equations among the main fabricating parameters, the surface roughness, the flatness, and the CNC milling time were then established by the RSM. Significant influences of fabrication elements on surface roughness, flatness, and CNC milling time were evaluated by variance analysis and sensitivity analysis based on three distinct CNC milling toolpath strategies. Finally, the Lichtenberg optimization algorithm was carried out based on regression equations to define the optimized factors for three cutting strategies. The optimized results showed that the reverse CNC milling toolpath strategy was the best for achieving the three quality responses. Furthermore, the results demonstrated that the inaccuracies among optimized as well as experiment confirmations for the surface roughness, flatness and CNC milling time were 6.54%, 18.182% and 11.972%, respectively. The verifications of experiment results were relatively suitable with the anticipated consequences. The outcomes reveal that an integration optimization methodology is a successful approach to tackling the multi-objective optimal problem of determining the best CNC milling parameters for the cartwheel specimen made of SKD11 material in injection mold technology. It can also be expanded to apply to complicated multi-criteria optimization problems. Full article

To address the challenges of strong tectonic stress anisotropy, multi-scale pore networks, and complex seepage pathways in the tight sandstone reservoirs of the Shaximiao Formation, southern Sichuan Basin, this study integrates petrophysical analysis with machine learning techniques to develop an intelligent flow unit identification methodology applicable to complex structural settings. Based on core petrophysical properties, mercury injection capillary pressure (MICP) data, and production dynamics, the reservoirs were classified into a fracture-type plus four conventional-type (I–IV) flow unit system. Quantitative identification of flow units was achieved using conventional well-logging curves (Gamma Ray, Spontaneous Potential, Caliper, etc.—eight curves total) using the Gradient Boosting Decision Tree (GBDT), Backpropagation Neural Network (BPANN), and Supervised Self-Organizing Map (SSOM) algorithms. Key findings include the following: The SSOM algorithm delivered optimal performance, achieving a 90.1% average accuracy on the test set, significantly outperforming GBDT (87.8%) and BPANN (85.5%), particularly in capturing nonlinear responses of fracture-type reservoirs and class-overlapping samples. Flow unit spatial distribution exhibits dual sedimentary-structural control: High-quality units (Types I/II) are enriched at the base of distributary channels in deltaic plain facies (J₂S1²), while fracture-type units cluster near fault peripheries. Strong planar heterogeneity is observed in the J₂S1³ sub-member: Near-source areas (south/southwest) develop banded Type I/II units, whereas distal regions are dominated by Type IV units. This methodology provides a theoretical foundation and intelligent technological pathway for the efficient development of highly heterogeneous tight sandstone reservoirs. Full article

To solve common defects such as warpage deformation, interface debonding, and uneven filling during the two-color injection molding of medical goggles while meeting their multi-performance requirements, including high light transmittance, impact resistance, chemical corrosion resistance, and structural stability, this study conducts research on the process optimization of two-color injection molding. Firstly, based on the principle of material compatibility and Moldflow simulation, a suitable material combination was selected: the first-shot frame adopts Apec 1745 PC material, and the second-shot lens uses Makrolon 2858 PC material, which effectively avoids the risk of interface non-fusion. Subsequently, a high-precision 3D simulation model was established using Moldflow software, and the injection sequence of “frame first, lens second” was optimized and determined. A gating system with double-gate (for the frame) and single-gate side feeding (for the lens), as well as a cooling system with an 8 mm diameter, was designed, and all key indicators of mesh quality meet the simulation requirements. Taking the mold and melt temperatures, holding pressures, and holding times of the two shots as design variables and warpage deformation as the optimization objective, sample data were obtained through an L₃₂ (7⁴) orthogonal test. A BP neural network was constructed to describe the nonlinear relationship between parameters and quality, and the Sparrow Search Algorithm (SSA) was combined to optimize the weights and thresholds of the network, forming a BP-SSA intelligent optimization model. The results show that the mean absolute percentage error (MAPE) of the proposed model is only 2.28%, which is significantly better than that of the single BP neural network (14.36%). The optimal process parameters obtained by optimization are a mold temperature of 130 °C, first-shot melt temperature of 311 °C, second-shot melt temperature of 310 °C, first-shot holding pressure of 83 MPa, second-shot holding pressure of 70 MPa, first-shot holding time of 14 s, and second-shot holding time of 8 s. Simulation and mold test verification indicate that after optimization, the warpage deformation of the goggles is reduced to 0.8956 mm (simulation) and 0.944 mm (measured), with a relative error of only 5.4%, which is 67.9% lower than the initial simulation result. The integrated method of “material selection—CAE simulation—orthogonal test—BP-SSA intelligent optimization” proposed in this study provides technical support for the high-precision manufacturing of thin-walled transparent multi-material medical products. Full article

Multi-stage multi-cluster hydraulic fracturing in conglomerate reservoirs is often characterized by strong cluster-to-cluster variability in fluid distribution, which can reduce stimulation efficiency. However, field-scale observations that constrain how injected fluid is partitioned among clusters remain limited, especially in strongly heterogeneous formations. In this study, wide-field electromagnetic (WFEM) monitoring was applied to a horizontal well completed in the Baikouquan Formation sandstone–conglomerate reservoir of the Mahu Sag, Junggar Basin. The monitored treatment consisted of 13 fracturing stages, each containing six perforation clusters. Time-lapse electromagnetic data acquired during pumping were inverted to reconstruct the spatiotemporal evolution of the effective conductive fluid-swept region. Based on the inversion results, we introduce a set of quantitative proxy indicators (swept area, swept length, cluster-specific sweep, and an asymmetric index) to support relative comparison of fluid distribution patterns at both stage and cluster scales. Results show pronounced non-uniformity within and between stages, even under similar pumping conditions. A limited number of clusters exhibit stronger and farther-reaching WFEM-inferred conductive-fluid responses, whereas other clusters show weaker or more localized responses. Asymmetric sweep patterns on opposite sides of the wellbore are also commonly observed. These patterns are consistent with the combined influences of reservoir heterogeneity, local structural/stress disturbances, and operational factors, although WFEM alone does not uniquely validate causal mechanisms of fracture growth. Overall, this study demonstrates that WFEM monitoring provides a field-scale proxy tool for delineating effective conductive fluid-swept regions and for evaluating cluster-to-cluster variability under consistent acquisition and inversion settings. The findings offer practical guidance for interpreting fluid distribution and optimizing multi-cluster fracturing in strongly heterogeneous unconventional reservoirs. Full article

The cyclic injection production operations in underground gas storage (UGS) facilities lead to alternating changes in effective stress, resulting in irreversible permeability damage and productivity decline. To accurately evaluate the long-term operational performance of UGS, a damage factor m was introduced to modify the stress sensitivity model. A productivity equation incorporating stress sensitivity effects was subsequently established. Utilizing experimental data on the seepage capacity of reservoir rocks from the X UGS facility and well test data from the XC1 well, this study analyzed variations in the maximum gas injection capacity during multi-cycle injection operations. The results demonstrate that the modified model provides a significantly superior fit compared to classical models. The damage factor m exhibits a power-law decreasing trend with increasing cycle, declining from 0.95 in the first cycle to 0.62 in the fifth cycle during the production phase. Concurrently, the maximum injection capacity decreased from 1027.35 × 10⁴ m³/d in the first cycle to 797.62 × 10⁴ m³/d in the fifth cycle, representing a cumulative decline of 22.4%. The rate of decline decelerated markedly after the third cycle, with the annual attenuation rate dropping from 9.7% to 3%, indicating that plastic deformation tends to stabilize. The proposed model effectively captures the hysteresis and cycle-dependent permeability evolution in UGS reservoirs. The findings of this research offer valuable insights for guiding UGS production operations and optimizing production allocation strategies. Full article

Conventional material balance methods, typically based on single- or dual-porosity models solvable via single-step linearization, are inadequate for hydraulically fractured shale oil reservoirs due to their pronounced heterogeneity and contrasting interzonal connectivity. Specifically, dual-zone models fail to represent the realistic characteristics of shale oil reservoirs because they treat artificially created hydraulic fractures and natural fractures as equivalent, despite their substantially different properties. To address this gap, this paper proposes a novel three-zone conceptual model, segmenting the reservoir into the matrix zone (MZ), the Weakly Stimulated Zone (WSZ, low-conductivity zone), and the Strongly Stimulated Zone (SSZ, high-conductivity zone). A corresponding three-zone gas injection replenishment material balance model is developed. This model explicitly captures interactions between injected gas and formation fluids and incorporates dynamic variations in pore volume and fluid saturation induced by imbibition. To solve the complexities introduced by the triple-porosity system, a dedicated two-step linearization solution procedure is proposed. Utilizing conventional production performance and basic PVT data, the method enables simultaneous estimation of zone-specific developed reserves and prediction of the Estimated Ultimate Recovery (EUR) through a least squares algorithm. Validation against actual well cases and multi-well statistics confirms that the method provides stable and reliable zonal reserve characterization and EUR forecasting. The results indicate that the MZ contributes the majority of the geological reserves, accounting for >70%. The WSZ contributes approximately 29.5% of the reserves and serves as the primary source for energy replenishment in the shale oil reservoir. In contrast, the SSZ contributes less than 0.5% of the reserves but acts as the dominant channel for flow convergence, controlling the main fluid production pathways. The proposed framework not only offers a practical tool for refined reserve assessment in shale oil reservoirs but also provides a computational basis and decision support for the design and injection parameter optimization of pre-pad CO₂ energy storage fracturing schemes. Full article

The advancement of Industry 5.0 hinges on the deep integration of artificial intelligence (AI) with domain expertise to foster sustainable industrial development. This study proposes a deep learning-based surrogate modeling framework that integrates reservoir production requirements with AI technologies, providing intelligent decision support for production optimization and enhanced efficiency. To evaluate AI’s effectiveness in complex industrial scenarios, we conduct an integrated analysis encompassing model construction, dynamic prediction, and production optimization using a real-world oilfield case. This oilfield features a dynamically increasing number of wells and requires dynamic adjustments to injection–production relationships. To address this challenge, we enhance the Embed-to-Control model by improving the nonlinear representation capability within its decoder structure. Subsequently, we construct a high-fidelity dataset containing 300 samples for model training and testing. The experimental results demonstrate that the proposed improved model achieves a high accuracy in predicting key state variables (pressure and saturation) and oil production. Regarding computational efficiency, a single model run requires only approximately 17.3 s, achieving an over 200× speedup relative to traditional numerical simulators. Finally, we coupled the trained surrogate model with the particle swarm optimization algorithm to optimize the injection well control strategy. The optimized scheme increases daily oil production by 13.84%, boosting economic benefits. This study demonstrates a practical technological pathway to accelerate the oil and gas industry’s transition toward Industry 5.0. Full article

Economic gas recovery from shale reservoirs is inherently difficult because of the extremely low permeability of these formations. To overcome this challenge, horizontal wells are drilled and subjected to multi-stage hydraulic fracturing treatments, which generate high-conductivity flow pathways. The adoption of these technologies has significantly boosted the economic recovery of gas from shale formations, particularly the Marcellus Shale, which stands as the most productive shale gas play in the United States. The effectiveness of a fracturing treatment in enabling economic gas production from shale reservoirs is governed by the characteristics of the fractures it creates. The propagation of initial fracture, during multi-stage hydraulic fracturing, modifies the initial stress conditions in the surrounding area, commonly referred to as a “stress shadow.” The stress shadow restricts the initiation and subsequent propagation of later fracture stages, leading to the development of less favorable fracture properties. As a result, the uneven contribution of individual fracture stages to gas flow ultimately diminishes overall gas recovery from the horizontal well. For efficient gas drainage from the shale, the fracture stages are often closely spaced. When fracture stages are placed in close proximity, the stress shadow effect can be intensified. Thus, accounting for the stress shadow is essential in the design of hydraulic fracture treatments. This study investigates how fracture spacing, injected fluid volume, and fluid type influence the magnitude of the stress shadow effect, its impact on fracture properties, and the resulting gas recovery from the Marcellus Shale. The goal is to facilitate the optimization of the hydraulic fracture design to mitigate the stress shadow impact and enhance gas production. Data from several Marcellus Shale horizontal wells, along with published findings, were compiled and analyzed to determine the petrophysical and geomechanical characteristics of the formation. These results were then used to construct a reservoir model representative of a Marcellus Shale horizontal well. Fracture properties, altered by the stress shadow, were assessed through hydraulic fracturing simulations and incorporated into the model. Ultimately, the reservoir model was employed to predict the production performance. The results of the investigation confirmed that close stage spacing intensifies the impact of the stress shadow. The stress shadow was found to impair fracture conductivity which negatively impacted gas recovery. The negative impact of the stress shadow on gas recovery was observed to gradually diminish as the production rate declined over time. The volume and type of the fluid injected during fracturing treatment can amplify the stress shadow’s impact. Full article

Enhanced Geothermal Systems (EGS) extend geothermal energy beyond conventional hydrothermal resources but face challenges in creating sustainable heat exchangers in low-permeability formations. This review synthesizes achievements from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a field laboratory advancing EGS readiness in 175–230 °C granitic basement. From 2017 to 2025, drilling, multi-stage hydraulic stimulation, and monitoring established feasibility and operating parameters for engineered reservoirs. Hydraulic connectivity was created between highly deviated wells with ~300 ft vertical separation via hydraulic and natural fracture networks, validated by sustained circulation tests achieving 10 bpm injection at 2–3 km depth. Advanced monitoring (DAS, DTS, and microseismic arrays) delivered fracture propagation diagnostics with ~1 m spatial resolution and temporal sampling up to 10 kHz. A data infrastructure of 300+ datasets (>133 TB) supports reproducible ML. Geomechanical analyses showed minimum horizontal stress gradients of 0.74–0.78 psi/ft and N–S to NNE–SSW fractures aligned with maximum horizontal stress. Near-wellbore tortuosity, driving treating pressures to 10,000 psi, underscores completion design optimization, improved proppant transport in high-temperature conditions, and coupled thermos-hydro-mechanical models for long-term prediction, supported by AI platforms including an offline Small Language Model trained on Utah FORGE datasets. Full article

A comprehensive study on the distribution characteristics and exploitation strategies of remaining oil was carried out in the Ordovician ultra-deep fault-controlled fractured–vuggy carbonate reservoir within the Shunbei No. 1 strike–slip fault zone. This research addresses challenges such as severe watered-out and gas channeling encountered during multi-stage development, marking a shift toward a development phase focused on residual oil recovery. By integrating seismic attributes, drilling, logging, and production performance data—and building upon previous methodologies of “hierarchical constraint and genetic modeling”—a three-dimensional geological model was constructed with a five-tiered architecture: strike–slip fault affected zone, fault-controlled unit, cave-like structure, cluster fillings, and fracture zone. Numerical simulations were subsequently performed based on this model. The results demonstrate that the distribution of remaining oil is dominantly controlled by the coupling between key geological factors—including fault kinematics, reservoir architecture formed by karst evolution, and fracture–vug connectivity—and the injection–production well pattern. Three major categories with five sub-types of residual oil distribution patterns were identified: (1) local low permeability, weak hydrodynamics; (2) shielded connectivity pathways; and (3) Well Pattern-Dependent. Accordingly, two types of potential-tapping measures are proposed: improve well control through optimized well placement and sidetrack drilling and reservoir flow field modification via adjusted injection–production parameters and sealing of high-permeability channels. Techniques such as gas (nitrogen) huff-and-puff, gravity-assisted segregation, and injection–production pattern restructuring are recommended to improve reserve control and sweep efficiency, thereby increasing ultimate recovery. This study provides valuable guidance for the efficient development of similar ultra-deep fractured–vuggy carbonate reservoirs. Full article

As shale gas development extends into deeper formations, the unclear brittle-ductile transition (BDT) mechanism and low fracturing efficiency have emerged as critical bottlenecks, posing challenges to the sustainable and economical utilization of this clean energy resource. This study, focusing on the Liangshang Formation shale of Sichuan Basin’s Pingye-1 Well, pioneers a paradigm shift by identifying Poisson’s ratio ($\mathsf{\nu }$) as the master variable governing this transition. Triaxial tests reveal that $\mathsf{\nu }$ systematically increases with depth, directly regulating the failure mode shift from brittle fracture to ductile flow. Building on this, we innovatively propose the Poisson’s Ratio-regulated Energy-based Brittleness Index (PNE-BI) model. This model achieves a decoupled diagnosis of BDT by quantifying how ν intrinsically orchestrates the energy redistribution between elastic storage and plastic dissipation, utilizing ν as the sole governing variable to regulate energy weighting for rapid and accurate distinction between brittle, transitional, and ductile states. Experiments confirm the $\mathsf{\nu }$-dominated energy evolution: Low $\mathsf{\nu }$ rocks favor elastic energy accumulation, while high $\mathsf{\nu }$ rocks (>0.22) exhibit a dramatic 1520% surge in plastic dissipation, dominating energy consumption (35.9%) and confirming that $\mathsf{\nu }$ enhances ductility by reducing intergranular sliding barriers. Compared to traditional multi-variable models, the PNE-BI model utilizes $\mathsf{\nu }$ values readily obtained from conventional well logs, providing a transformative field-ready tool that significantly reduces the experimental footprint and promotes resource efficiency. It guides toughened fracturing fluid design in ductile zones to suppress premature closure and optimizes injection rates in brittle zones to prevent fracture runaway, thereby enhancing operational longevity and minimizing environmental impact. This work offers a groundbreaking and sustainable solution for boosting the efficiency of mid-deep shale gas development, contributing directly to more responsible and cleaner energy extraction. Full article

Horizontal wells combined with multi-stage fracturing are key techniques for extracting tight oil formation. However, due to the ultra-low permeability and porosity of reservoirs, energy depletion occurs rapidly, necessitating external supplements to sustain production. During the hydraulic fracturing process, large volumes of fracturing fluid are injected into reservoirs, increasing its pressure to a certain extent. However, due to the oil-wet nature of the formation, the fracturing fluid cannot penetrate the rock, failing to enhance oil recovery during the shut-in period. Surfactant-based nanofluids have been introduced as fracturing fluid additives to reverse rock wettability, thereby boosting imbibition-driven recovery. Although the imbibition has been studied to inspire the tight oil recovery, few studies have demonstrated the imbibition in enhanced fossil hydrogen energy, which further promotes the imbibition recovery. In this paper, complex nanofluid dispersions (CND) have been proved to enhance the tight reservoir pressure. Through contact angle and imbibition experiments, it is shown that CND can transform oil-wet rock to water-wet, reduce the adhesion of oil, and improve the ultimate oil recovery through the imbibition effect. Then, core flow testing experiments were conducted to show CND can decrease the flow resistance and improve the swept area of the injected fluid. In the end, pressure transmission tests were conducted to show CND can enhance the formation energy and production after fracturing. Results demonstrate that CND enables the fracturing fluid to travel further away from the hydraulic fractures, thus decreasing the depletion of tight formation pressure and maintaining a higher oil production rate. Results help optimize the design of the hydraulic fracturing of tight offshore reservoirs. Full article

Accurate characterization of fluid distribution in offshore waterflooded oilfields has been challenging due to complex heterogeneity and the limitations of traditional interpretation tools, which often cannot integrate multi-scale datasets such as core samples, well logs, and seismic surveys. This study addresses these challenges by developing an integrated interpretation workflow based on a calibrated rock physical fluid substitution model. The model, constrained by low-frequency laboratory measurements and elastic parameters from well logs, is used to assess the impact of fluid variations on core elastic properties and to ensure physical consistency across core, log, and seismic data scales. Key findings demonstrate that the calibrated model effectively detects impedance changes caused by water injection and accurately identifies remaining oil deposits. When applied to time-lapse seismic interpretation and reservoir numerical simulation, the model proves valuable for guiding infill well placement and optimizing development strategies in mature offshore reservoirs. Additionally, this approach provides a robust framework for integrating multi-source data, thereby enhancing the reliability of reservoir characterization in waterflooded wells. Full article

The separate-layer gas injection technology is a key means to improve the effect of refined gas injection development. Currently, the measurement and adjustment of separate injection wells primarily rely on manual experience and automatic measurement via instrument traversal, resulting in a long duration, low efficiency, and low qualification rate for injection allocation across multi-layer intervals. Given the different CO₂-containing natural gas injection rates across different intervals, this paper establishes a coupled flow model of a separate-layer gas injection wellbore–gas distributor–formation based on the energy and mass conservation equations for wellbore pipe flow, and develops a solution method for determining gas nozzle sizes across multi-layer intervals. Based on the maximum allowable gas nozzle size, an optimization method for multi-layer collaborative allocation of separate injection wells is established, with minimum wellhead injection pressure and layered injection allocation as the optimization objectives, and the opening of gas distributors for each layer as the optimization variable. Taking Well XXX as an example, the optimization process of allocation schemes under different gas allocation requirements is simulated. The research shows that the model and method proposed in this paper have high calculation accuracy, and the formulated allocation schemes have strong adaptability and minor injection allocation errors, providing a scientific decision-making method for formulating refined allocation schemes for separate-layer gas injection wells, with significant theoretical and practical value for promoting the refined development of oilfields. Full article

The overlying rock in the weathering and oxidation zone has well-developed micro-fissures, making roadway roof control highly challenging. Ordinary cement slurry is hard to inject, failing to achieve effective reinforcement. By introducing admixtures like ultrafine fly ash and polyvinyl alcohol (PVA) to modify ultrafine cement, this paper developed a PVA-modified ultrafine cement-based grouting material (PVAM-UFCG). It systematically investigated the influences of various factors on the slurry’s setting time, fluidity, water separation rate, viscosity, and 28-day uniaxial compressive strength, determining the optimal mix ratio through comprehensive analysis. The results show that the water–cement ratio is the dominant factor affecting slurry viscosity, strength, and setting time; the polycarboxylate superplasticizer concentration has the most significant influence on fluidity and water separation rate; a 20% ultrafine fly ash replacement rate can optimize particle gradation and enhance long-term strength; and a 1.0% polyvinyl alcohol concentration can effectively control the water separation rate (≤5%) and improve slurry cohesiveness. Through range analysis and multi-indicator comprehensive evaluation based on the entropy weight method, the performance-balanced optimal mix ratio meeting the grouting requirements for the Weathering and Oxidation Zone was determined: a water–cement ratio of 0.6, an ultrafine fly ash replacement rate of 20%, a polyvinyl alcohol concentration of 1.0%, and a polycarboxylate superplasticizer concentration of 0.4%. This mix ratio material exhibits good permeability, stability, and appropriate reinforcement strength. The research results can provide a new material choice and theoretical basis for controlling the surrounding rock of roadways under similar geological conditions. Full article