Search Results (330)

The Shenhu sea area is rich in unconsolidated hydrate reserves, but the formation mineral particles are small, the rock cementation is weak, and the coupling mechanism of hydrate phase change, fluid seepage, and formation deformation is complex, resulting in unclear productivity change law under depressurization exploitation. Therefore, a thermal–fluid–solid–chemical coupling model for natural gas hydrate depressurization exploitation in the Shenhu sea area was constructed to analyze the variation law of reservoir parameters and productivity. The results show that within 0–30 days, rapid near-well pressure drop (13.83→9.8 MPa, 36.37%) drives peak gas production (25,000 m³/d) via hydrate dissociation, with porosity (0.41→0.52) and permeability (75→100 mD) increasing. Within 30–60 days, slower pressure decline (9.8→8.6 MPa, 12.24%) and fines migration cause permeability fluctuations (120→90 mD), reducing gas production to 20,000 m³/d. Within 60–120 days, pressure stabilizes (~7.6 MPa) with residual hydrate saturation < 0.1, leading to stable low permeability (60 mD) and gas production (15,000 m³/d), with cumulative production reaching 2.2 × 10⁶ m³. This study clarifies that productivity is governed by coupled “pressure-driven dissociation–heat limitation–fines migration” mechanisms, providing key insights for optimizing depressurization strategies (e.g., timed heat supplementation, anti-clogging measures) to enhance commercial viability of unconsolidated hydrate reservoirs. Full article

This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges. Full article

The design of high-performance drilling fluid systems is of vital importance for the safe and efficient exploitation of natural gas hydrates. Incorporating appropriate nanoparticles into drilling fluids can significantly enhance drilling fluid loss control, wellbore stability, and hydrate inhibition. However, the combined effects of nanoparticles and conventional additives on hydrate inhibition in drilling fluid systems remain poorly understood. In this study, the influence of nanoparticles on hydrate formation was first evaluated in a base mud, followed by an investigation of their combined effects with common drilling fluid additives. The results demonstrate that hydrophilic nano-CaCO₃ particles exhibit hydrate inhibitory effects, with the strongest inhibition observed at 3.0%. Composite system tests (incorporating nanoparticles with sepiolite, filtrate reducers, and flow modifiers) revealed diverse effects on hydrate formation. Specifically, the combination of nanoparticles and sepiolite promoted hydrate formation; the combination of nanoparticles and filtrate reducers showed divergent effects. Mixtures of nanoparticles with 0.2% low-viscosity anionic cellulose (LV-PAC), carboxymethyl starch (CMS), and low-viscosity carboxymethyl cellulose (LV-CMC) inhibited hydrate formation, while mixtures with 0.2% sulfonated phenolic resin (SMP-2) and hydrolyzed ammonium polyacrylonitrile (NH₄-HPAN) accelerated hydrate formation. Notably, the incorporation of nanoparticles with 0.3% guar gum, sesbania gum, high-viscosity carboxymethyl cellulose (HV-CMC), or high-viscosity polyanionic cellulose (HV-PAC) resulted in the complete inhibition of hydrate formation. By contrast, the synergistic inhibition effect of the nanoparticle/xanthan gum (XC) composite system was relatively weak, with the optimal compounding concentration determined to be 0.3%. These findings provide critical insights for the development of drilling fluid systems in natural gas hydrate reservoirs, facilitating the optimization of drilling performance and enhancing operational safety in hydrate-bearing formations. Full article

Natural gas hydrates (NGHs) are promising energy resources, although their marine exploitation is limited by low reservoir permeability and hydrate decomposition efficiency. Multi-cluster fracturing technology can enhance reservoir permeability, yet complex properties of hydrate sediments render the prediction of fracture behavior challenging. Therefore, we developed a three-dimensional (3D) fluid–solid coupling model for hydraulic fracturing in NGH reservoirs based on cohesive elements to analyze the effects of sediment plasticity, hydrate saturation, fracturing fluid viscosity, and injection rate, as well as the stress interference mechanisms in multi-cluster simultaneous fracturing under different cluster spacings. Results show that selecting low-plastic reservoirs with high hydrate saturation (S_H > 50%) and adopting an optimal combination of fracturing fluid viscosity and injection rate can achieve the co-optimization of stimulated reservoir volume (SRV) and cross-layer risk. In multi-cluster fracturing, inter-fracture stress interference promotes the propagation of fractures along the fracture plane while suppressing it in the normal direction of the fracture plane, and this effect diminishes significantly till 9 m cluster spacing. This study provides valuable insights for the selection of optimal multi-cluster fracturing parameters for marine NGH reservoirs. Full article

Natural gas hydrates, with their efficient and clean energy characteristics, are deemed a significant pillar within the future energy sector, and their resource quantification and development have a profound impact on the transformation of global energy structure. However, how to accurately identify gas hydrate reservoirs (GHRs) is currently a hot research topic. This study explores the logging identification method of marine GHRs based on machine learning (ML) according to the logging data of the International Ocean Drilling Program (IODP) Expedition 311. This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). The internal relationship between logging data and hydrate reservoir is analyzed through six ML algorithms. The results show that the constructed ML model performs well in gas hydrate reservoir identification. Among them, RF has the highest accuracy, precision, recall, and harmonic mean of precision and recall (F1 score), all of which are above 0.90. With an area under curve (AUC) of nearly 1 for RF, it is confirmed that ML technology is effective in this area. Research has shown that ML provides an alternative method for quickly and efficiently identifying GHRs based on well logging data and also offers a scientific foundation and technical backup for the future prospecting and mining of natural gas hydrates. Full article

This study focuses on the application of electrical resistivity tomography (ERT) for monitoring the growth process of CO₂ hydrate in subsea carbon sequestration, aiming to provide technical support for the safety assessment of marine carbon storage. By designing single-target, dual-target, and multi-target hydrate samples, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and residual neural networks (ResNets) were constructed and compared with traditional image reconstruction algorithms (e.g., back-projection) to quantitatively analyze ERT imaging accuracy. The experiments used boundary voltage as the input and internal conductivity distribution as the output, employing the relative image error (RIE) and image correlation coefficient (ICC) to evaluate algorithmic performance. The results demonstrate that neural network algorithms—particularly RNNs—exhibit superior performance compared to traditional image reconstruction methods due to their strong noise resistance and nonlinear mapping capabilities. These algorithms significantly improve the edge clarity in target identification, enabling the precise capture of the hydrate distribution during carbon sequestration. This advancement effectively enhances the monitoring capability of CO₂ hydrate reservoir characteristics and provides reliable data support for the safety assessment of hydrate reservoirs. Full article

Coalbed methane (CBM), a significant unconventional natural gas resource, holds a crucial position in China’s ongoing energy structure transformation. However, the inherent low permeability, high brittleness, and strong sensitivity of CBM reservoirs to drilling fluids often lead to severe formation damage during drilling operations, consequently impairing well productivity. To address these challenges, this study developed a novel low-damage, plugging, and anti-collapse water-based drilling fluid gel system (ACWD) specifically designed for coalbed methane drilling. Laboratory investigations demonstrate that the ACWD system exhibits superior overall performance. It exhibits stable rheological properties, with an initial API filtrate loss of 1.0 mL and a high-temperature, high-pressure (HTHP) filtrate loss of 4.4 mL after 16 h of hot rolling at 120 °C. It also demonstrates excellent static settling stability. The system effectively inhibits the hydration and swelling of clay and coal, significantly reducing the linear expansion of bentonite from 5.42 mm (in deionized water) to 1.05 mm, and achieving high shale rolling recovery rates (both exceeding 80%). Crucially, the ACWD system exhibits exceptional plugging performance, completely sealing simulated 400 µm fractures with zero filtrate loss at 5 MPa pressure. It also significantly reduces core damage, with an LS-C1 core damage rate of 7.73%, substantially lower than the 19.85% recorded for the control polymer system (LS-C2 core). Field application in the JX-1 well of the Ordos Basin further validated the system’s effectiveness in mitigating fluid loss, preventing wellbore instability, and enhancing drilling efficiency in complex coal formations. This study offers a promising, relatively environmentally friendly, and cost-effective drilling fluid solution for the safe and efficient development of coalbed methane resources. Full article

The efficient extraction of natural gas from marine natural gas hydrate (NGH) reservoirs is challenging, due to their low permeability, high hydrate saturation, and fine-grained sediments. Hydraulic fracturing has been proven to be a promising technique for improving the permeability of these unconventional reservoirs. This study presents a comprehensive triaxial experimental investigation of the fracturing behavior and fracture initiation mechanisms of NGH-bearing sediments, using large-scale ice-saturated synthetic cubic models. The experiments systematically explore the effects of key parameters, including the injection rate, fluid viscosity, ice saturation, perforation patterns, and in situ stress, on fracture propagation and morphology. The results demonstrate that at low fluid viscosities and saturation levels, transverse and torsional fractures dominate, while longitudinal fractures are more prominent at higher viscosities. Increased injection rates enhance fracture propagation, generating more complex fracture patterns, including transverse, torsional, and secondary fractures. A detailed analysis reveals that the perforation design significantly influences the fracture direction, with 90° helical perforations inducing vertical fractures and fixed-plane perforations resulting in transverse fractures. Additionally, a plastic fracture model more accurately predicts fracture initiation pressures compared to traditional elastic models, highlighting a shift from shear to tensile failure modes as hydrate saturation increases. This research provides new insights into the fracture mechanisms of NGH-bearing sediments and offers valuable guidance for optimizing hydraulic fracturing strategies to enhance resource extraction in hydrate reservoirs. Full article

Recently, shallow gas fields and hydrate-bearing sand in the deepwater area of the northern South China Sea have been successively discovered, and the accurate prediction of shallow sands is an important foundation. However, most of the current prediction methods are mainly for deep oil and gas reservoirs. Compared with those reservoirs with high degree of consolidation, shallow sandy reservoirs are loose and unconsolidated, whose geophysical characteristics are not well understood. This paper analyzes the logging data of shallow sandy reservoirs recovered in the South China Sea recently, which show that the sand content has a significant influence on Young’s modulus and Poisson’s ratio of the sediments. Therefore, this paper firstly constructs a new petrophysical model of unconsolidated strata targeting sandy content and qualitatively links the mineral composition and the elastic parameters of the shallow marine sediments and defines a new indicator for sandy content: the modified brittleness index (MBI). The effectiveness of MBI in predicting sandy content is then verified by measured well data. Based on pre-stack seismic inversion, the MBI is then inverted, which will identify the sandy deposits. The method proposed provides technical support for the subsequent shallow gas and hydrate exploration in the South China Sea. Full article

Natural gas hydrates are a promising alternative energy source for oil and gas in the future. However, geomechanical issues, such as wellhead instability, may arise, affecting the safe and efficient development of hydrates. In the present work, a sensitivity analysis was performed on sediment subsidence and wellhead instability during the development of marine hydrates using a multi-field coupled model. This is accomplished by adjusting the corresponding parameters based on the basic data of the default case. Meanwhile, the corresponding influencing mechanisms were explored. Finally, design recommendations for operation parameters were proposed based on the research findings regarding wellhead stability. It was found that the wellhead undergoes rapid sinking during a certain period in the early stage of hydrate development, followed by a slower, continued sinking. The sensitivity analysis found that when the depressurization amplitude is small, the wellhead sinking is also minimal. To maintain wellhead stability during the development process, it is recommended that neither the depressurization amplitude or drawdown pressure exceed 3.0 MPa. Although a high heating temperature can increase gas production to some extent, the accompanying excessive hydrate dissociation may compromise the stability of both the formation and wellhead. To balance gas production and wellhead stability, it is recommended that the heating amplitude does not exceed 50 °C. In addition, the permeability influences the distribution of pore pressure, which in turn affects sediment subsidence and wellbore stability. Wellhead stability deteriorates as permeability increases. Therefore, it is crucial to accurately determine the reservoir characteristics (such as permeability) before developing hydrates to avoid wellhead instability. Finally, the investigation results reveal that using different versions of the investigation model can impact the accuracy of the results, and neglecting certain physical fields may lead to an underestimation of the wellhead sinking. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

Ensuring long-term wellbore integrity is critical for CO₂ injection and storage operations. Conventional cement degrades in CO₂-rich environments, compromising zonal isolation and increasing leakage risks. This study presents a novel self-healing cement formulation incorporating Barite, Pozzolan, and Chalcedony, optimized using a Design of Experiment (DOE) approach. Geochemical simulations were conducted using PHREEQC and Python to evaluate porosity evolution, mineral stability, and self-sealing efficiency under CO₂ exposure. The results demonstrate that the optimized formulations significantly reduce porosity (within 7–14 days) through the formation of calcium silicate hydrate (C-S-H) gels, enhancing crack sealing and mechanical resilience. Saturation index and phase volume analyses confirm the long-term stability of ECSH2 and Calcite, reinforcing the cement matrix. Compared to conventional cement, the self-healing formulations exhibit improved durability, lower permeability, and superior resistance to CO₂-induced degradation. These findings support the use of self-healing cement in carbon capture and storage (CCS), geothermal energy, and deep-well applications, offering a cost-effective and durable solution for long-term wellbore integrity. However, further experimental validation and field-scale evaluation are needed to confirm the practical performance of these formulations under real-world reservoir conditions. Full article

Lithology identification is fundamental for the logging evaluation of natural gas hydrate reservoirs. The Sanlutian field, located in the permafrost zones of the Qilian Mountains (PZQM), presents unique challenges for lithology identification due to its complex geological features, including fault development, missing and duplicated stratigraphy, and a diverse array of rock types. Conventional methods frequently encounter difficulties in precisely discerning these rock types. This study employs well logging and core data from hydrate boreholes in the region to evaluate the performance of four data-driven machine learning (ML) algorithms for lithological classification: random forest (RF), multi-layer perceptron (MLP), logistic regression (LR), and decision tree (DT). The results indicate that seven principal lithologies—sandstone, siltstone, argillaceous siltstone, silty mudstone, mudstone, oil shale, and coal—can be effectively distinguished through the analysis of logging data. Among the tested models, the random forest algorithm demonstrated superior performance, achieving optimal precision, recall, F1-score, and Jaccard coefficient values of 0.941, 0.941, 0.940, and 0.889, respectively. The models were ranked in the following order based on evaluation criteria: RF > MLP > DT > LR. This research highlights the potential of integrating artificial intelligence with logging data to enhance lithological classification in complex geological settings, providing valuable technical support for the exploration and development of gas hydrate resources. Full article

The characteristics of marine–continental transitional shale reservoirs and the performance parameters of fracturing fluids, such as pH and mineralization, play a crucial role in influencing the flowback efficiency of these fluids. Excessive retention of fracturing fluids within the reservoir can lead to a significant decrease in permeability, thereby diminishing gas well productivity. This study investigates shale samples sourced from the marine–continental transitional shale formation in the eastern Ordos Basin, along with field-collected fracturing fluid samples, including formation water, sub-formation water, distilled water, inorganic acids, and organic acids, through flowback experiments. The results show that: (1) the flowback rate of shale fracturing fluids exhibits a positive correlation with salinity, with low-salinity fluids showing a dual effect on clay mineral hydration. These fluids increase the pore volume of the sample from 0.003 cm³/g to 0.0037 cm³/g but also potentially reduce permeability by 31.15% to 99.96%; (2) the dissolution effects of inorganic and organic acids in the fracturing fluids enhance the flowback rate by 16.42% to 22.25%, owing to their chemical interactions with mineral constituents; (3) in the development of shale gas reservoirs, it is imperative to carefully devise reservoir protection strategies that balance the fracture-inducing effects of clay mineral hydration and expansion, while mitigating water sensitivity damage. The application of acid preflush, primarily including inorganic or organic acids, in conjunction with the advanced fracturing techniques, can enhance the connectivity of shale pores and fractures, thereby improving fracture conductivity, increasing the flowback rate of fracturing fluids, and ensuring sustained and high gas production from wells. Full article

Deep coalbed methane (CBM) reservoirs have the characteristics of low permeability, low porosity, and low water saturation, which easily experience wellbore instability due to drilling fluid, severely affecting drilling safety. Based on the physical property analysis of coal samples, the wellbore instability mechanism of the deep CBM reservoir was investigated by multiple methods. It was found that the wellbore instability is mainly caused by drilling fluid intrusion and the interaction between drilling fluid and coal formation; the fracture pressure of coal after immersion decreased from 27.4 MPa to 25.0 MPa because of the imbibition of drilling fluid. A novel nano-plugging agent with a size of 460 nm was prepared that can cement coal particles to form disc-shaped briquettes with a tensile strength of 2.27 MPa. Based on that, an effective anti-collapse drilling fluid for deep coal rock reservoirs was constructed, the invasion depth of the optimized drilling fluid was only 6 mm. The CT result shows that the number of fractures and pores in coal rock significantly reduced after treatment with the wellbore-stabilizing drilling fluid; nano-plugging anti-collapse agent in drilling fluid can form a dense layer on the coal surface, and then the hydration swelling of clay in the wellbore region can be effectively suppressed. Finally, the drilling fluid in this work can achieve the purpose of sealing and wettability alternation to prevent the collapse of the wellbore in the deep coal reservoir. Full article