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<jats:p> Shallow-water carbonate structures are characterized by different shapes, sizes and identifying features, which depend, among other factors, on the age of deposition and on the carbonate factory associated with a specific geologic period. These variations have a significant impact on the imaging of these structures in reflection seismic data. This study aims at providing an overall, albeit incomplete, picture of how the seismic expression of shallow-water carbonate structures has evolved through deep time. 297 shallow-water carbonate systems of different ages, spanning from Precambrian to present, with a worldwide distribution of 159 sedimentary basins, have been studied. For each epoch, representative seismic examples of shallow-water carbonate structures were described through the assessment of a selection of discriminating seismic criteria, or parameters. The thinnest structures, commonly represented by ramp systems, usually occurred after mass extinction events, and are mainly recognizable in seismic data through prograding clinoform reflectors. The main diagnostic seismic features of most of the thickest structures, which were found to be Precambrian, Late Devonian, Middle-Late Triassic, Middle-Late Jurassic, some Early Cretaceous pre-salt systems, #8220;middle#8221; and Late Cretaceous, Middle-Late Miocene and Plio-Pleistocene, are steep slopes, and reefal facies. Slope-basinal, resedimented seismic facies, were mostly observed in thick, steep-slope platforms, and they are more common, except for megabreccias, in post-Triassic structures. Seismic-scale, early karst-related dissolution features were mostly observed in icehouse, platform deposits. Pinnacle structures and the thickest margin rims are concentrated in a few epochs, such as Middle-Late Silurian, Middle-Late Devonian, earliest Permian, Late Triassic, Late Jurassic, Late Paleocene, Middle-Upper Miocene, and Plio-Pleistocene, which are all characterized by high-efficiency reef builders. </jats:p>

<jats:p> Fractures in reservoirs have a profound impact on hydrocarbon production operations. The more accurately fractures can be detected, the better the exploration and production processes can be optimized. Therefore, fracture detection is an essential step in understanding the reservoir's behavior and the stability of the wellbore. The conventional method for detecting fractures is image logging, which captures images of the borehole and fractures. However, the interpretation of these images is a laborious and subjective process that can lead to errors, inaccuracies, and inconsistencies, even when aided by software. Automating this process is essential for expediting operations, minimizing errors, and increasing efficiency.While there have been some attempts to automate fracture detection, this paper takes a novel approach by proposing the use of YOLOv5 as a Deep Learning (DL) tool to detect fractures automatically. YOLOv5 is unique in that it excels at speed, both in training and detection, while maintaining high accuracy in fracture detection. We observed that YOLOv5 can detect fractures in near real-time with a high mean average precision (mAP) of 98.2, requiring significantly less training than other DL algorithms. Furthermore, our approach overcomes the shortcomings of other fracture detection methods.The proposed method has many potential benefits, including reducing manual interpretation errors, decreasing the time required for fracture detection, and improving fracture detection accuracy. Our approach can be utilized in various reservoir engineering applications, including hydraulic fracturing design, wellbore stability analysis, and reservoir simulation. By using this technique, the efficiency and accuracy of hydrocarbon exploration and production can be significantly improved. </jats:p>

<jats:p> In this study, we interpret the maximum horizontal stress (SHmax) azimuth from the breakout positions of wellbore and attempt to constrain the SHmax gradient based on the interpreted breakout width. A cumulative of 110 m of breakouts were deciphered within the Ordovician Hamra Quartzite interval of the Oued Mya Basin from a 138 m of acoustic image log. These breakouts were ranked as “A-Quality” following the World Stress Map ranking guidelines. We infer a mean SHmax orientation of N28°E ± 8°. Following the frictional faulting mechanism and stress polygon approach, measurement of minimum horizontal stress (Shmin) from minifrac tests and observations of compressive failures from acoustic image log provided strong constraints on the SHmax magnitude in the reservoir interval in the absence of core-measured rock strength. Interpreted breakout widths exhibit a range between 32.6° and 90.81°, which indicated a SHmax range of 24.4–34.7 MPa/km. The average breakout width of 62.58° translates to a narrower SHmax gradient range, varying between 27.2 and 31.2 MPa/km. The relative magnitudes of the principal stresses indicate a strong strike-slip tectonic stress state. Considering all the uncertainties, we infer a SHmax/Shmin ratio of 1.41–1.81 within the Ordovician interval. </jats:p>

<jats:p> The accurate quantification and mapping of subsurface natural fracture systems using a borehole imaging logs are critical for the success of CO<jats:sub>2</jats:sub> sequestration in geological formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and deep learning workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by utilizing actual amplitude values from acoustic image logs alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs, while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to The Illinois Basin – Decatur Project (IBDP) site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with high resolution of 0.01 ft or 0.12 inch (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO<jats:sub>2</jats:sub> pressure forecasting and post-injection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared to alternative feature extraction technologies. It presents exciting possibilities for advancing CO2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO<jats:sub>2</jats:sub> sequestration projects, facilitating sustainable environmental practices and combating climate change. </jats:p>

<jats:p> Artificial neural networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well-log measurements that were used as input for machine-learning (ML) models. Well-log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. The results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics. </jats:p>

<jats:p> The characteristics and formation of maximum flooding (MF) black shales are important aspects in defining the geology of fine-grained reservoirs. The MF black shales are located at the bottom of the Longmaxi Formation on the Upper Yangtze Platform, corresponding to graptolite zone LM1. Seismic interpretation, X-ray diffraction entire rock analysis, total organic carbon (TOC) tests, and field emission scanning electron microscopy analysis indicate that the MF black shales have an average content of 49.3% quartz (85% clay size), 10.5% calcite, 8.4% dolomite, and 23.4% clay minerals. The quartz content increases basinward, whereas the clay mineral content decreases. The shale has developed during rapid sea level rise, with a thickness of 0.5–2.8 m that gradually thickens basinward. The TOC content, averaging 5.4%, gradually decreases basinward, with four distinct stacking patterns. The mineral composition and thickness of the Longmaxi shale are related closely to rapid transgression, biology, and volcanism during the period of sedimentation. Rapid transgression has led to a decrease in terrestrial input and shale thickness. In addition, biological activity and volcanism have caused the prevalence of microcrystalline quartz. Shales with high TOC content are related to anoxic conditions, along with low sedimentation rates and high primary productivity. The combination of an anoxic water column, weak dilution, and enhanced organic matter (OM) supply have enhanced the preservation of the OM. The four TOC stacking patterns are related to the water depth. The supply of clay minerals decreases with increasing water depth, whereas the degradation and recycling of OM decrease the TOC content. The sediment accommodation increases with increasing water depth, resulting in four TOC stacking patterns. </jats:p>

<jats:p> Enhancing vertical resolution and signal-to-noise ratio (S/N) is a key objective in seismic data processing. Considering the underground medium is inhomogeneous and incompletely elastic, seismic wave energy attenuation occurs during underground propagation, which has a significant impact on seismic data resolution and S/N. Traditional fast-matching pursuit (FMP) algorithms make it difficult to separate valid signals and noise effectively while reconstructing the noisy signals. Therefore, an improved FMP algorithm that combines the variational-mode decomposition (VMD) strategy is developed. The VMD algorithm is used to obtain intrinsic mode functions with varying amplitudes, frequencies, and center times. It can achieve a multiscale decomposition of nonstationary seismic data. Based on the intrinsic mode functions of different scales, the FMP algorithm can reconstruct prior information of the amplitude, frequency, and center time of valid signals and noise signals in the mode functions. Thus, the high-resolution sparse representation of intrinsic mode functions is achieved. The numerical results indicate that our method not only separates the effective signal and noise but also preserves the valid signal as much as possible. In addition, the feasibility of the method is further verified by field exploration data. The results indicate that this strategy can enhance the resolution of seismic data while restoring the attenuated energy using multiscale seismic data. </jats:p>

<jats:p> Seismic imaging of crustal structures becomes difficult in the presence of rough basements or complex bathymetry. Here, we present a 900 km deep seismic reflection profile collected across the Southwest Sub-basin (SWSB) of the South China Sea. By analyzing the types and distinctions of noise and effective signals, we employed deep structure migration techniques to improve crustal structure imaging, wide-line processing to predict 3D space multiples, and F-K domain time-space variable adaptive de-ghosting and different offset stacking to enhance the weak signals reflected from deep strucutures. The imaged continental crustal structure in the Penxi Bank exhibits moderate thinning, down to 15 km, and is intersected by continental-ward low-angle normal faults. Within the limitations of the OBS P-wave velocity model, we detected sub-horizontal lower crustal reflections that may be indicative of a weak lower crust. Two small-scale rollover structures along detachment faults rooted and rafted to the top of these weak lower crust. Based on the presence of narrow continent-ocean transitions(COTs), continental-ward detachment faults, and high lithosphere heat flow, we deduced that the mantle lithosphere breakup occurred earlier than the crust in the SWSB. Moreover, the continent-ocean transitions and oceanic crust domains demonstrate rough basements with numerous faults and approximately 20% diffuse or weak Moho reflections. From the southern COT to the initial oceanic domain, the thickness of the crust gradually reduces to only 3-5 km. This suggests a relatively low magmatic budget and protracted tectonic extension from the continental breakup to the onset of seafloor spreading. Within the oceanic crust domain, the crust thickness ranges from approximately 4-6 km, indicating a thinner oceanic crust than normal crust. Lower crustal reflections with a ridge-ward dipping pattern terminate at the Moho reflections and are partly connected to syn-spreading faults, hinting at their possible generation through syn-spreading faulting. </jats:p>

<jats:p> The Zhongnan Fault Zone (ZFZ) is a large-scale tectonic belt in the South China Sea (SCS) oceanic basin, playing an important role in the formation and evolution of the basin. Nevertheless, debates persist regarding its location, orientation, nature, time of activity, and genesis. In this study, we investigate the characteristics and origin of the fault zone through an integrated analysis of multibeam bathymetric and two-dimensional (2D) multi-channel seismic data. Our results reveal the ZFZ as a fault zone approximately 400 km long and 50-90 km wide, situated between the east (ESB) and southwest (SWSB) sub-basins. The ZFZ is oriented N8°W, roughly perpendicular to and laterally displacing the relict spreading center and related spreading lineaments. Bounded by discontinuous linear seamounts, the ZFZ comprises two V-shaped sub-parallel pull-apart basins and a separating basement high. Steeply dipping (&gt;60°) normal basement-involved faults bound these pull-apart basins, forming typical negative flower structures. Numerous NE-oriented en-echelon linear bathymetric highs within the ZFZ are identified as secondary antithetic shears. These shears are characterized by their orientation relative to the principal displacement zones defined along the pull-apart basins. The ZFZ exhibits differences from adjacent sub-basins in water depth, basement burial, stratal thickness, and seismic stratigraphic characteristics. Five seismic sequences (S1-S5 upwards) in the ZFZ and nearby ESB and SWSB are defined, dating to Early Miocene syn-spreading (S1) and Middle Miocene to Recent post-spreading (S2-S5) stages, respectively. The difficulty in correlating seismic facies in sequences S1-S3, compared to the comparable seismic facies in sequences S4-S5 between the ZFZ and adjacent sub-basins, suggests a horizontal displacement during the syn-spreading and early post-spreading stages. We propose that the ZFZ functioned as a right-lateral transform fault zone during the syn-spreading period (∼24-16 Ma) of the SWSB and transitioned into a left-lateral strike-slip fault zone during the successive early post-spreading period (∼16-5.3 Ma). </jats:p>