Search Results (34)

The central and deep reservoirs of the Wushi Sag in the Beibu Gulf Basin, China, are characterized by structurally complex settings, strong heterogeneity, multiple controlling factors for physical properties of reservoirs, rapid lateral variations in reservoir thickness and petrophysical properties, and limited seismic resolution. To address these challenges, this study integrates the INPEFA inflection point technique and Morlet wavelet transform to delineate system tracts and construct a High-Frequency Stratigraphic Framework (HFSF). Sedimentary facies are identified through the integration of core descriptions and seismic data, enabling the mapping of facies distributions. The vertical constraints provided by the stratigraphic framework, combined with the lateral control from facies distribution, which, based on identification with logging data and geological data, support the construction of a geologically consistent low-frequency initial model. Subsequently, geostatistical seismic inversion is performed to derive acoustic impedance and lithological distributions within the central and deep reservoirs. Compared with the traditional methods, the accuracy of the inversion results of this method is 8% higher resolution than that of the conventional methods, with improved vertical resolution to 3 m, and enhances the lateral continuity matched with the sedimentary facies structure. This integrated workflow provides a robust basis for predicting the spatial distribution of sandstone reservoirs in the Wushi Sag’s deeper stratigraphic intervals. Full article

Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. To resolve this challenge, this study investigates geophysical logging as an innovative method for coal quality prediction. By integrating scanning electron microscopy (SEM), X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, this research analyzes the coal quality and mineral composition of the Shanxi Formation coal seams in northern Jiangsu, China. A predictive model linking geophysical logging responses to coal quality parameters was established to delineate relationships between subsurface geophysical data and material properties. The results demonstrate that the Shanxi Formation coals are gas coal (a medium-metamorphic bituminous subclass) characterized by low sulfur content, low ash yield, low fixed carbon, high volatile matter, and high calorific value. Mineralogical analysis identifies calcite, pyrite, and clay minerals as the dominant constituents. Pyrite occurs in diverse microscopic forms, including euhedral and semi-euhedral fine grains, fissure-filling aggregates, irregular blocky structures, framboidal clusters, and disseminated particles. Systematic relationships were observed between logging parameters and coal quality: moisture, ash content, and volatile matter exhibit an initial decrease, followed by an increase with rising apparent resistivity (LLD) and bulk density (DEN). Conversely, fixed carbon and calorific value display an inverse trend, peaking at intermediate LLD/DEN values before declining. Total sulfur increases with density up to a threshold before decreasing, while showing a concave upward relationship with resistivity. Negative correlations exist between moisture, fixed carbon, calorific value lateral resistivity (LLS), natural gamma (GR), short-spaced gamma-gamma (SSGG), and acoustic transit time (AC). In contrast, ash yield, volatile matter, and total sulfur correlate positively with these logging parameters. These trends are governed by coalification processes, lithotype composition, reservoir physical properties, and the types and mass fractions of minerals. Validation through independent two-sample t-tests confirms the feasibility of the neural network model for predicting coal quality parameters from geophysical logging data. The predictive model provides technical and theoretical support for advancing intelligent coal mining practices and optimizing efficiency in coal chemical industries, enabling real-time subsurface characterization to facilitate precision resource extraction. Full article

The geological storage of carbon dioxide (CO₂) in offshore saline aquifers stands as a primary option for reducing CO₂ emissions in coastal regions. China’s coastal regions, particularly Shandong and Jiangsu provinces, face significant challenges in CO₂ reduction. Therefore, evaluating the feasibility of CO₂ geological storage in the adjacent seas is critical. To assess the suitability of a CO₂ storage site, understanding its structural and reservoir characteristics is essential to mitigate injection and storage risks. In this study, we analyzed the structural characteristics and potential traps of the Yantai Depression in the South Yellow Sea Basin based on seismic data interpretation. We further conducted well logging analysis and post-stack seismic inversion to obtain lithological data, including acoustic impedance and sandstone content percentages from the Cenozoic Funing Formation, Dainan–Sanduo Formation, and Yancheng Formation. Our findings highlight that the Yantai Depression in the South Yellow Sea Basin exhibits diverse structural traps and favorable reservoir–caprock combinations, suggesting promising geological conditions for CO₂ storage. This area emerges as a suitable candidate for implementing CO₂ geological storage initiatives. Full article

A high-resolution seismic survey (HRSS) is often used in coal exploration to bridge the data gap between two consecutive boreholes and avoid ambiguity in geological interpretation. The application of high-resolution seismic surveys in the Indian context is challenging as the delineation of thin non-coal layers within the coal layer requires a very high seismic data resolution. However, conventional seismic processing techniques fail to resolve thin coal/non-coal layers and faults, which is crucial for the precise estimation of coal resources and mine economics. To address these issues, we applied the inverse continuous wavelet transform deconvolution (ICWT-Decon) technique to post-stack depth-migrated seismic sections. We examined the feasibility of the ICWT-Decon technique in both a synthetic post-stack depth-migrated model and 2D/3D seismic data from the North Karanpura and Talcher Coalfields in Eastern India. The results offered enhanced seismic sections, attributes (similarity and sweetness), and acoustic inversion that aided in the precise positioning of faults and the delineation of a thin non-coal layer of 4.68 m within a 16.7 m coal seam at an approximate depth of 450 m to 550 m. This helped in the refinement of the resource estimation from 74.96 MT before applying ICWT-Decon to 55.92 MT afterward. Overall, the results of the study showed enhancements in the seismic data resolution, the better output of seismic attributes, and acoustic inversion, which could enable more precise lithological and structural interpretation. Full article

To analyze the impact of volcanic rocks in the Offshore Indus Basin on hydrocarbon reservoir formation, seismic data interpretation, seismic data inversion, and sea–land correlation analysis were carried out. The results show that, longitudinally, volcanic rocks are mainly distributed at the top of the Cretaceous system or at the bottom of the Paleocene, and carbonate rock platforms or reefs of the Paleocene–Eocene are usually developed on them. On the plane, volcanic rocks are mainly distributed on the Saurashtra High in the southeastern part of the basin. In terms of thickness, the volcanic rocks revealed by drilling in Karachi nearshore are about 70 m thick. We conducted sparse spike inversion for acoustic impedance in the volcanic rock area. The results show that the thickness of the Deccan volcanic rocks in the study area is between 250 and 750 m which is thinning from southeast to northwest. Based on sea–land comparison and comprehensive research, the distribution of volcanic rocks in the Indian Fan Offshore Basin played a constructive role in the Mesozoic oil and gas accumulation in the Indus offshore. Therefore, in the Indian Fan Offshore Basin, attention should be paid to finding Mesozoic self-generated and self-stored hydrocarbon reservoirs and Cenozoic lower-generated and upper-stored hydrocarbon reservoirs. Full article

Engineers need to know properties of shallow marine sediments to build piers, pipelines and even offshore windfarms. We present a method for estimating the density, P velocity and thickness of these sediments. The traveltime inversion of primary and multiple reflections enables their semiquantitative estimation in marine surveys when using a minimal acquisition system such as a monochannel Boomer. Picking errors, ambient noise and interfering events lead to significant errors in the estimates. Similar, albeit milder, instabilities occur when inverting the signal amplitudes to determine the reflectivity of the layer interfaces. In this paper, we introduce a coupling between the separate inversion of amplitudes and traveltimes to obtain a better Earth model. The P velocity shows up in two stable terms provided by the separate inversions: the acoustic impedance of shallow sediments (through the amplitudes) and the transit time across the sediment layer (through the traveltimes). We couple the two inversion engines by imposing a smoothness condition on velocity and density and thickness of the layer while keeping the impedance and traveltime constant. We thus exploit the ambiguity of the solution to introduce geological criteria and reduce the noise contribution. We validated the proposed method with synthetic and real data. Full article

The seismic image produced by pre-stack depth migration is more accurate and has clearer geological significance than the time image. However, the waveform of the depth-domain seismic image is affected not only by depth-dependent velocity variation but also by media attenuation, resulting in strong spectral variation of depth-domain seismic data. Therefore, depth-domain seismic inversion is still challenging. We propose a wavelet extraction method of attenuation media based on the generalized seismic wavelet, to address this issue. Then, the estimated depth-domain wavelets were applied to the direct acoustic impedance inversion. First, we investigated the effect of attenuation media on depth-domain source wavelets and derived an analytical formula for the depth-domain wavelets of attenuation media. Next, the time-domain generalized seismic wavelet was extended to the depth domain, which was utilized to study the feasibility of using the generalized seismic wavelet to characterize the seismic wavelet of the depth-domain attenuation media. Based on the orthogonal matching pursuit, we propose a method to extract the depth-domain generalized seismic wavelet directly from depth-domain seismic data. Finally, we applied this method to the depth-domain direct acoustic impedance inversion of a 3D field data example. Tests on the synthetic and 3D field datasets show that the proposed method can correctly extract the depth-domain seismic wavelet of attenuation media and attain direct inversion of the depth-domain acoustic impedance with high accuracy. Therefore, our method is effective and has robust potential in reservoir characterization, fluid prediction, and attribute extraction in the depth domain. Full article

An accurate petrophysical model of the subsurface is essential for resource development and CO₂ sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, V_clay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% V_clay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO₂ geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO₂ saturation changes for monitoring purposes and simulate the effect. In the first 10% CO₂ substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO₂ saturation values, and S-wave velocity (V_s) increases with CO₂ saturation. Our analysis aspires to assist future reservoir studies and CO₂ sequestration in similar fields. Full article

Low frequency acoustic methods are a common tool for seabed stratigraphy mapping. Due to the efficiency in seabed mapping compared to geotechnical methods, estimating geotechnical sediment properties from acoustic surveying is attractive for many applications. In this study, co-located geotechnical and geoacoustic measurements of different seabed sediment types in shallow water environments (<5 m of water depth) are analyzed. Acoustic impedance estimated from sediment properties based on laboratory testing of physical samples is compared to acoustic impedance deduced from CHIRP sonar measurements using an inversion approach. Portable free fall penetrometer measurements provided in situ sediment strength. The results show that acoustic impedance values deduced from acoustic data through inversion fall within a range of ±25% of acoustic impedance estimated from porosity and bulk density. The acoustic measurements reflect variations in shallow sediment properties such as porosity and bulk density (~10 cm below seabed surface), even for very soft sediments (s_u < 3 kPa) and loose sands (~20% relative density). This is a step towards validating the ability of acoustic methods to capture geotechnical properties in the topmost seabed layers. Full article

The unreliable prediction of the low-frequency components from inverted acoustic impedance causes uncertainty in quantitative seismic interpretation. To address this issue, we first calculate various seismic and geological attributes that contain low-frequency information, such as relative geological age, interval velocity, and integrated instantaneous amplitude. Then, we develop a method to predict the low-frequency content of seismic data using these attributes, their high-frequency components, and recurrent neural networks. Next, we test how to predict the low-frequency components using stacking velocity obtained from velocity analysis. Using all the attributes and seismic data, we propose a supervised deep learning method to predict the low-frequency components of the inverted acoustic impedance. The results obtained in both synthetic and real data cases show that the proposed method can improve the prediction accuracy of the low-frequency components of the inverted acoustic impedance, with the best improvement in a real data example of 57.7% compared with the impedance predicted using well-log interpolation. Full article

Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role. Full article

Reverse Time Migration (RTM) is a preferred depth migration method for imaging complex structures. It solves the complete wave equation and can model all types of complex wave propagation with no dip limitation. Reverse time migration using the inverse scattering imaging condition produces structural images with an amplitude approximate to the reflectivity, which is a composite effect of the impedance and velocity changes in the acoustic media with variable velocity and density. In this study, we present a modified inverse scattering imaging condition to separate the effect of the impedance and velocity perturbations from the reflectivity. The proposed imaging condition is designed to predict the relative impedance perturbation by selecting near-angle reflections during common-shot RTM. We validate our approach on synthetic models and show that the proposed method can estimate reliable impedance perturbation. Full article

A growing research interest currently exists in the use of paper as a building material. This work aims to present the results of a measurement campaign developed on innovative waste paper-based building components. The research was carried out in Southern Italy and used some local by-product aggregates. Three different mixture designs were developed in the laboratory by adding three kinds of biomass to a pulp paper blend: fava bean residues (FB), sawdust powder (SP) and coffee grains (CG) extracted from exhausted chaffs. A physical characterization was carried out measuring the bulk density and bulk porosity. Scanning Electron Microscopy (SEM) analysis of the single aggregates was followed by a microstructure analysis of the final components. Bulk density evaluation showed a range between 200 and 348 kg·m⁻³. Furthermore, thermal performances were measured; the thermal conductivity of the experimented samples ranged from 0.071 to 0.093 W·m⁻¹·K⁻¹, thus it is possible to classify the tested materials as thermal insulators. Moreover, the acoustic properties were evaluated and tested. The normal incidence sound absorption coefficient was measured by the impedance tube on cylindrical specimens. In general, a different behavior was observed between the upper and lower base of each specimen due to the manufacturing process and the shrinkage caused by the different interactions occurring between the aggregates and the pulp paper waste; for example, the presence of sawdust reduced shrinkage in the final specimens, with consequent smaller physical variations among the two faces. The correlation existing between the manufacturing process and the microstructural properties was also investigated by the estimation of the non-acoustical parameters using the inverse method and taking into account the JCA (Johnson, Champoux and Allard) model as a reference. Full article

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve nonlinear inverse problems in recent years. This research obtained an acoustic impedance profile by feeding seismic profile and background impedance into a well-trained self-attention U-Net. The U-Net got convergence by appropriate iteration, and the output predicted the impedance profiles in the test. To value the quality of predicted profiles from different perspectives, e.g., correlation, regression, and similarity, we used four kinds of indexes. At the same time, our results were predicted by conventional methods (e.g., deconvolution with recursive inversion, and TV regularization) and a 1D neural network was calculated in contrast. Self-attention U-Net showed to be robust to noise and does not require prior knowledge. Furthermore, spatial continuity is also better than deconvolution, regularization, and 1D deep learning methods in contrast. The U-Net in this paper is a type of full convolutional neural network, so there are no limits to the shape of the input. Based on this, a large impedance profile can be predicted by U-Net, which is trained by a patchy training dataset. In addition, this paper applied the proposed method to the field data obtained by the Ceduna survey without any label. The predictions prove that this well-trained network could be generalized from synthetic data to field data. Full article

Modelling of sound propagation in porous media generally requires the knowledge of several transport properties of the materials. In this study, a three-parameter analytical model that links microstructure properties of sintered metal fibre materials and non-acoustical parameters of the JCAL model is used and modified, and two heuristic approaches based on the established model for inverse acoustic characterisation of fibrous metal felts are developed. The geometric microstructure of sintered fibrous metals is simplified to derive the relationship between pores and fibre diameters. The new set of transport parameters in the modified three-parameter model can cover two controllable parameters during the fabrication process of fibrous metals. With two known transport parameters, six sintered specimens are characterised using a deterministic algorithm, and a satisfactory result is achieved in fitting the normalised surface impedance measured by an acoustic measurement system. Moreover, the forward evaluation shows that our modified three-parameter theoretical model is capable of yielding accurate results for the sintered metal fibre materials. A numerical investigation of the complete inverse acoustic characterisation of fibrous metals by a global non-deterministic algorithm indicates that inversion from two porous material properties is preferable to the normalised surface impedance. Full article