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This study investigates the natural gas hydrates within the Qiongdongnan Basin by integrating well-log and seismic data. Through pre-stack inversion and rock physics analysis, key parameters such as P-wave and S-wave impedances were utilized to distinguish hydrate-bearing formations from other geological bodies. A low-frequency model was constructed using the Inverse Distance Weighting (IDW) algorithm to improve the precision of parameter inversion. This study employs a multi-constraint inversion strategy, incorporating hard constraints from multiple wells and soft constraints from geological frameworks, ensuring reliable inversion results. Findings indicate that hydrate reservoirs are characterized by increased wave velocity and density due to hydrate accumulation, providing insights into the spatial distribution and characteristics of hydrates. This research enhances the understanding of hydrate reservoirs and offers valuable data for exploration in the Qiongdongnan Basin. [ABSTRACT FROM AUTHOR]

Tight oil reservoirs are considered important exploration targets in lacustrine basins. High-quality reservoir prediction is difficult as the reservoirs have complex distributions of depositional facies and diagenesis processes. Previous research has found that the diagenesis process of tight oil sandstones varies greatly in different depositional facies. However, diagenesis variation in different depositional facies is still poorly studied, especially in distributary channels of shallow water delta deposits in lacustrine basins. Based on the description of core samples, the observation of rock slices, the interpretation of well logging data, and the analysis of porosity and permeability data, the differences in the lithofacies types, diagenesis processes, and pore structures of different distributary channels have been clarified. Ultimately, a model of diagenesis and reservoir heterogeneity distribution in the shallow-water delta of Chang 8 Member of the Yanchang Formation in the Caijiamiao area of the Ordos Basin has been established. This research indicates that the main distributary channels in the study area are dominated by massive bedding sandstone lithofacies, while the secondary distributary channels are primarily characterized by cross-bedding sandstone lithofacies. There are significant differences in the compaction, dissolution, and cementation of authigenic chlorite and carbonate among different parts of the distributary channels. Plastic mineral components, such as clay and mica, are abundant in sheet sands, and are more influenced by mechanical and chemical compaction. Influenced by the infiltration of meteoric water and hydrocarbon generation, dissolution pores are relatively well-developed in the underwater distributary channel reservoirs. A large amount of carbonate cementation, such as calcite and siderite, is found within the sandstone at the interface between sand and mud. The occurrence of authigenic chlorite exhibits a clear sedimentary microfacies zonation, but there is little difference in the kaolinite and siliceous cementation among different microfacies reservoirs. Finally, a model of diagenetic differences and reservoir quality distribution within dense sand bodies has been established. This model suggests that high-quality reservoirs are primarily developed in the middle of distributary channels, providing a theoretical basis for the further fine exploration and development of oil and gas in the study area. [ABSTRACT FROM AUTHOR]

Volcanic oil and gas resources are abundant and have been one of the principal focuses of research in the oil and gas industry for a long time. Due to the complexity of the volcanogenesis and the non-homogeneity of the reservoir, the construction of a reasonable volcanic rock petrophysical model to guide the reservoir prediction is the key to success or failure. Unlike conventional sedimentary reservoirs, volcanic rock skeletons have diverse mineralogical compositions and pore structures, making the construction of equivalent petrophysical models highly complicated. This paper proposes a method to construct equivalent petrophysical models of complicated volcanic rocks with multiple mineral components and pore structures utilizing conventional logging curves. In the middle basal volcanic rock development area of the Songnan Fault, the errors in mineral content calculation and transverse wave estimation are less than 10% when comparing the results of elemental capture spectroscopy (ECS) logging and core test analysis; meanwhile, it directs the volcanic rock phase-controlled pre-stack inversion work and upgrades the accuracy of volcanic rock well seismic multi-scale reservoir prediction. [ABSTRACT FROM AUTHOR]

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Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To predict complex reservoir spaces (with developed caves, pores, and fractures), based on the results of full-azimuth depth migration processing, we adopted reverse weighted nonlinear inversion to improve the accuracy of porous reservoir prediction. Scattering imaging three-parameter wavelet transform technology was used to accurately predict small-scale cave bodies. The joint inversion method of velocity and amplitude anisotropy was developed to improve the accuracy of small and medium-sized fracture prediction. The results of multiscale fracture modeling and characterization, interwell connectivity analysis, and connection path prediction are consistent with the production condition. Finally, based on the above prediction findings, favorable reservoir development areas were predicted. The above ideas and strategies have great application value for the efficient exploration and development of complex storage space reservoirs and the optimization of high-yield well locations. [ABSTRACT FROM AUTHOR]

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Abstract The sedimentary environment in the eastern Ordos area of China is complex and contains a large number of transitional facies environments. Yet, there are many characteristics such as large vertical lithology changes, complex lateral sedimentary environment changes, small monolith thickness and large organic matter content changes, which lead to large uncertainty in the prediction of favorable areas for transitional shale gas. As the intricate reservoirs continue to unfold, the conventional linear prediction methods find themselves facing an arduous path to meet the demands of development. The ever-evolving complexity of these reservoirs has outpaced the capabilities of these traditional approaches. It becomes apparent that a more comprehensive and adaptable approach is necessary to navigate the intricacies of these reservoirs and unlock their hidden potential. Therefore, we put forward a method of introducing knowledge graph into transitional shale gas reservoir prediction in the eastern Ordos region by using big data technology. Because artificial intelligence big data relies heavily on data tags, it is particularly important for the construction of tags. Firstly, a top-down knowledge graph in the field of reservoir prediction is constructed to determine the key parameters used in prediction, namely porosity, total organic carbon and brittleness index. Secondly, the decision tree knowledge graph optimization label is constructed in a bottom-up way. The key parameter of this prediction is the knowledge graph obtained according to the professional knowledge of reservoir prediction, so as to optimize the school label of U-net and reduce the workload of artificial judgment. The results of the combination of the two methods are applied to 11 wells in Daji area of Ordos, and the experimental results are consistent with the actual situation of the reservoir. Based on the foundation of theoretical knowledge, this method enhances the efficiency and accuracy of interpretation and evaluation. It provides fundamental and technical support for the selection of favorable areas in shale gas exploration and the evaluation of exploration and development prospects, particularly in transitional shale gas areas, which is innovative and advanced in the field.

Shengli Oilfield has entered the middle and later development stages， and higher requirements for development seismic technologies have been set to develop reservoirs and further enhance oil recovery finely. The distribution and connectivity of conventional reservoirs in Jiyang Depression are complex due to different buried depths， multi-stage tectonic movements， various sedimentary types， and other factors. At the same time， constrained by seismic recognition， the well-seismic correspondences of some reservoirs are unclear， limiting the accuracy of fine reservoir characterization. In response to the development needs of different types of conventional reservoirs in Shengli Oilfield， a series of development seismic technologies have been formed through practice. These technologies include multi-method integrated reservoir prediction technology for middle and shallow fluvial facies reservoirs， integrated fine description technology for fracture systems in complex fault block reservoirs combing fine fault edge depiction and multiple attributes， prediction technology for middle-deep reservoirs with low permeability， and description technology of buried hill reservoirs fusing multiple parameters of “fault-fracture-dissolution composite storage space.” These technologies provide strong support for reservoir development. However， as the target reservoirs for development get deeper， thinner， and smaller and expand into unconventional reservoirs， the development seismic technologies faces higher requirements and challenges. It is necessary to accelerate the integration process of processing and interpretation， strengthen the theoretical research of rock physics， promote the application of OVT domain pre-stack inversion in the description of multi-type oil reservoirs， tackle the fine description technologies of complex fault block-lithofacies reservoirs， break through the technical bottleneck of well-ground joint acquisition and gravity-magnetic-electric-seismic technology， and realize the application of artificial intelligence technology in the development seismic field， so as to further promote reservoir development.

There is great exploration potential for ultra-deep reservoirs in Junggar Basin， and reservoir prediction with seismic data is the main technical means of oil and gas exploration at present. However， due to the low signal-to-noise ratio of seismic data of ultra-deep reservoirs， unclear reservoir-seismic data correspondence， and few actual drilling wells， it is difficult to establish an effective initial model for seismic inversion， which restricts the accuracy and reliability of ultra-deep reservoir prediction. Gravity inversion， as an important quantitative interpretation method， can obtain the characteristics of underground density distribution and provide support for geological interpretation. According to the density model， a relatively reliable low-frequency model can be established for seismic inversion， which can overcome the difficulty of applying seismic data in ultra-deep reservoirs to a certain extent. Meanwhile， the acquisition of gravity data is economical and convenient compared with that of seismic data， and it is easier to be applied in practice. Therefore， a new technique applying gravity inversion to seismic data-based reservoir prediction was developed in this article. Firstly， a quasi-neural network gravity inversion technique based on Gaussian radial basis functions was proposed to solve the gravity inversion problem and improve the resolution and reliability of gravity inversion. Then， the density body obtained by gravity inversion was used as training data， and a neural network was trained together with seismic and logging data to establish the initial model of seismic inversion. Finally， seismic inversion was carried out with the initial model constraints. This technique broke through the application bottleneck of single seismic data in ultra-deep reservoir prediction and overcame the limitations of logging constraints， providing a reliable initial model for seismic inversion. The application of this technique to the prediction of ultra-deep clastic rock reservoirs in Junggar Basin was consistent with the existing geological knowledge， indicating that this method had high practical value and application potential for ultra-deep reservoir prediction and could provide technical support for ultra-deep reservoir exploration.

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Strong reflection recognition and separation technology based on seismic multiwavelet decomposition has been widely used in seismic interpretive processing. This technology can remove the influence of strong shielding and improve the identification accuracy of concealed reservoir. Aiming at the issues of decomposition efficiency and accuracy of multi-wavelet decomposition technology, the instantaneous characteristics of seismic signal were introduced as the initial value, and local scanning parameters were reduced according to the relationship between wavelet parameters. A three-parameter fast wavelet decomposition technology was proposed to improve the accuracy and efficiency of wavelet decomposition. The relationship between formation thickness and strong shielding energy was analyzed through model forward modeling, and the relationship between formation thickness and separation coefficient was established. A new application idea of strong reflection separation with variable coefficient was explored. The new technology was applied to the actual seismic data, and the separation results weakened the influence of strong shielding, highlighted the effective signal of concealed reservoir, and improved the accuracy of reservoir prediction. The strong reflection separation process fully considered the relationship between coal seams with different thickness and separation coefficient, and the separation result was more consistent with the actual geological conditions than conventional methods. [ABSTRACT FROM AUTHOR]

The diagenetic facies of tight oil reservoirs reflect the diagenetic characteristics and micro-pore structure of reservoirs, determining the formation and distribution of sweet spot zones. By establishing the correlation between diagenetic facies and logging curves, we can effectively identify the vertical variation of diagenetic facies types and predict the spatial variation of reservoir quality. However, it is still challenging work to establish the correlation between logging and diagenetic facies, and there are some problems such as low accuracy, high time consumption and high cost. To this end, we propose a lithofacies identification method for tight oil reservoirs based on hybrid intelligence using the Fuyu oil layer of the Sanzhao depression in Songliao Basin as the target area. Firstly, the geological characteristics of the selected area were analyzed, the definition and classification scheme of diagenetic facies and the dominant diagenetic facies were discussed, and the logging response characteristics of various diagenetic facies were summarized. Secondly, based on the standardization of logging curves, the logging image data set of various diagenetic facies was built, and the imbalanced data set processing was performed. Thirdly, by integrating CNN (Convolutional Neural Networks) and ViT (Visual Transformer), the C-ViTM hybrid intelligent model was constructed to identify the diagenetic facies of tight oil reservoirs. Finally, the effectiveness of the method is demonstrated through experiments with different thicknesses, accuracy and single-well identification. The experimental results show that the C-ViTM method has the best identification effect at the sample thickness of 0.5 m, with Precision of above 86%, Recall of above 90% and F1 score of above 89%. The calculation result of the Jaccard index in the identification of a single well was 0.79, and the diagenetic facies of tight reservoirs can be identified efficiently and accurately. At the same time, it also provides a new idea for the identification of the diagenetic facies of old oilfields with only logging image data sets. [ABSTRACT FROM AUTHOR]

The Middle Permian Qixia Formation in the Shuangyushi area, northwestern Sichuan Basin, develops shoal-facies dolomite reservoirs. To pinpoint promising reservoirs in the Qixia Formation, deep thin shoal-facies dolomite reservoirs were predicted using the techniques of pre-stack Kirchhoff-Q compensation for absorption, inverse Q filtering, low-to high-frequency compensation, forward modeling, and facies-controlled seismic meme inversion. The results are obtained in six aspects. First, the dolomite reservoirs mainly exist in the middle and lower parts of the second member of Qixia Formation (Qi2 Member), which coincide with the zones shoal cores are developed. Second, the forward modeling shows that the trough energy at the top and bottom of shoal core increases with increasing shoal-core thickness, and weak peak reflections are associated in the middle of shoal core. Third, five types of seismic waveform are identified through waveform analysis of seismic facies. Type-I and Type-II waveforms correspond to promising facies (shoal core microfacies). Fourth, vertically, two packages of thin dolomite reservoirs turn up in the sedimentary cycle of intraplatform shoal in the Qi2 Member, and the lower package is superior to the upper package in dolomite thickness, scale and lateral connectivity. Fifth, in plane, significantly controlled by sedimentary facies, dolomite reservoirs laterally distribute with consistent thickness in shoal cores at topographical highs and extend toward the break. Sixth, the promising prospects are the zones with thick dolomite reservoirs and superimposition of horstegraben structural traps. [ABSTRACT FROM AUTHOR]

Accurately predicting the external morphology and internal structure of fractured-vuggy reservoirs is of significant importance for the exploration and development of carbonate oil and gas reservoirs. Conventional seismic prediction methods suffer from serious non-uniqueness and low efficiency, while recent advances in deep learning exhibit strong feature learning capabilities and high generalization. Therefore, this paper proposes an intelligent prediction technique for fault-controlled fracture-vuggy reservoirs based on deep learning methods. The approach involves constructing 3D seismic geological models that conform to the geological characteristics of the study area, simulating seismic wavefield propagation, and combining the interpretation results of fractured-vuggy reservoirs. Training sample datasets are separately established for strike-slip faults, karst caves, and fault-controlled fractured-vuggy reservoir outlines, which are then input into the U-Net model in batches for training. This leads to the creation of a deep learning network model for fault-controlled fractured-vuggy reservoirs. The trained network model is applied to the intelligent identification of fault, karst cave, and fault-controlled fracture-vuggy reservoir outlines using actual seismic data from the Shunbei area. A comparison with traditional methods is conducted, and the experimental results demonstrate that the proposed deep learning approach shows excellent performance in the identification and prediction of fault-controlled fractured-vuggy reservoirs. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Seismic inversion is the primary way to obtain subsurface models, lithologic and stratigraphic information. However, seismic elastic parameters inversion and 'discrete lithofacies' identification for complex volcanic reservoirs are usually independent during the whole inversion process. Also, the influence of reservoir lithology on elastic parameters is not always considered directly before lithofacies prediction. This paper proposes a probabilistic pre-stack seismic inversion method for lithofacies and elastic parameters of volcanic reservoirs. Under the framework of Bayesian inversion, considering that the prior probability distribution of elastic parameters of volcanic reservoirs is affected by volcanic lithofacies, a posteriori probability distribution characterized by a mixed probability model is first derived. Then, a single-point-direct sequential simulation stochastic algorithm with simultaneous optimization of multiple solutions is used to simulate the posterior probability distribution of elastic parameters and lithofacies of volcanic reservoirs, which improves the resolution of lithofacies prediction results of volcanic reservoirs. The feasibility and stability of our method are ensured through synthetic and field applications. The prediction results highly agree with logging curves and lithology logging interpretation data. We have improved the resolution of volcanic rock reservoir lithofacies prediction results. In one-dimensional tests, we achieved the prediction of lithofacies and elastic parameters for three types of volcanic lithofacies. The error compared to prior information is no higher than 15%, thereby verifying the method's good noise resistance. [ABSTRACT FROM AUTHOR]

The sedimentary tuff in the second member of Permian Wujiaping Formation in the northern Fuling area of Chongqing is a new type of organic-rich source rock, which is a new field of unconventional gas reservoir exploration. Clarifying the distribution of key sweet spot parameters of the sedimentary tuff series in the second member of Wujiaping Formation is a primary issue in exploration and evaluation. Based on drilling, logging, experimental analysis and seismic data, this paper evaluated the target sweet spot starting from the sedimentary tuff series' organic geochemistry, lithological characteristics, thickness distribution, formation pressure, fracture development and other geological conditions for reservoir formation and enrichment, and focusing on the two key factors of lithology quality and compressible fracture network. The results show that: (1) The sedimentary tuff series in the second member of Wujiaping Formation in the northern Fuling area is in the deep-water shelf facies, with high organic matter abundance. The TOC content is mainly 5%-6%, the Ro value ranges 1.90%-2.44%, the organic matter types are mainly Ⅰ and Ⅱ, and the thickness of high-quality sedimentary tuff section (TOC≥4%) is 15-20 m, with a wide distribution area. (2) The reservoir space of the high-quality sedimentary tuff section is diversified, the porosity is high, the brittle mineral content is high, the formation pressure coefficient is high to ultra high pressure level, and small-micro fracture zone is developed at the hinge zone, reflecting a good gas exploration potential of "self-generation and self-storage". (3) The sedimentary tuff gas reservoir in the second member of Wujiaping Formation in the northern Fuling area has the laws of high quality control of enrichment, overpressure control of reservoir and fracture network control of sweet spot. The sweet spot evaluation criteria for sedimentary tuff series in the second member of Wujiaping Formation are preliminarily established in combination with the main control factors, and three target sweet spots are selected, with an area of 186.4 km2. The L2-L1 well area in the northwest of the exploration area has good oil and gas shows, which can be a favorable target for recent exploration.

This study investigates the natural gas hydrates within the Qiongdongnan Basin by integrating well-log and seismic data. Through pre-stack inversion and rock physics analysis, key parameters such as P-wave and S-wave impedances were utilized to distinguish hydrate-bearing formations from other geological bodies. A low-frequency model was constructed using the Inverse Distance Weighting (IDW) algorithm to improve the precision of parameter inversion. This study employs a multi-constraint inversion strategy, incorporating hard constraints from multiple wells and soft constraints from geological frameworks, ensuring reliable inversion results. Findings indicate that hydrate reservoirs are characterized by increased wave velocity and density due to hydrate accumulation, providing insights into the spatial distribution and characteristics of hydrates. This research enhances the understanding of hydrate reservoirs and offers valuable data for exploration in the Qiongdongnan Basin.

In order to accurately predict reservoirs with similar physical properties between sandstone and surrounding rocks, this study takes Carboniferous-Permian system in southeastern Ordos Basin as an example, and puts forward a multi-attribute probabilistic neural network prediction method based on acoustic time difference compaction correction. Firstly, the time-frequency analysis method is used to divide the frequency of the original acoustic time difference curve, and the low-frequency long-trend correction of all wells is carried out based on the low-frequency components of standard wells. Then, it is frequency-divided and fused with the original high-frequency components to form new acoustic time difference data, and the long-term trend decompaction correction of acoustic time difference is completed, so that the overlapping area of acoustic time difference between reservoir and surrounding rock is reduced. Multi-attribute probabilistic neural network is an algorithm based on sampling points, before reservoir prediction, inversion parameters must be optimized to save calculation time and reduce prediction error. There is a high correlation between longitudinal velocity and lithologic change trend on the inversion profile of sound velocity. The comparison results of various inversions show that the velocity profile of multi-attribute probabilistic neural network has the highest coincidence rate and high resolution with the velocity curve of logging acoustic wave. The example shows that this method has high accuracy in predicting reservoir thickness, especially for the strata with little difference in physical properties between reservoir and surrounding rock. [ABSTRACT FROM AUTHOR]

The dolomite reservoir of the Middle Permian Qixia Formation in the Shuangyushi structure in northwestern Sichuan Basin is deeply buried, thin, and highly heterogeneous. With low-resolution seismic data, it is not easy to identify the reservoir signal and it is difficult to accurately describe the plane distribution of thin reservoirs. Based on high-resolution processing of seismic data, well-seismic combination technology, forward simulation technology, and cross-well profiles, in this study, we defined the waveform characteristics that correspond to different modes of reservoir development, summarized the seismic response modes of reservoirs in the area, optimized sensitive attributes, and finely depicted their plane distribution. The ability of seismic data to identify thin reservoirs was significantly improved after high-resolution processing. According to a comprehensive analysis, the reservoirs of the Qixia Formation in the study area had two response modes: "weak peak" and "complex wave." The waveform clustering attribute was optimized to describe the plane distribution characteristics of the Qixia Formation reservoirs in the study area. Type-I and type-II waveforms were favorable waveforms for reservoir development, and they mainly were distributed in the west of the Devonian denudation line, in a strip along the north–south direction, and mostly along faults. The predictions made with this method agreed well with the actual reservoir development, which provided a reference for the prediction of deep and thin reservoirs in this area. [ABSTRACT FROM AUTHOR]

Heavy oil has high density and viscosity, and exhibits viscoelasticity. Gassmann's theory is not suitable for materials saturated with viscoelastic fluids. Directly applying such model leads to unreliable results for seismic inversion of heavy oil reservoir. To describe the viscoelastic behavior of heavy oil, we modeled the elastic properties of heavy oil with varying viscosity and frequency using the Cole-Cole-Maxwell (CCM) model. Then, we used a CCoherent Potential Approximation (CPA) instead of the Gassmann equations to account for the fluid effect, by extending the single-phase fluid condition to two-phase fluid (heavy oil and water) condition, so that partial saturation of heavy oil can be considered. This rock physics model establishes the relationship between the elastic modulus of reservoir rock and viscosity, frequency and saturation. The viscosity of the heavy oil and the elastic moduli and porosity of typical reservoir rock samples were measured in laboratory, which were used for calibration of the rock physics model. The well-calibrated frequency-variant CPA model was applied to the prediction of the P- and S-wave velocities in the seismic frequency range (1–100 Hz) and the inversion of petrophysical parameters for a heavy oil reservoir. The pre-stack inversion results of elastic parameters are improved compared with those results using the CPA model in the sonic logging frequency (∼10 kHz), or conventional rock physics model such as the Xu-Payne model. In addition, the inversion of the porosity of the reservoir was conducted with the simulated annealing method, and the result fits reasonably well with the logging curve and depicts the location of the heavy oil reservoir on the time slice. The application of the laboratory-calibrated CPA model provides better results with the velocity dispersion correction, suggesting the important role of accurate frequency dependent rock physics models in the seismic prediction of heavy oil reservoirs. [ABSTRACT FROM AUTHOR]

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Acoustic wave features, including velocity dispersion and attenuation, induced by fluid flow in porous media have attracted significant attention in reservoir exploration. To enhance the quantitative understanding of these features, various wave propagation mechanisms have been developed. It has been discovered that wave dispersion and attenuation are associated with multiple reservoir parameters, each with different sensitivity. It is difficult to distinguish the effects of individual physical parameters on acoustic features by traditional wave equations. Considering the ability of deep neural networks (DNNs) in establishing the relationships between two datasets, a fully connected DNN has been employed as a surrogate rock physics model, and the Shapley additive explanations model (SHAP) based on this DNN has been introduced to evaluate the contributions of different parameters. In this study, the classic White model is used to generate datasets for training the DNN. Datasets include seven parameters (bulk modulus, shear modulus, and density of the solid matrix, frequency, porosity, fluid saturation, and permeability), along with velocity dispersion and attenuation. By embedding SHAP into the trained DNN, the presented ShaRock algorithm allows for a clear quantification of the contributions of various reservoir parameters to acoustic features. Furthermore, we analyze the underlying interactions between two parameters by using their combined quantified contributions to the features. The application of this proposed algorithm, which is based on wave propagation mechanisms, demonstrates its potential in providing valuable insights for parameter inversions in hydrocarbon exploration. [ABSTRACT FROM AUTHOR]

The tight sandstone gas reservoir is thin, highly heterogeneous, and its spatial distribution is very complex. Its development is faced with the problems of low gas reservoir drilling rate and poor fracturing effect. In order to overcome the difficulties in the development of horizontal wells in tight gas reservoirs, the real-time steering technology of seismic geological integration horizontal wells based on 3D seismic data of "two wide and one high" is proposed in this paper. The pilot test of 12 horizontal wells has been completed using the proposed technology. The average horizontal section length is 1755 meters, the sandstone length is 1506 meters, the sandstone drilling rate is 85.7%, the effective reservoir length is 1290 meters, the effective reservoir drilling rate is 74.5%, the coincidence rate of seismic prediction is 91.7%, the success rate of one shot is 100%, and the structural prediction error is 0.45 ‰. This technology can effectively improve the drilling rate and fracturing effect of horizontal wells in tight sandstone gas reservoirs, and has guiding significance for the development of horizontal wells in tight sandstone gas reservoirs. [ABSTRACT FROM AUTHOR]

The history and results of petroleum exploration in the Santos Basin, Brazil are reviewed. The regularity of hydrocarbon enrichment and the key exploration technologies are summarized and analyzed using the seismic, gravity, magnetic and drilling data. It is proposed that the Santos Basin had a structural pattern of two uplifts and three depressions and the Aram-Uirapuru uplift belt controlled the hydrocarbon accumulation. It is believed that the main hydrocarbon source kitchen in the rift period controlled the hydrocarbon-enriched zones, paleo-structures controlled the scale and quality of lacustrine carbonate reservoirs, and continuous thick salt rocks controlled the hydrocarbon formation and preservation. The process and mechanism of reservoirs being transformed by CO2 charging were revealed. Five key exploration technologies were developed, including the variable-velocity mapping for layer-controlled facies-controlled pre-salt structures, the prediction of lacustrine carbonate reservoirs, the prediction of intrusive/effusive rock distribution, the detection of hydrocarbons in lacustrine carbonates, and the logging identification of supercritical CO2 fluid. These theoretical recognitions and exploration technologies have contributed to the discovery of deep-water super-large reservoirs under CNODC projects in Brazil, and will guide the further exploration of deep-water large reservoirs in the Santos Basin and other similar regions.