Seismic reservoir characterization in data-limited settings is often hindered by sparse wells, limited resolution, and ambiguous facies delineation. This study develops a convolutional neural network seismic (CNNS) workflow that combines rock-physics-driven synthetic data and transfer learning to improve prediction of elastic properties and lithofacies. Pseudo-wells generated from rock physics models and nearby wells are used to simulate AVO gathers and multi-attribute seismic volumes, which are first used to pre-train the CNNS; the fully connected layers are then fine-tuned with real seismic and well-log data. Elastic property prediction (P-impedance, porosity, and density) is systematically benchmarked against conventional pre-stack seismic inversion (PSSI), a Fully Connected deep neural network (DNN), and a DNN trained with synthetic data (DNNS). Quantitative comparison shows that CNNS yields the most reliable elastic property volumes, with overall R2 values of 0.9655 for Pimpedance (0.9749 at a blind well), 0.9644 for density, and 0.9695 for porosity. Variogram and spatial autocorrelation analyses demonstrate that CNNS best preserves vertical resolution and anisotropic lateral continuity, whereas DNNS tends to over-smooth and PSSI/DNN exhibit higher short-wavelength noise. Based on these CNNS-derived P-impedance, porosity, and density volumes selected because they provide the most geologically consistent elastic fields lithofacies prediction is performed exclusively with CNNS, without direct facies comparison to PSSI, DNN, or DNNS. Facies are classified into four categories: non-reservoir mudstone and low, medium, and high porosity sandstone. The CNNS-based facies model shows excellent agreement with well-log interpretations and channel architecture, achieving classification accuracies of 91.02 % (mudstone), 97.05 % (low-porosity sand), 100 % (medium-porosity sand), and 98.80 % (high-porosity sand. The resulting 3D facies distribution delineates laterally continuous high-quality sandstone bodies and discontinuous injectites, providing a robust basis for targeting sweet spots and optimizing drilling strategies in complex reservoirs.

The inversion of pre-stack wave impedance is closely related to the parameters such as incident angle,P-wave velocity,S-wave velocity,and density.Compared to post-stack inversion,the inclusion of both P-to S-wave velocities allows for the derivation of more comprehensive elastic parameters.This enhances the ability to differentiate lithology and hydrocarbon-bearing formations,providing a more accurate foundation for drilling decisions.Shear-wave velocity data are particularly critical for pre-stack impedance inversion.However,in the T block,only one set of S-wave logging data is available,and its reliability is uncertain due to factors such as mud invasion.As a result,accurately estimating shear-wave velocity especially its hydrocarbon-sensitive variations are a crucial step in elastic parameter inversion for pre-stack analysis.Given the characteristics of the main target formations,KT-Ⅰ and KT-Ⅱ,in the T block,porous carbonate reservoirs with strong heterogeneity,this study proposes a shear-wave velocity estimation method based on an empirical Poisson's ratio model.This approach effectively enhances the sensitivity of pre-stack seismic attributes to hydrocarbons.Through test processing,parameters of the Poisson's ratio-based estimation model were selected to the geological conditions of the T Block.A reservoir-sensitive threshold for the vP/vS ratio was identified as 1.8.Based on the vP/vS parameters of KT-ⅡG formation,the thickness of effective reservoir is predicted.The predicted thickness is more than 85％consistent with well verification,which provides a strong support for exploration and evaluation of well location deployment.

The Tajik Basin is located on the eastern edge of the Central Asian segment of the Tethyan tectonic domain. The basin underwent intense tectonic transformation during the Himalayan period, resulting in complex structural styles, unclear original sedimentary characteristics and oil and gas geological conditions, and a complex process of oil and gas accumulation, which restricts the further evaluation of the basin's exploration potential. Studying the Tajik Basin in the macro background of the Tethys tectonic domain, the tectonic sedimentary evolution of the Tethys tectonic domain has a significant effect on the basin's tectonic evolution, sedimentary characteristics, and oil and gas accumulation conditions. The Tajik Basin has gone through four stages of tectonic evolution: the Late Permian to Triassic was the stage of back arc foreland basin; the Jurassic period was the stage of back arc extensional faulting depression; the Cretaceous-Paleogene period was the stage of depression basins; and the Neogene is the stage of the regenerated foreland basins. Through field geological surveys and analysis of outcrop samples, it has been determined that the Tajik Basin has developed three sets of source rocks: the Middle and Lower Jurassic, Cretaceous, and Paleogene. Among them, the organic matter abundance of the Middle and Lower Jurassic is relatively high, most of them are in the mature stage, and they are primarily gas-generating source rocks. The Cretaceous and Paleogene source rocks are mainly oil generating and in a low-mature state. There are four sets of reservoirs developed in the Tajik Basin: Middle-Upper Jurassic carbonate rocks, Lower Cretaceous clastic rocks, Upper Cretaceous carbonate rocks and Paleogene carbonate rocks. Comprehensive research shows that the Tajik Basin mainly develops three types of oil and gas reservoirs: Jurassic carbonate gas reservoirs, distributed in the southwestern Gissar Uplift and Surhan Depression in the western part of the basin; Paleogene carbonate reservoirs, distributed in the southern Vakhsh Depression and the eastern Kuliabu Depression; and multi layer-multi lithology oil and gas reservoirs, distributed in the northern Dushanbe Depression. The primary controlling factor for the three types of oil and gas reservoirs is tectonic movement, which forms traps and simultaneously reshapes the reservoirs, ultimately leading to effective accumulation of oil and gas. The distribution of oil and gas in the Tajik Basin is characterized by "west gas and east oil, west more and east less, west pre-salt and east post-salt, and pre-salt gas and post-salt oil". Affected by the regional tectonic movements of the Tethys rich oil and gas tectonic domain, the basin has high-quality hydrocarbon source rocks, reservoirs, and cap rock conditions. The pre-salt Jurassic has the potential to form large natural gas reservoirs, while the post-salt Cretaceous and Paleogene still have further potential for exploration.

Reservoir characterization through seismic data analysis is essential for exploration and production in the petroleum industry. However, seismic-to-well tie discrepancies, limited availability of high-quality well data, and resolution constraints pose a reliability challenge. While previous studies offer valuable insights, they still struggle to achieve high-resolution predictions in a complex geologically environment given high reliance on well data. This study integrates synthetic data-driven techniques with real data, including convolutional neural networks (CNN) and transfer learning, to improve seismic reservoir characterization. We utilize nearby well statistics and a rock physics model (RPM) to simulate pseudo wells representing various geological scenarios. Synthetic seismic gathers are generated from these pseudo wells, which are based on RPM and local well control, to train the CNN. Transfer learning is then applied to adapt the CNN to better distinguish between real and synthetic data, enhancing reservoir predictions. A comparative analysis of P-impedance predictions from three methodologies: theory-driven Pre-Stack-Seismic-Inversion (TDSI), Deep-Neural-Network (DNN), and our CNN approach, showed that CNN achieved nearly 97

Lithofacies identification plays a pivotal role in understanding reservoir heterogeneity and optimizing production in tight sandstone reservoirs. In this study, we propose a novel supervised workflow aimed at accurately predicting lithofacies in complex and heterogeneous reservoirs with intercalated facies. The objectives of this study are to utilize advanced clustering techniques for facies identification and to evaluate the performance of various classification models for lithofacies prediction. Our methodology involves a two-information criteria clustering approach, revealing six distinct lithofacies and offering an unbiased alternative to conventional manual methods. Subsequently, Gaussian Process Classification (GPC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models are employed for lithofacies prediction. Results indicate that GPC outperforms other models in lithofacies identification, with SVM and ANN following suit, while RF exhibits comparatively lower performance. Validated against a testing dataset, the GPC model demonstrates accurate lithofacies prediction, supported by synchronization measures for synthetic log prediction. Furthermore, the integration of predicted lithofacies into acoustic impedance versus velocity ratio cross-plots enables the generation of 2D probability density functions. These functions, in conjunction with depth data, are then utilized to predict synthetic gamma-ray log responses using a neural network approach. The predicted gamma-ray logs exhibit strong agreement with measured data (R2 = 0.978) and closely match average log trends. Additionally, inverted impedance and velocity ratio volumes are employed for lithofacies classification, resulting in a facies prediction volume that correlates well with lithofacies classification at well sites, even in the absence of core data. This study provides a novel methodological framework for reservoir characterization in the petroleum industry.

The Longwangmiao Formation gas reservoir in Sichuan is a large-scale marine carbonate gas reservoir discovered in China, which controlled by various factors such as structures, faults and fractures. The strong heterogeneity, rapid lateral changes and complex gas-water relationships affect the production capacity construction of the block. Thus, it is necessary to study the reservoir characteristics and the gas-water distribution. The rock physical analysis of multiple well sections in the research area shows that the gas bearing reservoirs have low P-S wave velocity ratio, low density and low P wave impedance. Density parameter is a sensitive elastic parameter for effectively identifying high gas bearing reservoirs. The accurate density inversion results could lay a good foundation for the precise description and quantitative prediction of gas reservoirs. In the study, the objective function of the coefficient matrix for density inversion was improved, and the contribution to the objective function was quantitatively weighted using Mohs distance instead of Euclidean distance. The new method effectively improves the problem of ill conditioned coefficient matrix in the density inversion process and obtained more accurate density inversion results. The research results indicate that the central part of the main area of the Longwangmiao Formation gas reservoir in the GM area exhibits low density values and good gas bearing properties of the reservoir. The research results are highly consistent with the actual logging results, effectively support the efficient development of the Longwangmiao Formation carbonate gas reservoir in the study area.

The Mahu sag slope area, which holds significance as an oil and gas resource, still have some underexplored regions because of structural mismatches, presenting a potential challenge to be properly addressed. To resolve, this study conducts a comprehensive investigation concerning structural characteristics, fault combinations, favorable reservoir distribution, reservoir control factors, and oil-water distribution characteristics within the Triassic Baikouquan formation, evaluating the impact of depositional environments and sedimentary dynamics on reservoir quality. For this purpose, constrained sparse spike inversion and seismic waveform indication inversion were employed to comparatively evaluate oil and gas reservoirs, further integrating petrophysical and geological data with geological modeling to enhance accuracy in complex structural geology and enable high-precision reservoir prediction. The findings elucidated the distribution range of the Baikouquan formation and the location of oil reservoir sand bodies, as exemplified by well B and identified potential hydrocarbon traps, offering valuable insights into reservoir performance. It demonstrated comparatively reliable effects and considerable predictability power of seismic waveform indication inversion. These outcomes provide a strong foundation for future evaluations and multi-layer system deployment in the region by serving as a novel valuable framework for subsequent development activities not only in the Mahu sag but also in similar regions.

Geoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile–quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) and qualitative log curve assessment through three wells (X4, X5, X6) in complex geological formation to distinguish coal from tight sand and shale. Also, we identify the reservoir rock typing (RRT), gas-bearing and non-gas bearing potential zones. Results showed gamma-ray and resistivity logs are not reliable tools for coal identification. Further, coal layers highlighted high acoustic (AC) and neutron porosity (CNL), low density (DEN), low photoelectric, and low porosity values as compared to tight sand and shale. While, tight sand highlighted 5–10% porosity values. The SOM and clustering assessment provided the evidence of good-quality RRT for tight sand facies, whereas other clusters related to shale and coal showed poor-quality RRT. A t-SNE algorithm accurately distinguished coal and was used to make CNL and DEN plot that showed the presence of low-rank bituminous coal rank in study area. The presented strategy through conventional logs shall provide help to comprehend coal-tight sand lithofacies units for future mining.

Reservoir characterization is a vital task within the oil and gas industry, with the identification of lithofacies in subsurface formations being a fundamental aspect of this process. However, lithofacies identification in complex geological environments with high dimensions, such as the Lower Indus Basin in Pakistan, poses a notable challenge, especially when dealing with limited data. To address this issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), and self-organizing map (SOM). We utilized these proposed approaches to assess their performance in scenarios with varying core sample availability, specifically evaluating their effectiveness in identifying lithofacies within the Lower Goru formation of the middle Indus Basin. The study reveals that in scenarios with a limited number of core samples, MRGC is the preferred choice, while KNN or MRGC is more suitable for larger datasets. The results demonstrate the superior performance of MRGC and KNN in lithofacies identification within the specified geological environment, with SOM following closely behind, and ANN exhibiting comparatively lower efficacy. The accurate identification of lithofacies from the selected model is complemented by the application of the truncated Gaussian simulation method for facies modeling. Comparative results confirm the excellent agreement between the model identification of lithofacies from well logs and electro-facies obtained from the truncated Gaussian simulation electro-facies volume. This study highlights the crucial role of selecting the right machine learning approach for precise lithofacies identification and modeling in complex geological environments. The comparative analysis provides practitioners in the petroleum industry with insights into the strengths and limitations of each method, enhancing existing knowledge. In conclusion, this research emphasizes the significance of comprehensive research and method selection for advancing lithofacies identification in diverse formations or study areas, ultimately benefiting the broader field of subsurface characterization in the petroleum industry.

Shale samples from the Ordovician Wulalike Formation at the western margin of the Ordos Basin are studied to define the types, microstructures and connectivity of pores as well as the relationships between the pore structures and gas content of the samples by using experimental techniques such as high-resolution field emission scanning electron microscopy (FESEM), mercury injection capillary pressure (MICP), low-temperature nitrogen adsorption (LTNA), CO2 adsorption, and focused ion beam scanning electron microscopy (FIB-SEM). The results show that the shale has 10 different lithofacies, typical mixed sedimentary characteristics, and poorly developed pores. The reservoir space mainly consists of intercrystalline pores, dissolution pores, intergranular pores, and micro-fissures, with organic pores occasionally visible. The pore size is mostly within 0.4–250 nm range but dominated by micropores and mesopores less than 20 nm, with pore numbers peaking at pore sizes of 0.5 nm, 0.6 nm, 0.82 nm, 3 nm, and 10 nm, respectively. The pores are poorly connected and macropores are rarely seen, which may explain the low porosity and low permeability of the samples. Samples with high content of organic matter and felsic minerals are potential reservoirs for oil and gas with their favorable physical properties and high connectivity. The pores less than 5 nm contribute significantly to the specific surface area and serve as important storage space for adsorbed gas.

Geophysical reservoir characterization is a significant task in the oil and gas industry and elastic logs prediction of subsurface formations is a fundamental aspect of this process. However, elastic log prediction in a high-dimensional and complex geological environment, such as the Lower Indus Basin Pakistan, poses a significant challenge where traditional empirical methods often fail to provide competitively accurate results. Therefore, this study proposes a novel machine learning approach that combines unsupervised clustering (K-means) and ensemble-based machine learning (random forest) to improve prediction accuracy. By clustering data based on statistical similarity and ensemble algorithms to each cluster, the methodology addresses the challenges of sonic log prediction in the Lower Indus Basin (Pakistan). This approach was evaluated using real-world data, outperforming several baseline methods with a root mean square error of 98% accuracy. Its effectiveness to predict elastic log makes it a valuable tool in reservoir characterization, earthquake analysis, and geothermal energy exploration. Overall, combining this methodology with other techniques can enhance seismic data analysis and enable better decision-making in the oil and gas industry. This novel approach presents an effective solution for predicting sonic log in the Lower Indus Basin and contributes to advancements in geophysical reservoir characterization.

The Yenisei-Khatanga Basin is located on the Taymer Peninsula. It is similar in origin to the West Siberia Basin. It belongs to the Mesozoic rift-depression basin. As a whole, it represents a long and narrow depression belt with a northeast-southwest trend. The tectonic units include In the central depression, central uplift area and slope area, the Jurassic-Cretaceous clastic rock sedimentary stratum in the central depression zone is 6000 m thick. There are mainly two sets of hydrocarbon-bearing systems in the basin, the Middle-Lower Jurassic and the Upper Jurassic-Cretaceous. Formation; five sets of accumulation assemblages developed in the thick Jurassic-Cretaceous clastic rock interaction deposition, the Upper Jurassic Yanovstanov Formation and the Upper Cretaceous Dorozhkov Formation are two sets of areas In addition, several sets of local mudstone caprocks are developed. The trap types of the discovered oil and gas fields in the basin include anticline traps and tectonic-lithological and lithological traps, which are mainly located in the west of the central uplift area and the slope area of the central depression. The Yenisei-Hartanger Basin is a low-exploration basin with great oil and gas potential. The lithologic traps in the Neocomian wedge deposits in the southern and northern margins of the central depression of the basin are the main exploration targets.

For the successful discovery and development of tight sand gas reserves, it is necessary to locate sand with certain features. These features must largely include a significant accumulation of hydrocarbons, rock physics models, and mechanical properties. However, the effective representation of such reservoir properties using applicable parameters is challenging due to the complicated heterogeneous structural characteristics of hydrocarbon sand. Rock physics modeling of sandstone reservoirs from the Lower Goru Basin gas fields represents the link between reservoir parameters and seismic properties. Rock physics diagnostic models have been utilized to describe the reservoir sands of two wells inside this Middle Indus Basin, including contact cement, constant cement, and friable sand. The results showed that sorting the grain and coating cement on the grain’s surface both affected the cementation process. According to the models, the cementation levels in the reservoir sands of the two wells ranged from 2% to more than 6%. The rock physics models established in the study would improve the understanding of characteristics for the relatively high Vp/Vs unconsolidated reservoir sands under study. Integrating rock physics models would improve the prediction of reservoir properties from the elastic properties estimated from seismic data. The velocity–porosity and elastic moduli-porosity patterns for the reservoir zones of the two wells are distinct. To generate a rock physics template (RPT) for the Lower Goru sand from the Early Cretaceous period, an approach based on fluid replacement modeling has been chosen. The ratio of P-wave velocity to S-wave velocity (Vp/Vs) and the P-impedance template can detect cap shale, brine sand, and gas-saturated sand with varying water saturation and porosity from wells in the Rehmat and Miano gas fields, both of which have the same shallow marine depositional characteristics. Conventional neutron-density cross-plot analysis matches up quite well with this RPT’s expected detection of water and gas sands.

Paleogeomorphology is the main factor affecting the development of reservoirs such as reef, shoal, fracture and cave in weathered crust and so on. The major method for paleogeomorphology restoration commonly used in the industry is to roughly describe the paleogeomorphology state at that time by drawing the contour map of the paleogeomorphology. This method belongs to qualitative expression and do not reach the accuracy corresponding to geological deposition, well logging response and other parameters. At present, there are many methods to improve the accuracy of paleogeomorphology restoration, but they basically need the assistance of other parameters. The commonly used paleogeomorphology restoration methods mainly include residual thickness method that analyzes thickness, stratigraphic restoration method, back-stripping method, filling-leveling method, sedimentology analysis method and sequence stratigraphic restoration method. The Composite impression method and sedimentology method are combined to restore the paleogeomorphology of terrigenous clastic sediments of Visean before KT-II deposition in the eastern margin of the Precaspian Basin, which is conducive to study the control of Visean paleogeomorphology on the development of reefs and shoals of overlying carbonate strata. The Visean is relatively flat as a whole. There are intra-platform sags in the northwest, banded down-cutting valleys in the west and local bulges in the middle. The shoals are mainly developed at the slope break zone of a bulge.

Irregular measurements may occur during the drilling process due to unconsolidated formation resulting in poor signal recordings by the logging tool. This affects the quality of data acquisition and the accuracy of elastic logs, such as density and velocity profiles, in reservoir characterization. It is of paramount importance to ensure the stability of the wireline-logging tool and to prevent compromising measurements of the formation's physical properties. While previous literature focused on the application of different machine learning (ML) algorithms for well logging, their application in a particular domain implied a narrow methodological utility for researchers. Therefore, this study combined two superior techniques of ML, supervised and unsupervised, for enhancing the elastic log response to ultimately help us to enhance reservoir characterization and interpretation. First, the density-based spatial clustering of applications with noise (DBSCAN) was used for outlier detection, and then, feature selection was used to identify highly correlated logs, which helped in rebuilding the density log. After successful ranking, the scaled-down features were carried forward to construct a regression model for density logs rebuilding. The comparative results confirmed the high accuracy of porosity estimated from rebuilt density log compared to that of core data. Consequently, it reduces cumbersome human efforts and time.

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model's input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation.

Precision porosity and facies determinations are critical in reducing drilling uncertainty and increasing hydrocarbon recoveries from heterogeneous sources. The porosity and facies distribution of the Taiyuan-Shanxi Formations (T9c-T9d), and Shihezi-1 Formation (T9d-T9e) within the Hangjinqi area are uncertain and no studies have covered the spatial distribution on a regional scale. The heterogeneous nature of coal, mudstone, and sandstone makes it challenging to comprehend the distribution of porosity and lithofacies. Also, the seismic resolution is not able to resolve the reservoir heterogeneity. Therefore, we have employed regional 3D seismic and well logs by utilizing the advanced acoustic impedance inversion to accomplish our study. Results of petrophysical analysis conducted on the well J32 showed that Shihezi-1 and Shanxi-1 Formations have potential gas-saturated zones. Crossplot analysis distinguished the lowest impedance coal from the highest impedance tight sandstone facies. The outcomes of the constrained sparse spike inversion (CSSI) reliably distinguished the coal facies from the channel-tight sandstone facies. The tight sandstone facies showed the highest impedance values as compared to coal and mudstone facies on the absolute acoustic impedance section. Impedance and porosity maps of T9d and T9e suggested the presence of a maximum porosity (8%–12% for T9d, and 5%–10% for T9e), and maximum distribution of tight sandstone facies, while T9c shows the lowest porosity (0%–6%) and lowest impedance values due to the presence of coal facies. Thick braided fluvial channels are evident on the T9d impedance and porosity maps, making it the most favorable horizon to produce the maximum gas. Whereas, T9c shows the least distribution of sandstone facies making it the least favorable. We propose that the zones of maximum porosity on the T9c, T9d, and T9e horizons can be exploited for future gas explorations.

The Hangjinqi area was explored for natural gas around 40 years ago, but the efficient consideration in this area was started around a decade ago for pure gas exploration. Many wells have been drilled, yet the Hangjinqi area remains an exploration area, and the potential zones are still unclear. The Lower Shihezi Formation is a proven reservoir in the northern Ordos Basin. This study focuses on the second and third members of the Lower Shihezi Formation to understand the controlling factors of faults and sedimentary facies distribution, aimed to identify the favorable zones of gas accumulation within the Hangjinqi area. The research is conducted on a regional level by incorporating the 3D seismic grid of about 2500 km(2), 62 well logs, and several cores using seismic stratigraphy, geological modeling, seismic attribute analysis, and well logging for the delineation of gas accumulation zones. The integrated results of structural maps, thickness maps, sand-ratio maps, and root mean square map showed that the northwestern region was uplifted compared to the southern part. The natural gas accumulated in southern zones was migrated through Porjianghaizi fault toward the northern region. Well J45 from the north zone and J77 from the south zone were chosen to compare the favorable zones of pure gas accumulation, proving that J45 lies in the pure gas zone compared to J77. Based on the faults and sedimentary facies distribution research, we suggest that the favorable zones of gas accumulation lie toward the northern region within the Hangjinqi area.

The slope zone of Mahu Depression in Junggar Basin contains the largest glutenite oilfield discovered over the world so far. However, because of the mechanism of near-source sedimentation and rapid facies transition, the glutenite reservoirs are strongly heterogeneous which makes it difficult to be discriminated directly from seismic data. To characterize the lateral variation and improve the vertical resolution of glutenite reservoirs, we develop an adaptive impedance inversion method based on the Bayesian theory. In this method, an automatically adjusted damping factor is derived to obtain the best balance between the vertical resolution and the inversion stability according to the noise level. And the trace-by-trace recursive inversion strategy, which uses the inversion result of the previous adjacent trace as the initial model for the next, is adopted to ensure the lateral variation. Synthetic data tests on 1-D and 2-D models verify the adaptive ability and the high resolution advantage of the introduced method. Real data application results in Mahu Oilfield shows that the prediction results of glutenite reservoir have distinct lateral variation and high vertical resolution, with good agreement with the actual drilling and sedimentary trend, which indicates a good application prospect in the heterogeneous glutenite reservoir exploration.