Post-stack Inversion Research Articles

Structural and Reservoir Characteristics of Potential Carbon Dioxide Storage Sites in the Northern South Yellow Sea Basin, Offshore Eastern China

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.

Icehouse mixed carbonate and siliciclastic sequence evolution based on 3D seismic analysis: Insights from the Eastern Shelf of the Permian Basin, Texas

Reciprocal model was introduced for mixed carbonate and siliciclastic sequences within the Virgilian (Upper Pennsylvanian – Gzhelian) and Wolfcampian (Lower Cisuralian) on the Eastern Shelf of the Permian Basin in Texas. This model emphasizes carbonate dominance during transgressive and highstand on the slope, and siliciclastic dominance in the basin during lowstand. Contradictory observations from other mixed basins, such as compositionally mixed lithology in slope cores, accumulation of siliciclastics behind carbonate highs on the outer shelf, off-shelf transport of siliciclastics during highstands, and carbonate deposition on the slope during falling stage, require a re-evaluation of the reciprocal model. This research examines mixed sequences in the Cisco Group and maps lithology distribution by integrating geological and geophysical data, using advanced seismic interpretation techniques, wireline-log crossplot analyses, model-based post-stack inversion, probabilistic neural networks, and supervised Bayesian classification. Observations from the investigation are presented at two scales. In ∼400kyr sequences, relative sea level fall starts with carbonate deposition on the slope while siliciclastics persist near the inner shelf. As the falling stage progresses, siliciclastics reach the shelf edge, forming offlapping fans at the toe of the slope, likely truncating part of the siliciclastic topsets. Subsequently, mixed facies were deposited during the early stages of rising relative sea level, followed by carbonate deposition on the slope and shelf throughout transgression. The mixed facies found inland of transgressive carbonates indicate that prodelta siliciclastics mixed with carbonate mud. Occasionally, transgressive carbonates create shelf-edge build ups, hindering the transport of highstand siliciclastics to the slope. In the composite sequence spanning ∼1200kyr, siliciclastic input decreases significantly with the onset of sea-level fall. The fall truncates topsets with limited siliciclastics, preserving carbonate and mixed facies-dominated foresets with offlapping fans at the toe of the slope. This study advances our understanding of mixed sequences, challenges existing models and lays the groundwork for future investigations in similar geological settings.

Seismic Inversion Techniques and Attribute Analysis for Accurate Well Placement in Offshore Niger Delta Basin, Nigeria

Before this study, simple methods like seismic attribute analysis have often been used for reservoir characterization with successes however, there is still the need to reduce exploration uncertainty to a negligible level and boost investors’ confidence. This study integrated seismic inversion with seismic attribute analysis to better characterize the reservoirs in MTW Field in deep-offshore Niger Delta Basin. Five (5) wells with complete suite of petrophysical logs, three-Dimensional (3-D) seismic data, checkshot and other well information were used. The well data were thoroughly quality checked, reservoirs were litho-stratigraphically delineated and used for petrophysical analysis across the wells. This was followed by seismic-to- well-tie, seismic interpretation, and seismic attribute analysis (Root Mean Square-RMS) generated using depth surface maps. 3-D static reservoir model and volumetric evaluation were carried out. Petrophysical properties were derived and distributed across the 3-D static model using sequential gaussian simulation algorithm to ascertain shale volume spread across the model. To improve the seismic resolution and reduce interpretation uncertainty at greater depth, model- based post-stack seismic inversion was performed to obtain acoustic impedance cube. Litho-stratigraphic and petrophysical analysis result revealed five reservoir sands (A, B, C, D, and E). Reservoir-A was seen to be more viable with a thickness of 13.42 m, high effective porosity of 27%, permeability of 3187.53 mD, low water saturation of 34% and low shale volume of 11% which are indication good reservoir quality and producibility. The seismic interpretation revealed thirty-one (31) growth and antithetic faults oriented in the NE-SW and NW-SE directions, respectively. The RMS result revealed high amplitude reflectivity which is a measure of zone of interest. Based on this, seven (7) prospects and three (3) leads were identified. The seismic inversion result shows a high level of accuracy with a correlation coefficient of 0.997; 0.997; 0.995; and 0.996 in MTW-001, MTW-003ST1, MTW-004ST1 and MTW-005 wells, respectively. The acoustic impedance successfully resolved and improved on the resolution of the seismic stacking velocity especially at reservoir layers and at depth deeper than 3600 ms. Acoustic impedance as a layer property has improved on the lateral and vertical resolutions of the data beyond what the usual seismic interval velocity could image thus, validating some of the prospects and leads identified in this study. This demonstrated that uncertainty can be reduced by a blend of RMS and seismic inversion in identifying reservoir for accurate placement of wells. It is therefore recommended that E and P operators should adopt the technique in their future hydrocarbon exploration endeavor in frontier and matured basins.

Classification and Prediction Method for Dolomite Reservoirs in the Permian Maokou Formation in the Sichuan Basin

The Lower Permian Maokou Formation in the Hechuan area is one of the key horizons for natural gas exploration in the Sichuan Basin, showing a low degree of exploration and great exploration potential. The Lower Permian gas reservoir is mainly controlled by the reservoir, therefore it is subject to the development of the Maokou Formation reservoir. However, due to the strong heterogeneity and significant variations in physical properties of the Maokou Formation dolomite reservoir in the Hechuan area, there are significant differences in seismic response characteristics of reservoirs with different porosities. Through comprehensive analysis of gas testing wells in the Maokou Formation, criteria for reservoir classification were established based on lithology, average porosity, plane porosity, and thickness of dolomite reservoirs. The effective reservoirs in the Maokou Formation were divided into two types: high-porosity reservoirs and medium-porosity reservoirs. On the basis of establishing a physical quantity model for the Lower Permian carbonate rock in the research area, the rock physical characteristics and seismic response characteristics of reservoirs with different pores are determined through model forward modeling, and appropriate methods are selected for prediction. By using the optimization processing method of gathers and wavelet decomposition to remove sidelobes, the issue of seismic abnormal response caused by lateral velocity changes in the underlying Maokou Formation has been solved. The relative strength relationship of seismic response amplitude can effectively characterize the true situation of reservoir development. Then, by using post-stack inversion and pre-stack simultaneous inversion methods, the high-porosity and medium-porosity dolomite reservoirs in the Maokou Formation are classified and predicted, thereby to effectively improve the prediction accuracy of reservoirs, ultimately providing a reliable basis for the exploration and development of the Maokou Formation in the Hechuan area in the later stage.

Post-stack seismic inversion through probabilistic neural networks and deep forward neural networks

Seismic inversion, a nonlinear, non-unique-solution, and human-prone error problem, seeks the optimal solution in multivariate space, a task that AI algorithms perform better. So, inversion with AI becomes appropriate to extract information from post-stack data. A probabilistic neural network (PNN) and deep feedforward neural network (DFNN) inverted seismic data using multiattributes, and their results were compared with those provided by a model-based inversion (MBI). Applied to a post-stack volume of the Frontier Formation at the Teapot-Dome field, Wyoming-USA, the three methods provided porosity volumes (ϕ), gamma rays (GR), and Lamé impedances (µρ, λρ). The comparison between well-logs and the provided volumes revealed a reliable PNN prediction, while DFNN achieved the best geologically correlated parameter sections and a less reliable MBI prediction. The geomorphological time-slice analysis discriminated high ϕ-µρ areas and low GR–λρ areas, geologically interpreted as a deltaic environment, with distributary channels and beachfront lobes subject to the action of the tides, which agrees with the well-log interpretation and the basin geological evolution. The results enhanced the stacked section’s importance by the geological information contained, which would contribute to reducing the prospecting risk. Finally, PNN and DFNN encouraging results reaffirm their best searching-in-multivariate space ability.

Improved prediction of thin reservoirs in complex structural regions using post-stack seismic waveform inversion: a case study in the Junggar Basin

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.

Posterior sampling with convolutional neural network-based plug-and-play regularization with applications to poststack seismic inversion

Uncertainty quantification is a crucial component in any geophysical inverse problem, as it provides decision makers with valuable information about the inversion results. Seismic inversion is a notoriously ill-posed inverse problem, due to the band-limited and noisy nature of seismic data; as such, quantifying the uncertainties associated with the ill-posed nature of this inversion process is essential for qualifying the subsequent interpretation and decision-making processes. Selecting appropriate prior information is a crucial — yet nontrivial — step in probabilistic inversion because it influences the ability of sampling-based inference algorithms to provide geologically plausible posterior samples. However, the necessity to encapsulate prior knowledge into a probability distribution can greatly limit our ability to define expressive priors. To address this limitation and following in the footsteps of the plug-and-play (PnP) methodology for deterministic inversion, we develop a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss — a measure of the distance between the approximated and target probabilistic distributions — with a convolutional neural network-based denoiser. We call this new algorithm PnP Stein variational gradient descent and determine its ability to produce high-resolution trustworthy samples that realistically represent subsurface structures. Our method is validated on synthetic and field poststack seismic data.

Generating 3D lithology probability volumes using poststack inversion, probabilistic neural networks, and Bayesian classification — A case study from the mixed carbonate and siliciclastic deposits of the Cisco Group of the Eastern Shelf of the Permian Basin, north-central Texas

The deposition and mixing of carbonates and siliciclastics in the Cisco Group of the Eastern Shelf of the Permian Basin are complicated by the temporal overlap between icehouse eustatic sea-level oscillations and fluctuations in sediment influx due to the rejuvenation of the Ouachita fold belt. Previous investigators have used well-log correlation as the primary tool in their interpretations of the area’s reciprocal depositional model, but well-log correlation alone cannot explain the full range of spatial lithology variations in the system. To better understand the lithology variation in the area, we use an integrated technique that combines wireline log information from 17 wells with 625 km2 3D seismic data through poststack seismic inversion, probabilistic neural networks (PNNs), and Bayesian classification. We use deterministic matrix inversion to derive lithology classes from well logs. Crossplot analyses reveal that the acoustic impedance and neutron porosity log pair can be used to differentiate lithologies. We perform model-based poststack inversion to generate a P-impedance volume and use PNNs to generate a neutron porosity volume. We combine these volumes through supervised Bayesian classification to generate lithology probability volumes for each lithology and a most probable lithology volume throughout the seismic data. The lithology volumes highlight the dominant lithologies (carbonate, shale, sand, and mixed) that allowed the interpretation of major carbonate platforms, sand-to-shale ratio variations, carbonate buildups between wells, and channel fill lithologies. Our semiautomated lithology detection workflow applies to regional studies and is also valid for reservoir-scale studies to determine variations in lithologies.

Accurate identification of traps and pinch-outs on a stratigraphic reservoir-A case from Hala'alate Mountain in the Junggar Basin, China.

In the investigation of stratigraphic reservoirs, a significant discrepancy frequently exists between the delineation of the formation pinch-out line as traced using the characteristics of seismic wave reflections and the actual location of the formation pinch-out line. This has been the main problem restricting further hydrocarbon exploration and development. In this study, Hala'alate Mountain on the northwestern margin of the Junggar Basin is taken as an example for carrying out the study of stratigraphic reservoirs by integrating logging, drilling, and 3D seismic data. On the one hand, in studies based on the identification of formation pinch-out points using seismic data, the identification error of reservoir pinch-out lines is reduced by the improved included angle extrapolation method by utilizing the half energy attribute. On the other hand, the Poisson's ratio curve is reconstructed using acoustic curves and oil-gas sensitive logging, then the reservoir oil-bearing facies zone is predicted using Poisson's ratio post-stack genetic inversion to comprehensively analyze the controlling factors of stratigraphic reservoirs. The study area mainly features structural lithologic reservoirs, structural stratigraphic reservoirs and stratigraphic overlaps that pinch out reservoirs. The boundary of a stratigraphic reservoir is affected by the dip angle of the unconformity surface, the formation dip angle, and other factors. The improved included angle extrapolation method improves the identification accuracy of stratigraphic overlap pinch-out reservoirs. The reservoir distribution then is calculated according to Poisson's ratio inversion, improving the prediction accuracy for the reservoir. This method improves the predictive effect for stratigraphic reservoirs and provides a new idea for the exploration and development of similar reservoirs.

Prediction of acoustic impedance and density porosity using seismic inversion, and geostatistical methods on Krishna Godavari basin, India: A case study

Seismic inversion is a geophysical technique used to estimate subsurface acoustic impedance models from seismic reflection data. The basic concept behind seismic inversion is to establish mathematical assumptions that can relate seismic responses to geological formation properties. This study addresses two types of seismic inversion methods, namely Band-limited inversion (BLI) and Linear programming inversion (LPI) techniques for the delineation of gas reservoirs. The study presents the successful use of the inversion in a real example from a gas sand reservoir in KG-Basin, India. Inversion has led to the removal of ambiguity and revealing clear information about the target area. This work aims to use and compare the results of these two generic seismic post-stack inversion methods for characterizing the reservoir. The inversion reveals the presence of low acoustic impedance around 2320-2440 ms and is inferred as a sandstone layer with a potential hydrocarbon reservoir. One can improve the analysis of the elastic properties for reservoir characterization by using rock physics knowledge to understand better reservoir properties such as water saturation, porosity, and shale volume. This study also uses the Rock Physics Template (RPT) and plots it with elastic properties to aid the efficient interpretation. RPT helps to identify the lithology and fluid content in the area. Results from the seismic study suggest a comparatively better analysis done through the LPI technique over BLI. In the last, the Multi-attribute transform as a part of the Geostatistical method is used to predict porosity away from the borehole. The method shows a very high porosity section and clearly shows the distribution of porosity in and around the region. The interpretation shows that a very high porosity zone (15–25%) is present that is in cross-correlation with a low impedance zone (4000-8000 m/s*g/cc) and confirms the presence of a hydrocarbon zone in-between 2320 and 2440 ms two-way travel time. All seismic inversion and geostatistical methods can retrieve subsurface information with very high-resolution images but the analysis based on linear programming is found slightly better in each step and depicts good prediction results. The study finally gives a flowchart that combines seismic inversion and geostatistical methods to extract petrophysical parameters from seismic data and can help to interpret hydrocarbon-bearing formation in any virgin area.

A preliminary analysis of natural CO2-saturated gas sands distribution in the Gemah Field-Jabung Block, South Sumatra Basin, Indonesia, by using simple seismic attributes

The discovery of CO2-saturated gas in sandstone reservoirs of the Lower Talang Akar Formation (LTAF) in the Jabung Block of South Sumatra Basin is a significant exploration risk that affects development plans. Gemah Field is one of the fields that produces CO2-saturated gas in the area. The field has several oil rims that can be optimized for production by using carbon capture, utilization, and storage (CCUS) enhanced oil recovery (EOR). This paper discusses a preliminary analysis of the natural CO2-saturated gas sands distribution in the FS3 interval of Gemah Field by using simple seismic attributes. The results can be used for guiding the feasibility analysis of CCUS-EOR implementation and mitigation of drilling risk of subsequent developments wells. Poststack seismic attributes and inversion analysis were performed and constrained by data from 64 wells in the FS3 interval. Although these two methods give similar distribution of sandstone reservoirs, this study finds that the sum of negative amplitude (SNA) attribute is better because there is a linear relationship between SNA with sand thickness and CO2 saturation. High CO2 saturation gas reservoir zones are observed along the highly faulted areas. CO2 saturation decreases away from the faulted areas and follows the northeast–southwest direction of reservoir deposition. The identified high CO2 regions can be considered as zones of potential CO2 sources for CCUS-EOR. The low CO2 areas are a potential location for a subsequent drilling well to obtain cleaner gas reservoirs with low CO2 saturation.

Prediction of Petrophysical Properties using Post Stack Seismic Inversion and Geostatistical Techniques over F-3 Block, Netherlands - A Comparative Study

To estimate petrophysical parameters, seismic inversion techniques have been frequently used for estimating attributes like P-impedance, elastic impedance, S-impedance, density, Vp / Vs ratio, and gamma-ray logs from seismic and well-log data. These characteristics enable us to comprehend subsurface lithology for geo-seismic analysis, including its extent and shape. Four different post-stack inversion techniques, including bandlimited inversion (BLI), colored inversion (CI), maximum likelihood sparse spike inversion (MLSSI), and model-based inversion (MBI), have been applied to the post-stack seismic data from the F3 block in the Netherlands in this study. The objective is to compare the efficacy of these inversion methods over F3 block seismic data. For post-stack inversion, the data was inverted into a very high-resolution P-impedance volume. The analysis depicts that all four inversion methods provide mutually consistent subsurface information with marginally better MBI results. Furthermore, geostatistical techniques have been used intensively for further testifying the results obtained from post-stack inversion methods. The geostatistical techniques use seismic data-derived attributes and inverted impedance-derived attributes as input to estimate P-wave velocity, porosity, and density away from the boreholes. Two geostatistical methods namely probabilistic neural network (PNN) and multilayer feed-forward neural network (MLFN) are used for the analysis. Porosity, density, and P-wave velocity have been predicted using both techniques which highlighted different characteristics of the subsurface with very detailed information. The derived results show that reservoir properties have been better estimated with the combination of MBI and PNN techniques for F3 block data over the other methods used in this study.

Estimation of Reservoir Porosity Using Seismic Post-Stack Inversion in Lower Indus Basin, Pakistan

Seismic post-stack inversion is one of the best techniques for effective reservoir characterization. This study intends to articulate the application of Model-Based Inversion (MBI) and Probabilistic Neural Networks (PNN) for the identification of reservoir properties i.e. porosity estimation. MBI technique is applied to observe the low impedance zone at the porous reservoir formation. PNN is a geostatistical technique that transforms the impedance volume into porosity volume. Inverted porosity is estimated to observe the spatial distribution of porosity in the Lower Goru sand reservoir beyond the well data control. The result of inverted porosity is compared with that of well-computed porosity. The estimated inverted porosity ranges from 13-13.5% which shows a correlation of 99.63% with the computed porosity of the Rehmat-02 well. The observed low impedance and high porosity cube at the targeted horizon suggest that it could be a probable potential sand channel. Furthermore, the results of seismic post-stack inversion and geostatistical analysis indicate a very good agreement with each other. Hence, the seismic post-stack inversion technique can effectively be applied to estimate the reservoir properties for further prospective zones identification, volumetric estimation and future exploration.

Delineating tidal channel feature using integrated post-stack seismic inversion and spectral decomposition applications of the Upper Cretaceous reservoir Abu Roash C: A case study from Abu-Sennan oil field, Western Desert, Egypt

Delineating tidal channels that bear hydrocarbons in fluvial systems is crucial to minimize drilling risks, and raise the hydrocarbon potential. Therefore, we performed an integrated workflow that combines/compares the outcomes of a pair of post-stack inversion techniques (model-based inversion and colored inversion) and the spectral decomposition attribute to delineate (vertically and horizontally) the hydrocarbon-bearing tidal channel contained in the Upper Cretaceous Abu Roash (AR)/C reservoir in the South West Abu-Sennan (SWS) area in Abu-Gharadig basin in the Northern Egyptian Western Desert (NEWD). We used a dataset consisting of twenty seismic reflection profiles and borehole geophysical data of four wells. Data conditioning was performed in detail for the whole dataset that was used in this study. The application of the absolute impedance (model-based inversion) produced better results relative to the band-limited acoustic impendence (colored inversion). The model-based inverted sections gave an improved subsurface image of seismic data and delineated the reservoir properly. The low-frequency range detected through the use of seismic inversion techniques and the spectral decomposition attribute analysis showed a good fit with the low impedance values corresponding to hydrocarbons. The study results showed a correlation of roughly 0.99 exists between the generated synthetic impedance, original seismic, and the borehole geophysical data characterizing the tidal channel confined within the AR/C reservoir. The match between the post-stack inversion techniques and the spectral decomposition analysis results is considered a visual validation of the delineated tidal channel. The performed workflow can be applied and impact the understanding of comparable reservoirs with similar settings in near areas.

Delineation of thin and discrete sand reservoir facies from shale-dominated Kopili Formation (Middle to Late Eocene) using the post-stack seismic inversion and neural network algorithm: A case study from Assam Basin, India

Delineation of thin and discrete sand reservoir facies from shale-dominated Kopili Formation (Middle to Late Eocene) using the post-stack seismic inversion and neural network algorithm: A case study from Assam Basin, India

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.

Delineation of thin-bedded sands and porosity using post-stack seismic inversion in the Lower Goru Formation, Kadanwari gas field, Pakistan

Delineation of thin-bedded sands and porosity using post-stack seismic inversion in the Lower Goru Formation, Kadanwari gas field, Pakistan

Simultaneous model-based inversion of pre-stack 3D seismic data targeting a deep geothermal reservoir, Northwest Hungary

The well-known, traditional way to extend P-wave acoustic impedance data between and beyond the well log locations is the post-stack inversion of seismic data usually available in the surroundings of the boreholes. A relatively new trend in the seismic exploration is based on the pre-stack inversion of the seismic CDP gathers providing both the P- and S-wave acoustic impedance sections (and volumes), as well as the estimated density data. This methodology is often called as simultaneous model-based inversion and can be utilized not only for hydrocarbon exploration, but it might also be a useful tool for the investigation of geothermal resources. In this study, we will compare the results of post-stack and pre-stack acoustic impedance inversions utilizing the same seismic volume. We will demonstrate and analyze the inverted attribute sections (and some of their derivatives) obtained by the pre-stack algorithm in detail. Finally, we will draw the conclusions about the lithological discrimination of the studied complex carbonate geothermal reservoir located in the pre-Cenozoic basement of the Little Hungarian Plain.

Factors controlling spatial distribution of complex lithology in transitional shale strata: Implications from logging and 3D seismic data, Shan-2 Lower Sub-member, Eastern Ordos Basin

The Shanxi Formation Shan-2 Lower Sub-member transitional shale in the eastern Ordos Basin is characterized by high total organic carbon value, wide distribution, and large single-layer/cumulative thickness; it is the key target interval for transitional shale gas exploration. Based on drilling, logging, 3D seismic, and natural energy spectrum data, this study discusses the spatial distribution and controlling factors of complex lithology in the transitional shale stratum. Using the multi-lithological eigenvalue method, the lithologic differential distribution characteristics of the Shan-2 Lower Sub-member were identified through seismic attribute analysis and post-stack seismic parameter inversion. On this basis, the controlling factors of lithology spatial difference distribution are revealed by paleogeomorphology restoration, the sedimentary environmental indicator characteristics of shale, and seismic facies division. The results indicate that the lithology distribution of transitional shale strata in the Shan-2 Lower Sub-member is significantly different, and the geomorphological pattern of interjacent depressions and highlands lays a foundation for the formation of the sedimentary system. The environmental and energy differences of paleo-sedimentary water form a sedimentary environment with frequent facies transitions, which controls the distribution of sediments. Th/U and Th/K radios, which indicate redox conditions and sedimentary water energy during the early sedimentary stage of the Shan-2 Lower Sub-member, show that the geomorphic uplift area is characterized by a high-energy transitional environment of mainly developed sandstone and silty mudstone, while the depression is relatively low-energy under a brackish water reduction environment, mainly composed of both dark and carbonaceous shale. During the late sedimentary stage of the Shan-2 Lower Sub-member, when geomorphic control was weakened, it presented a shallow-water, high-energy, and oxidized transitional environment, with a lithology dominated by the complex interbedding of fine-grained sandstone, silty shale, shale, carbonaceous shale, and coal seam. Therefore, it is inferred that the Shan-2 Lower Sub-member may have developed as a barrier island-lagoon sedimentary model. The early depositional period was dominated by barrier island-lagoon-tidal flat depositional combination, thence the sedimentary environment gradually evolved into a tidal flat-swamp environment.

Three-parameter prestack nonlinear inversion constrained by gradient structure similarity

Estimating the elastic parameters from prestack inversion is of great importance for reservoir characterization. Conventional three-parameter prestack inversion methods rely heavily on well logs, and it is difficult to obtain reliable inversion results in situations with limited numbers of wells. Alternatively, we have developed a joint inversion strategy, integrating the advantages of post- and prestack inversion, to deal with the situation. First, due to the high signal-to-noise ratio of poststack seismic data, the high-precision acoustic impedance (AI) inversion is conducted. Second, the exact Zoeppritz equation is used to establish the objective function of the prestack inversion. To better constrain the elastic parameters (P- and S-wave velocities and density), a new similarity measurement criterion, the gradient structure similarity (GSS), is defined to describe the structural similarity between the prestack inversion results and the inverted AI from the poststack inversion. Third, the LM optimization algorithm is used to solve the nonlinear objective function. Through the model test, we verify the effectiveness of our GSS regularization scheme. Some synthetic and field examples find that our method can provide more stable and accurate inverted results relative to the conventional prestack inversion methods.