Pre-stack Simultaneous Inversion Research Articles

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.

Rock characterization of rupelian-chatian Asmari carbonate formation using the simultaneous pre-stack inversion technique and lame parameters in the Kopal oilfield, Dezful embayment, Zagros basin

Rock characterization of rupelian-chatian Asmari carbonate formation using the simultaneous pre-stack inversion technique and lame parameters in the Kopal oilfield, Dezful embayment, Zagros basin

Using seismic petrophysical modeling and prestack simultaneous inversion to provide insights into the physical properties of uranium-bearing reservoirs: Implications for favorable sites of sandstone-hosted uranium deposits

Seismic prospecting has been accepted as one of the most widely available methods for exploring sandstone-hosted uranium deposits (SUDs). However, conventional seismic interpretation faces a challenge in the identification and characterization of a uranium reservoir’s complexity. How to characterize in detail a uranium reservoir’s physical complexity and effectively improve uranium reservoir prediction accuracy remain unresolved problems. To address this, we develop a novel combination of petrophysical modeling and prestack simultaneous inversion to understand in detail the physical properties of uranium-bearing reservoirs and efficiently predict favorable SUD sites. First, we develop a workflow of rock-physics modeling for SUD logs using the Xu-White method to calculate the modulus of elasticity of the grain matrix; subsequently, we extend the Walton model for the modulus prediction of the dry rocks and the Gassmann equation for one of the saturated rocks after a massive calculation test; and then, we predict the S-wave data used for the following inversion. Second, we execute a prestack simultaneous inversion to obtain the petrophysical parameters (e.g., P-impedance, density [[Formula: see text]], shear modulus [[Formula: see text]], Lamé coefficient [[Formula: see text]], and Young’s modulus) that can provide insights into the physical properties of a uranium metallogenic environment. Accordingly, we discover that sites bearing uranium mineralization strongly correspond to areas with low elastic-parameter values (especially [Formula: see text] and [Formula: see text]), whereas nonuranium anomalies occur in high-value sites. This indicates that weakened elastic characteristics are caused by the enhancement of the total organic content and total clay mineral volumes of the uranium-bearing layers. In summary, the developed combination approach can yield an effective and accurate characterization of the geologic properties of uranium-bearing formations, and it can provide prediction factors (e.g., parameters related to the shear modulus) for uranium mineralization.

Unsupervised machine learning-based multi-attributes fusion dim spot subtle sandstone reservoirs identification utilizing isolation forest

Subtle sandstone reservoirs are difficult to identify due to their weak seismic responses. Here, we propose to identify subtle sandstone reservoirs by an unsupervised machine learning-based multi-attribute fusion scheme using prestack seismic data. The proposed scheme carries out seismic attenuation gradient analysis and prestack simultaneous inversion to obtain the attributes that are sensitive to subtle channel sands, and uses them as the selected multiple attributes, and further employs a state-of-the-art unsupervised machine learning algorithm, called isolation forest, for the multi-attribute anomaly detection and analysis to identify subtle sandstone reservoir. To the best of our knowledge, this is the first time to introduce the isolation forest unsupervised anomaly detection algorithm in the reservoir identification. Prestack simultaneous inversion can use multi-angle and multi-scale information as constraints, and the attenuation gradient reflects the body response of the reservoir. For the field seismic data from a subtle channel sandstone reservoir in the western Sichuan basin, China, we found that the proposed scheme has good application effect in identifying subtle reservoirs. The application example demonstrates that the identified results are highly consistent with the actual development results, illustrating the feasibility and effectiveness of this scheme on the characterization for dim spot subtle sandstone reservoirs. This study is hoped to be useful as an aid for reservoir identification for dim spot subtle sandstone reservoirs, as well as to provide a new technical idea and method for reservoir characterization.

Geophysical prediction of organic matter abundance in source rocks based on geochemical analysis: A case study of southwestern Bozhong Sag, Bohai Sea, China

The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin, and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply. Most of the drillings were located at the structural high, and there were few wells that met good quality source rocks, so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only. Based on the Rock-Eval pyrolysis, total organic carbon (TOC) testing, the organic matter (OM) abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated, including the lower of second member of Dongying Formation (E3d2L), the third member of Dongying Formation (E3d3), the first and second members of Shahejie Formation (E2s1+2), the third member of Shahejie Formation (E2s3). The results indicate that the E2s1+2 and E2s3 have better hydrocarbon generative potentials with the highest OM abundance, the E3d3 are of the second good quality, and the E3d2L have poor to fair hydrocarbon generative potential. Furthermore, the well logs were applied to predict TOC and residual hydrocarbon generation potential (S2) based on the sedimentary facies classification, using ΔlogR, generalized ΔlogR, logging multiple linear regression and BP neural network methods. The various methods were compared, and the BP neural network method have relatively better prediction accuracy. Based on the pre-stack simultaneous inversion (P-wave impedance, P-wave velocity and density inversion results) and the post-stack seismic attributes, the three-dimensional (3D) seismic prediction of TOC and S2 was carried out. The results show that the seismic near well prediction results of TOC and S2 based on seismic multi-attributes analysis correspond well with the results of well logging methods, and the plane prediction results are identical with the sedimentary facies map in the study area. The TOC and S2 values of E2s1+2 and E2s3 are higher than those in E3d3 and E3d2L, basically consistent with the geochemical analysis results. This method makes up the deficiency of geochemical methods, establishing the connection between geophysical information and geochemical data, and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.

Prestack simultaneous inversion of P-wave impedance and gas saturation using multi-task residual networks

Prestack simultaneous inversion of P-wave impedance and gas saturation using multi-task residual networks

Simultaneous seismic inversion study for channel sandstone identification, northern part of the Eastern Shelf, King County, North-Central Texas

This paper documents the results of a prestack simultaneous seismic inversion conducted to investigate the Wolfcampian lower Tannehill sandstone channels within a mixed carbonate siliciclastic system in the northern Eastern Shelf in King County, North Central Texas. Previous exploration methods for the Tannehill sandstone were based largely on well-log correlation which, because of their widely spaced sample points, are not optimal for channel delineation. We have generated crossplots of P- and S-wave velocity ratios ([Formula: see text]/[Formula: see text] ratios) versus P-impedance to determine lithologic types. We then used 3D seismic data and wireline logs to implement a prestack simultaneous seismic inversion procedure to generate P- and S-impedances, density, and [Formula: see text]/[Formula: see text] ratio volumes to identify the valley-fill Tannehill lowstand systems. Our results find that (1) based on impedance and density, the sandstone-rich channels can be easily separated from the carbonate-rich zones but are difficult to distinguish using [Formula: see text]/[Formula: see text]. (2) Although there appeared to be several channel systems in the area of investigation, two of these, hereby referred to as channel A and channel B, are recognized to be the key contributors of sandstone sediments to the shelf edge, slope, and basin areas. (3) Of these two channels, channel A is the larger and highly sinuous; it is associated with a meander loop, a point bar, and an abandoned channel. At the point-bar location, channel A has a width of 890 m (2920 ft) and a depth of 29 m (94 ft). The point bar is structurally anticlinal and thus constitutes a potential exploration target. In contrast, channel B is less sinuous, without any associated point bar. It has a maximum depth and width of 23 m (75 ft) and 270 m (886 ft), respectively. Finally, the inversion results suggest that other potential Tannehill sandstone reservoirs may still be present elsewhere within King County.

Application of a model-driven simultaneous prestack inversion of rock physical properties in ultra-deep Ordovician carbonate reservoirs in the Shunbei area

Synchronous prestack inversion technology has been widely used to predict the fluid and lithological properties of reservoirs. Its prediction accuracy depends on the seismic data quality. Ultra-deep carbonate reservoirs in the Shunbei area of the Tarim Basin were used as research objects. Based on the shear wave (S-wave) prediction method, petrophysical logging analysis was carried out to select the physical property-sensitive elastic parameters. A physical property indicator with porosity as the target curve was constructed to quantitatively characterize the differences in physical properties in this area. The signal-to-noise ratio (SNR) and resolution of the dataset were improved by preprocessing the seismic and logging data. P- and S-wave impedances and densities obtained from prestack synchronous inversion, which integrate the physical property indicator model. Spatial distribution and transverse variation of the physical parameters of the Ordovician carbonate reservoirs are described. The planar distribution of reservoirs can be effectively predicted.

Facies-based Bayesian simultaneous inversion technology and its application: A case study of the north section of No.5 fault zone in Shunbei area, Tarim Basin, China

The fault-karst carbonate reservoir in the Shunbei area of the Tarim Basin is controlled by deep strike-slip faults and forms a fault-karst system. The reservoir space primarily includes holes and fractures, and its strong anisotropism aggravates the complexity of the reservoir seismic response characteristics. High-quality reservoirs in the fault-karst body in this area have different burial depths, which is not conducive to the establishment of low-frequency models in traditional inversion. Facies-based Bayesian simultaneous inversion technology combines Bayesian classification with pre-stack simultaneous inversion, divides different facies based on multi-elastic parameters such as P-wave and S-wave velocity and density, and conducts an in-depth trend analysis for each phase to establish the initial model. Compared with traditional inversion technology, this technology not only improves the inversion accuracy but also increases the stability of the density inversion. Taking the carbonate fault-karst body in the northern section of the No.5 fault zone in the Shunbei area as the research object, combined with the actual production situation, two facies, fractured-cavity limestone, and tight limestone, were divided by elastic parameters and then subjected to depth trend analysis and inversion. Through the single fracture-cavity equivalent model test and practical application analysis, the density data obtained by the Facies based Bayesian simultaneous inversion were highly consistent with the reservoirs drilled by Wells W3 and W3C in the northern section of the No.5 fault zone, which verifies the applicability and reliability of the inversion technique in the study area and the reliability of the results.

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Compared with shale gas in north American, it is older, more mature, and significantly difference in China. Taking the Longmaxi Formation of WY block in Sichuan Basin as an example, the quantitative prediction method of sweet spots for high mature shale gas is formed. Firstly, the geophysical response characteristics of shale gas reservoir is studied, and the physical characteristics of the sweet spots are determined. Then, the distributions of reservoir evaluation parameters, such as total organic carbon, brittleness, and others, are obtained by pre-stack simultaneous inversion. Finally, fuzzy neural network method is carried out to predict the distribution of sweet spots and to deploy wells. Shale gas sweet spots in this block vertically concentrate within 30 m above the bottom of the Longmaxi Formation. And two grades of sweet spots are evaluated. The quantitative relationship between the production results of horizontal wells and the evaluation is established with Fuzzy neural network method.

An integrated approach to lithofacies characterization of a sandstone reservoir using the Single Normal Simulation equation: A Case study

This paper describes a comprehensive approach to improved lithofacies characterization using the simultaneous pre-stack inversion, Bayesian classification, and multiple-point geostatistics. This integrated workflow provides an enhanced lithofacies characterization by reproducing the continuity of geological facies in the meandering-fluvial sandstone reservoir from Upper Assam, India. The simultaneous pre-stack inversion provides the rock elastic properties of the reservoir, such as P- impedance, s-impedance, density, and VP/VS ratio. Next, the probabilistic modeling approach of Baye's theorem is used to integrates seismic elastic properties with well log lithofacies identified from the cross-plot analysis for probability volumes of lithofacies. Finally, the multiple-point geostatistics based on the Single Normal equation Simulation algorithm is used to generate the improved lithofacies model by constraining of the lithofacies probability volumes (soft data) and well logging data (hard data). These results were compared with the Bayesian lithofacies classification and proved that the adopted method had successfully improved the continuation of the reservoir lithofacies by reproducing the lithofacies, hence an improvement in the reservoir characterization.

Estimating Reservoir Properties from 3D Seismic Attributes Using Simultaneous Prestack Inversion: A Case Study of Lufeng Oil Field, South China Sea

Summary Lufeng oil field was discovered in the 1980s in the Pearl River Mouth Basin (PRMB) of the South China Sea and has entered the stage of secondary oil recovery. One major problem that has restricted the subsequent exploration of the oil field is the unclear regional reservoir and caprock distribution, because practitioners have been using post-stack attributes and acoustic impedance inversion to analyze the distribution of sandstone (reservoir) and mudstone (caprock). In current geophysical research on reservoirs and caprocks, prestack inversion has been widely used because of its advantage over post-stack inversion. However, the accuracy of using single P/S-wave velocity ratio (VP/VS) or density (Vden) inverted by prestack to predict lithology is still insufficient. In this study, we created a new attribute VRDEN, the sum of VP/VS and the weighted Vden, to capture the lithology variation of the reservoir. We integrated 3D seismic data and well log data and applied simultaneous prestack inversion and multiattribute regression analysis to determine reservoir properties, such as sand thickness, effective porosity, and distribution of sandstone and mudstone of the Lufeng oil field. Then, we calculated the new attribute VRDEN from VP/VS and Vden obtained from simultaneous prestack inversion to determine the lithology variation. The multiattribute regression analysis, combining prestack attributes and post-stack attributes, indicates the effective porosity and sand volume in the Enping Formation, which contains the main oil-bearing reservoirs in the Lufeng oil field. Results show that when the sandstone thickness is greater than 12.5 m, the prediction error of VRDEN is the lowest compared with VP/VS and Vden. In En-2 member of the Lower Enping Formation, medium- to high-porosity (14 to 17%) sandstone (19.5 m thickness) is widely distributed in the west and middle of the study area, with an area of nearly 300 km2. The high-porosity sand zones stretch from the east of the Lower Enping Formation to the west of the Upper Enping Formation, which is the result of westward progradation of the braided delta. Our workflow used a novel seismic attribute VRDEN in the simultaneous prestack inversion and multiattribute regression process to provide a more predictive spatial distribution of reservoir-nonreservoir features. The improved reservoir understanding will allow more efficient exploitation of the Lufeng oil field, and the improved workflow will facilitate exploration of other oil fields in the world.

Prestack Inversion Identification of Dolomite Reservoirs in the Fourth Member of the Sinian Dengying Formation in Moxi Area, Sichuan Basin, SW China

The dolomite reservoir of the fourth member of Dengying Formation in Moxi area of Sichuan Basin is thin, is fast in lateral variation, and has P-impedance difference from the surrounding rock; it is difficult to identify and predict the dolomite reservoir and fluid properties by conventional poststack seismic inversion. Through the correlation analysis of core test data and logging P-S-wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges and solved the issue that some wells in the study area have no S-wave logging data. AVO forward analysis reveals that whether the gas reservoir of dolomite reservoir is located at the top of the fourth member of Dengying Formation is the main factor affecting the variation of AVO type. Through cross-plotting analysis of elastic parameters, it is found that P-S-wave velocity ratio and fluid factor are sensitive parameters to gas-bearing property of dolomite reservoir in the study area. By comparing the inversion results of prestack parameters such as density, P-wave impedance, S-wave impedance, P-S-wave velocity ratio, and fluid factor, it is found that the gas-bearing prediction of dolomite reservoir by using P-S-wave velocity ratio and fluid factor obtained from simultaneous prestack inversion had the highest coincidence rate with actual drilling data. At last, according to the distribution characteristics of fluid factor and P-S-wave velocity ratio, the favorable gas-bearing areas of dolomite reservoir in the fourth member of Dengying Formation in the study area are finely predicted, and the next favorable exploration areas were pointed out.

Reservoir characterization over the Lille Prinsen and Ivar Aasen fields in the Norwegian North Sea using ocean-bottom-node seismic data — A case study

We have developed an integrated workflow for estimating elastic parameters within the Late Triassic Skagerrak Formation, the Middle Jurassic Sleipner and Hugin Formations, the Paleocene Heimdal Formation, and the Eocene Grid Formation in the Utsira High area of the Norwegian North Sea. Our workflow begins with petrophysical analysis carried out at the available wells. Then, model-based prestack simultaneous impedance inversion outputs were derived, and attempts were made to estimate the petrophysical parameters (the volume of shale, porosity, and water saturation) from seismic data using extended elastic impedance. On not obtaining convincing results, we switched over to multiattribute regression analysis for estimating them, which yielded encouraging results. Finally, the Bayesian classification approach was used for defining different facies in the intervals of interest.

Seismic Lithofacies Distribution Modeling Using the Single Normal Equation Simulation (SNESIM) Algorithm of Multiple-Point Geostatistics in Upper Assam Basin, India

Recently, multiple-point geostatistical simulation gained much attention for its role in spatial reservoir characterization/modeling in geosciences. Accurate lithofacies modeling is a critical step in the characterization of complex geological reservoirs. In this study, multiple-point facies geostatistics based on the SNESIM algorithm integrated with the seismic modeling technique is used as an efficient reservoir modeling approach for lithofacies modeling of the fluvial Tipam formation in the Upper Assam Basin, India. The Tipam formation acts as a potential reservoir rock in the Upper Assam Basin, India. Due to the basin geological complexity and limitation in seismic resolution, many discontinuities in depositional channels in this fluvial depositional environment have been identified using conventional lithofacies mapping. This study combines three techniques to reproduce continuity of the lithofacies for better reservoir modeling. The first is simultaneous prestack inversion for inverting prestack gathers with angle-dependent wavelets into seismic attributes. A cross-plot of P-impedance and VP/VS ratio from well-log data was used to classify the different reservoir lithofacies such as hydrocarbon sand, brine sand, and shale. The second is the Bayesian approach that incorporates probability density functions (PDFs) of non -parametric statistical classification with seismic attributes for converting the seismic attributes into lithofacies volume and the probability volumes of each type lithofacies. The third technique is multiple-point geostatistical simulation (MPS) using the Single Normal equation Simulation (SNESIM) algorithm applied to training images and probability volumes as constraints for a better lithofacies model. These integrated study results proved that MPS could improve reservoir lithofacies characterization.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Prestack simultaneous inversion for delineation of the Lower Wilcox erosional remnant sandstone beneath the Texas Gulf Coastal Plain: A case study

The Lower Wilcox lowstand sand deposits encased between two sequence boundaries along the Texas Gulf Coastal Plain are of good reservoir quality and usually gas productive. However, the sedimentation is sparsely scattered within such a depositional environment and it is hard to predict by qualitative interpretation methods. Simultaneous inversion of elastic parameters such as P-impedance, S-impedance, and density by the integration of prestack data and well logs allows us to quantitatively characterize the reservoirs and to distinguish them from the surrounding rocks. We have used prestack simultaneous inversion of the elastic parameters for delineation of the gas reservoir in an active field with limited log availabilities. For wells that are missing sonic and density logs, we estimate the parameters using the time-average equation (TAE) and Gardner’s equation, respectively. The shear wave velocity estimation methods are tested and compared using the measured log value. The estimation results are verified using well-log correlations in adjacent wells. Rock-physics analyses on wells are conducted to find the optimal elastic parameters for characterizing the gas-bearing sand. We successfully delineate the reservoir using the crossplot of VP/ VS versus S-impedance values. The inversion results are quality controlled by a producing well in the reservoir zone, and probability maps of each lithology are calculated by the probability density function. Our results from the Lower Wilcox Formation indicate that simultaneous inversion based on the estimated parameters using TAE is feasible, and the gas-bearing reservoirs can be recommended with high confidence.

Characterization and probabilistic estimation of tight carbonate reservoir properties using quantitative geophysical approach: a case study from a mature gas field in the Middle Indus Basin of Pakistan

In this study a tight carbonate gas reservoir of early Eocene (S1 formation) is studiedfor litho-facies estimation and probabilistic estimation of reservoir properties predictionusing quantitative geophysical approach from a mature gas field in the Middle IndusBasin, onshore Pakistan. Quantitative seismic reservoir characterization approachrelied on well based litho-facies re-classification, Amplitude Variation with Offset (AVO)attributes analysis and Pre-Stack simultaneous inversion attributes constrained withcustomized well-log and seismic data (gathers) conditioning. Three main litho-facies(hydrocarbon bearing limestone, tight limestone and shale) are classified estimatedbased on the precise analysis of well data using petrophysical properties. AVOattributes (intercept and gradient) conveniently inspection for amplitude behavior(reflection coefficients) of the possible AVO (class I), fluids and lithologycharacteristics. Probable litho-facies (tight limestone and shale) are estimated usingwell based litho-facies classification and inverted seismic attributes (p-impedance anddensity) from pre-stack simultaneous inversion in a Bayesian framework. Additionally,petrophysical properties (clay volume and porosity) are derived from probabilisticneural network approach using well logs and pre-stack inverted attributes (pimpedanceand density) constrained with sample-based seismic attributes(instantaneous, windowed frequency, filters, derivatives, integrated and time).

Prestack inversion identification of organic reef gas reservoirs of Permian Changxing Formation in Damaoping area, Sichuan Basin, SW China

Organic reef reservoirs in the platform margin of Kaijiang-Liangping trough in Damaoping area, Sichuan Basin are thin in single layer, fast in lateral variation, and have small P-impedance difference from the surrounding rock, it is difficult to identify and predict the reservoirs and fluid properties by conventional post-stack inversion. Through correlation analysis of core test data and logging P-S wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges, and solved the issue that some wells in the study area have no S-wave data. AVO forward analysis reveals that formation porosity is the main factor affecting the variation of AVO type, the change of water saturation cannot affect the AVO type, but it has an effect on the change range of AVO. Through cross-plotting analysis of elastic parameters, it is found that fluid factor is a parameter sensitive to gas-bearing property of organic reef reservoir in the study area. By comparing results of post-stack impedance inversion, post-stack high frequency attenuation property, pre-stack simultaneous inversion and AVO anomaly analysis of angle gathers, it is found that the gas-bearing prediction of organic reef reservoirs by using fluid factor derived from simultaneous pre-stack inversion had the highest coincidence rate with actual drilling data. At last, according to the characteristics of fluid factor distribution, the favorable gas-bearing area of the organic reef reservoir in Changxing Formation was predicted, and the organic reef trap at the top of Changxing Formation in Block A of Damaoping area was sorted out as the next exploration target.

Application of pre-stack simultaneous inversion to predict gas-bearing dolomite reservoir: a case study from Sichuan Basin, China

Pre-stack simultaneous inversion is a seismic exploration technology which is widely applied to recognize lithology and detect oil and gas by analyzing the feature of amplitude variation versus offset (AVO). A gas-bearing reservoir can cause an obvious change in P-impedance and density. To effectively discriminate gas-bearing dolomite within Longwangmiao Fm. in Sichuan Basin of China, the pre-stack inversion method is employed by strict quality procedures. These procedures mainly include rock physics analysis, AVO modeling and pre-stack simultaneous inversion. Using the technology, P-impedance, S-impedance and density are obtained simultaneously. The distribution of gas-bearing dolomite is identified by P-impedance versus density cross-plotting from well data. The final prediction result indicates the existence of gas-bearing dolomite in the upper part of Longwangmiao Fm. in the study area, which is consistent with well data. Generally, pre-stack simultaneous inversion represents an effective method to predict gas-bearing dolomite reservoir and should be applicable in other study areas.