P-wave Impedance Research Articles

Characterization of Natural Gas Hydrate Constrained by Well and Seismic Data in Qiongdongnan Basin

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

Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion

During the exploration and development of deep coalbed methane (CBM), delineating the thickness of coal seam and lithofacies of the roof and floor is one of the major challenging tasks. In past attempts, the prediction methods of these parameters have been limited to the conventional inversion. However, the effect of coal shielding on adjacent reflecting layers restricts the identification of underlying sand effectively by conventional inversion. Also, the depth at which the deep CBM zone is located (1,500–2000 m) produces a significant overlap of P-wave impedance and Vp/Vs of sands and shale which increases classification uncertainty between these two lithofacies. We proposed a new workflow for high-precision quantitative seismic interpretation of deep CBM reservoir. Not only P-wave impedance but also GR is selected as the optimized attributes for lithofacies classification. To reduce the effect of strong reflection of coal seam and identifying thin coal layers, the seismic waveform indication inversion method is used to obtain high-resolution results of P-wave impedance and GR. It uses horizontal changes in seismic waveforms to reflect lithological assemblage characteristics for facies-controlled constraints. Then, Bayesian classification theory is used to achieve three-dimensional lithofacies classification with multi-source data. To improve the continuity and accuracy of the interpreted results, a Markov chain is applied in the Bayesian rule as the spatial prior constraint. A well-associated synthetic test and field data application in Ordos Basin demonstrates the accuracy of the proposed workflow. Compared with conventional inversion, the results of proposed workflow showed higher resolution and accuracy. By providing a new solution for the identification of roof and floor lithofacies of deep CBM reservoir, this workflow aims to contribute to the better exploration and development of deep CBM.

Prediction of tight sandstone reservoirs by multiwave joint prestack inversion technology: A case study of the Qiulin area in the Sichuan Basin

The Shaximiao (SXM) Formation of the Middle Triassic in the central Sichuan Basin is a river-lake sedimentary system with an average burial depth of 2800 m. The channel sand bodies of the SXM Formation in the Qiulin area are developed with large lateral variation and high- and medium-porosity sand. This leads to great differences in the seismic response and difficulty in predicting the sand bodies and reservoirs using P-wave data. We carry out well-log analysis for different types of sand bodies. The no. 8 sand body of high porosity can be effectively identified using P-wave impedance (PI). Whereas for the no. 7 sand body of relatively lower porosity (mainly due to the higher shale content in the no. 7 sand compared with the no. 8 sand), sandstone and mudstone can be distinguished using S-wave impedance (SI), and reservoirs can be recognized by the P- to S-wave velocity ratio ([Formula: see text]/[Formula: see text]). Based on the PP wave and the P-to-S converted wave (PS wave) (after matching) angle-stacked data, a prestack inversion of multicomponent data is performed. Multiwave joint inversion results (SI and [Formula: see text]/[Formula: see text]) have better accuracy and stronger stability than the PP-wave inversion in practical application. In addition, the PI of the joint inversion more clearly describes the distribution and boundaries of the channel sand and better matches the prediction and drilling data in sand 8. The SI of the joint inversion can identify subtle sand bodies (weak PI contrast) that are difficult to be detected by PP-wave inversion. The distribution of the reservoir predicted by the [Formula: see text]/[Formula: see text] of the joint inversion is clearer (the no. 7 sand body) and better than that by the PP-wave inversion. This study demonstrates the advantage of the multiwave joint inversion technology in tight sand identification.

What Extra Information Can Be Provided by Multi-Component Seismic Data: A Case Study of 2D3C Prospecting of a Copper–Molybdenum Mine in Inner Mongolia, China

With the decrease in shallow mineral reserves, deep mineral resources have become the focus of exploration. Seismic exploration, renowned for its deep penetration and high spatial resolution and precision, stands as a primary technique in geophysical exploration. In comparison to traditional P-wave seismic exploration, multi-component seismic techniques offer the advantage of simultaneously acquiring P-wave and S-wave data, overcoming the limitations of single P-wave impedance in predicting lithology and enabling high-precision imaging of subsurface structures. Constrained by field survey costs, the reflection seismic illumination is lower and results in a poor signal-to-noise ratio of multi-component seismic data in metallic ore exploration, which poses great challenges in imaging converted S-waves. Based on the seismic and geological characteristics of metallic ores, this study conducts imaging research on metallic ore models through synthetic data and field multi-component seismic data from a copper–molybdenum mine in Inner Mongolia, China. The emphasis is given to PS-wave pre-stack time migration based on precisely sorting the commonly converted point so as to explore the feasibility and technical advantages of multi-component seismic exploration in metal mines. Synthetic data and field data testing demonstrate that PS-wave imaging contains more abundant structural and lithological information compared to PP-waves, indicating promising prospects for the application of multi-component seismic data in metallic ore exploration.

3D rock physics template-based probabilistic estimation of tight sandstone reservoir properties

Quantitative prediction of reservoir properties (e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties’ impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.

Seismic amplitude inversion based on a new PP-wave reflection coefficient approximation equation for vertical transversely isotropic media

We develop a method for the simultaneous amplitude-variation-with-offset or -angle inversion of anisotropic parameters for transversely isotropic media with vertical axis of symmetry (VTI media). First, we introduce a nonlinear PP-wave reflection coefficient approximation equation in terms of only P- and S-wave impedances for isotropic elastic media. Then, by replacing the isotropic part of Rüger’s equation with this equation, we obtain a new PP-wave reflection coefficient approximation equation called the ASI Rüger equation for VTI media. To invert the parameters for VTI media based on the ASI Rüger equation, we adopt the Bayesian generalized linear inversion (GLI) method, a combination of GLI and Bayesian linear inversion, in which the noise and model perturbation are assumed to conform to the zero-mean Gaussian distribution. Compared with Rüger’s equation, the ASI Rüger equation decreases the trade-off between the parameters and reduces the ill-posedness of the inverse problem. The synthetic and field data tests demonstrate the feasibility of our method for inverting VTI media parameters (the vertical P-wave impedance, the vertical S-wave impedance, and Thomsen’s parameters [Formula: see text] and [Formula: see text]).

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

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

Elastic rock properties analysis of diagenetic facies identification of deep tight sandstone in the Xihu Sag, China

In order to investigate the physical relationship between elastic properties and reservoir parameters, and determine sensitive seismic elastic parameters of diagenetic facies of deep tight sandstone in the sparse well network area, 16 samples of typical diagenetic facies were selected for rock physics experiment. Diagenetic characteristics and diagenetic facies of tight sandstone were analyzed using petrographic data, and mineral associations and diagenetic pathways of diagenetic facies were investigated. Then, based on rock physics experiment data and petrographic data, sensitive seismic elastic parameters were determined, by comprehensively using rock physics analysis, correlation analysis, and relationship between diagenetic characteristics, mineral composition and seismic elastic parameters. Sensitive seismic elastic parameters can identify four diagenetic facies of tight sandstone. The results show that shear modulus, p-wave to s-wave velocity ratio, p-wave impedance and porosity data volume are sensitive to diagenetic facies, and the correlation between four seismic elastic parameters is weak. The determination of sensitive seismic elastic parameters in this study makes seismic diagenetic facies research targeted to distinguish four diagenetic facies. Understanding the basic physical significance of four elastic parameters is very important for their application to diagenetic facies prediction.

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.

Quantitative characterization of tight gas sandstone reservoirs using seismic data via an integrated rock-physics-based framework

Seismic characterizing of tight gas sandstone (TGS) reservoirs is essential for identifying promising gas-bearing regions. However, exploring the petrophysical significance of seismic-inverted elastic properties is challenging due to the complex microstructures in TGSs. Meanwhile, interbedded structures of sandstone and mudstone intensify the difficulty in accurately extracting the crucial tight sandstone properties. An integrated rock-physics-based framework is proposed to estimate the reservoir quality of TGSs from seismic data. TGSs with complex pore structures are modeled using the double-porosity model, providing a practical tool to compute rock physics templates for reservoir parameter estimation. The VP/VS ratio is utilized to predict the cumulative thickness of the TGS reservoirs within the target range via the threshold value evaluated from wireline logs for lithology discrimination. This approach also facilitates better capturing the elastic properties of the TGSs for quantitative seismic interpretation. Total porosity is estimated from P-wave impedance using the correlation obtained based on wireline log analysis. After that, the three-dimensional rock-physics templates integrated with the estimated total porosity are constructed to interpret microfracture porosity and gas saturation from velocity ratio and bulk modulus. The integrated framework can optimally estimate the parameters dominating the reservoir quality. The results of the indicator proposed based on the obtained parameters are in good agreement with the gas productions and can be utilized to predict promising TGS reservoirs. Moreover, the results suggest that considering microfracture porosity allows a more accurate prediction of high-quality reservoirs, further validating the applicability of the proposed method in the studied region.

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

Oil and gas prediction basing on seismic inversion of elastic properties in Chaoshan depression, south China sea

The marine Mesozoic is widely distributed in the northeastern waters of the South China Sea and is an important field for oil-gas exploration in the South China Sea. The Chaoshan Depression is the largest residual depression in this sea. At a previous well, LF35-1-1, no oil and gas have been discovered with then pre-drilling prediction techniques. Post-drill analysis shows that the physical properties of the Mesozoic reservoir are not favorable there. So, in accurate prediction of the oil-gas reservoirs is necessary. Since the drilling at the LF35-1-1, extensive surveys and studies have been carried out which shows a number of favorable trapping structures. In the middle low bulge of the Chaoshan Depression, the DS-A structures found with potential reservoirs, complete trap structures, and dual source hydrocarbon supply on both sides, making it the most favorable zone for oil-gas accumulation. We apply the state of art prediction techniques for it using pre-stack seismic raw gather. The sensitivity analysis results of reservoir physical properties indicate that the difference in P- wave velocity between sand and mudstone is 500 m/s, the difference in density is 0.02 g/cm3, and the Poisson’s ratio ranges between 0.11 and 0.33. The Mesozoic sandstone reservoirs in the Chaoshan Depression have characteristics of high velocity and low Poisson’s ratio, and the P-wave velocity, density, and Poisson’s ratio are the main sensitive parameters for predicting reservoir and its oil-gas bearing properties. The density inversion, P-wave impedance inversion, and S-wave impedance inversion jointly characterize the “wedge-shaped” sand body in the DS-A structural area, with a maximum thickness of over 400 m and an area of ∼130 km2. The overlap of the sand body contour map and Poisson’s ratio inversion results indicates that the “wedge-shaped” sand body is an oil-gas bearing sand body. It can be concluded that pre-stack elastic parameter inversion is an effective method for reservoir prediction in deep-sea no-well exploration areas. It has the characteristics of high signal-to-noise ratio, strong stability and reliability, and high accuracy, which is conducive to reduce the non-uniqueness and uncertainty of seismic inversion. The inversion results predict that the DS-A structure is an oil-gas bearing structure.

Seismic response analysis and distribution prediction of source rocks in a survey of the South China Sea

Identification and prediction of high-quality source rocks is the key to obtaining new resources in the exploration area of Cenozoic basins in offshore China. We research the seismic response and distribution of hydrocarbon source rocks using seismic data, well curves, lithologic interpretation, and geochemical analysis. The target is the source rock development zone of the W Formation in a survey of the South China Sea. The results show that the seismic response of thick layer source rocks differ from surrounding rocks in the seismic profile (strong reflections with opposite polarity at the top and bottom and messy or chaotic reflections inside). Seismic reflections of interlayer source rocks have the characteristics of low frequency and continuous strong amplitude. The dominant frequency and maximum amplitude decrease as the number of mudstone layers increases. Through seismic petrophysical analysis, we have obtained three sensitive parameters of source rock in this survey: clay content, P-wave impedance, and elastic impedance. Different classification methods can realize the classification and prediction of hydrocarbon source rocks, among which the Kernel Fisher discriminant analysis method is the best. The prediction results are consistent with the geological background, geochemical information, and well curves.

Combination of extended elastic impedance and rock physics templates for reservoir characterisation in Temblador field, Eastern Venezuela basin

The Temblador Field is located on the southern flank of the Eastern Venezuela Basin. The Oficina Formation, present in the field, represents one of the most important producing Formations in the country. In this project, a detailed characterisation and delimitation of the sands of the Jobo and Morichal Members of the Oficina Formation. The work was outlined in three stages. The first stage consisted of studying the elastic properties of the reservoirs through unconsolidated rock physics modelling through the elaboration of the “Rock Physics Template” (RPT). Likewise, during this phase, different crossed graphs were prepared that allowed lithological discrimination. In the second stage, cubes of elastic properties such as P-wave impedance, S-wave impedance, Mhu-Rho (μρ) and Lambda-Rho (λρ) were obtained through the inversion of the Extended Elastic Impedance. Finally, the third stage consists of the classification of the lithofacies, of the cubes resulting from the inversion, through a Bayesian classification. These results allowed the construction of sand probability maps that allow delimiting the best quality sands within the Jobo and Morichal Members. In this way, the present work provides an additional tool when carrying out reservoir exploration studies, reducing uncertainty when locating a new prospect.

Rock Physical Properties of Longmaxi Shale Gas Formation in South Sichuan Province, China

Deep shale gas (burial depth > 3500 m) in the Longmaxi Formation of southern Sichuan Province will be the primary target for exploration and development in China for a relatively long period. However, the lack of a physical basis for the “sweet-spots” seismic and well-logging prediction is caused by uncertainty in the rock physical properties of deep shale gas in the research area. Acoustic and hardness measurements were performed on shale samples from a deep layer of the Longmaxi Formation in southern Sichuan. Microtextural characteristics of the shale samples were also analyzed by conventional optical microscopy and scanning electron microscopy. Based on these measurements, the rock physical properties of the shale samples and control factors are discussed. It is shown that the deep shale samples have similar properties to the shallow shale in mineral composition, microtexture, and pore type. However, the organic pore in deep shale samples is relatively undeveloped, while the dissolved pores are more developed. For high-quality shale samples (total organic content > 2%), crystal quartz of biological origin forms the framework of rock samples, resulting in effective dynamic and static properties, reflecting the elastic behavior of rigid quartz aggregates. For organic-lean samples (total organic content < 2%), orientated detrital clay particles take the role of load-bearing grains. Therefore, these shale samples’ overall rock physical properties are mainly controlled by the elastic properties of “soft” clay. The load-bearing grain variation from organic-rich shale samples to organic-lean samples results in an overturned “V”-type change in terms of velocity versus content. Organic-rich shale samples also show an apparent low Poisson’s ratio. Organic-rich shale has a slight velocity–porosity trend, while organic-lean shale shows a significant velocity–porosity trend. In addition, due to the difference in rock microtexture between organic-rich and organic-lean shale, these two kinds of reservoir rocks can be discriminated in cross plots of P-wave impedance versus Poisson’s ratio and Young’s modulus versus Poisson’s ratio. Change in hardness also reflects the control of microtexture, and shale samples with biological-origin quartz as load-bearing grains show higher hardness and brittleness. However, the variation in quartz content has less of an impact on hardness and brittleness in shale samples with clay as the load-bearing grain. Our results provide an experimental basis for the geophysical identification and prediction of deep shale gas layers.

Multi-Well Characterisation of Complex Carbonate Reservoirs Using S-Wave Impedance and P-Wave Impedance: Mission Canyon Carbonates (Mississippian), Williston Basin, US, as a Test Case

The complexity of carbonate reservoirs is, in part, caused by the multiple rock-types and porosity types that may occur in any carbonate reservoir. Consequently, a persistent concern has been how to derive accurate quantitative estimates of carbonate reservoir properties from borehole and seismic data. Recent developments in this pursuit involve combined use of S-wave and P-wave data, together with petrophysical definitions of their impedances in a manner that lends them geologic character and significance. S-wave impedance and P-wave impedance are linearly related with their respective velocities. Both relationships are rock-typing criteria since their slope and intercept terms consist of geology-dependent coefficients. Linear-linear crossplots of S-wave impedance versus S-wave velocity and P-wave impedance versus P-wave velocity, using core scale measurements in four wells, support the linearity, pattern of data points, and character of the lines predicted for brine and air/gas pore fluids. The slopes and intercepts obtained by the method of linear regression analysis also show values that are location-specific, as well as exhibit variabilities that are indicative of reservoir heterogeneities from one well to another. These results show strong potential in using impedance-velocity relationships for quantitative interpretations of well logs and reflection seismic data.

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.

Model-data-driven P-wave impedance inversion using ResNets and the normalized zero-lag cross-correlation objective function

Model-driven and data-driven inversions are two prominent methods for obtaining P-wave impedance, which is significant in reservoir description and identification. Based on proper initial models, most model-driven methods primarily use the limited frequency bandwidth information of seismic data and can invert P-wave impedance with high accuracy, but not high resolution. Conventional data-driven methods mainly employ the information from well-log data and can provide high-accuracy and high-resolution P-wave impedance owing to the superior nonlinear curve fitting capacity of neural networks. However, these methods require a significant number of training samples, which are frequently insufficient. To obtain P-wave impedance with both high accuracy and high resolution, we propose a model-data-driven inversion method using ResNets and the normalized zero-lag cross-correlation objective function which is effective for avoiding local minima and suppressing random noise. By using initial models and training samples, the proposed model-data-driven method can invert P-wave impedance with satisfactory accuracy and resolution. Tests on synthetic and field data demonstrate the proposed method's efficacy and practicability.

Determination of dynamic elastic moduli by integration of geophysical, geotechnical and rock physics diagnostic techniques for construction at Ba Na, Da Nang City, Vietnam

Dynamic elastic moduli are essential for civil engineering for many purposes since it has represented the load capacity of the soil before deformation to failure. To estimate the dynamic moduli such as Bulk moduli, Shear moduli, Young moduli, P-wave acoustic impedance, and Poisson's ratio for construction design at Ba Na, Dang Nang City, Vietnam both compressional (P) and shear (S) wave velocity are acquired and interpreted by integrating seismic refraction data and geotechnical data that has been validated by a useful technique called rock physics diagnostic (RPD). The subsurface profile is well divided into distinguished three weathering layers which characterized by compressional (P) wave velocity and shear (S) wave velocity ranging from 300 to 2900 m/s, 500–1500 m/s, respectively, and the unweathered granitic rock beneath the three weathering layers having P-wave velocity, S-wave velocity up to 3000 m/s and 1500–2500 m/s, respectively. The results found that the northwestern part of the survey area has much higher unweathered solid rock layers than those of the southeastern part, implying the higher moduli could be recognized in the northwest that is potentially suggested as an eligible area for foundation design.

Quantitative prediction of porosity and gas saturation based on a new dual-porosity rock-physics model and Shuey’s Poisson ratio for tight sandstone reservoirs

The prediction accuracy of physical parameters from seismic data has strong dependence on the quality of the used elastic parameters and rock-physics models especially for tight sandstone reservoirs. In this study, a new dual-porosity model with stiff and compliant porosities is firstly deduced from classical differential effective medium model according to the characteristics of both pores and fractures development in tight sandstones, it is the product of power function of both compliant and stiff porosities, and is independent of the addition order of pores. Then, the Shuey's AVO (Amplitude Variation with Offset) approximation is expanded, and a new Shuey Poisson's ratio which is the reciprocal of the difference between 1 and Poisson's ratio is defined and inverted from Shuey Poisson's ratio reflection seismogram to ensure the inversion accuracy of both P-wave impedance and ShueyPR. Next, a new two-dimension rock-physics template on P-wave impedance versus Shuey Poisson's ratio is built by the new dual-porosity model and Gassmann equations to characterize the quantitative relationship between P-wave impedance, Shuey Poisson's ratio and physical properties and is used to invert the porosity and saturation through global optimization algorithm. The new method has been applied to quantitative reservoir prediction of tight sandstone of Shaximiao Formation of QL gas field in Northwest Sichuan, China. Real application indicates that the distribution of high-porosity reservoirs with porosity more than 8% and their gas saturation can be accurately predicted.