This study presents a hybrid seismic inversion framework to estimate 3D acoustic impedance volumes in a geologically complex and data-limited environment. The approach integrates physics-informed pseudo-well generation, based on calibrated rock physics modeling and variogram statistics, with a deep feedforward neural network (DFNN) that maps multi-attribute seismic data to acoustic impedance while substantially reducing dependence on low-frequency background models and dense well calibration. The compact DFNN serves as a high-dimensional nonlinear mapper that learns the relationship between seismic attributes and impedance logs, a task for which it is well suited, leading to accurate predictions. We generated synthetic elastic logs (compressional and shear velocities, and bulk density) using calibrated rock physics modeling and variogram-constrained stochastic simulation to supplement real well logs. We produced a lithologically diverse and statistically coherent training dataset. This process, applied to only 3 real wells, generated a robust ensemble of 36 synthetic pseudo-wells, effectively addressing the severe data scarcity and providing sufficient training data. Seismic attributes representing amplitude, phase, and frequency characteristics are selected to facilitate the model's ability to resolve subtle geological heterogeneity. The trained DFNN is validated through a leave-one-well-out strategy yielding a cross-correlation coefficient of up to 95.4% and a normalized relative error below 1% when tested on the blind wells. Combining physical modeling with data-driven learning reduces reliance on low-frequency background models and dense calibration. Rather than replacing conventional inversion, it provides a complementary, geologically consistent, and computationally efficient approach for reliable reservoir characterization in offshore environments. Future work may focus on incorporate uncertainty quantification and volumetric convolutional networks to further improve spatial resolution and model reliability in complex subsurface settings.

A Bayesian approach is proposed to estimate litho-fluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Since computing this probability for all possible facies configurations may be impractical, a Markov Chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geological scenario and a well from a selected marine oil field. Two other wells from the same reservoir were used to gather prior distributions. Data from the well, calibration of the rock physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward modeling adaptations on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical datasets are then inverted for litho-fluid facies models under different prior inputs. Two challenging geoelectric scenarios were also tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating both geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also show potential for quantitative reservoir interpretations.

Abstract The transformation of seismic reflection data into quantitative rock properties can be very valuable in all phases of oil and gas exploration and production. In this study, public domain data from Australian offshore have been used to explain the logical workflow, which consists of systematic steps from seismic petrophysics to rock physics modeling and seismic inversion for quantitative rock property estimation. The application of seismic inversion technology offers many rewards, such as: better reservoir definition and management; better resource estimation; and reductions in uncertainty. The main benefit is that it improves direct and intangible interpretation of seismic data to provide meaningful geological boundaries in the subsurface. In order to provide the reliable estimate of rock properties for reservoir modeling the workflow is tested rigorously and divided into three categories which are based on the basic inversion types. The results reveal that relative impedance inversion should be performed first, which then followed by revision of seismic interpretation on impedance data. Full bandwidth rock properties are estimated by deterministic inversion. The results can be improved by several iterations of well to seismic tie, wavelet estimation, and low frequency models. As resolution with accuracy has always been challenging, the stochastic inversion approach is trailered for reservoir characterization and high resolution rock properties have been predicted. This produces a number of possible rock property models, as well as litho facies models and the results can be optionally constrained by the well data. It can be concluded that, to some degree, stochastic inversion is able to overcome the limited seismic bandwidth by integrating the rock physics properties of different lithologies. The examples of seismic inversion are discussed to demonstrate the significant benefits can be obtained by following an optimized workflow that is tailored to deal with the uncertainties that impact the end product. Introduction Seismic surveys are routinely performed to gather information about the geology of hydrocarbon fields. Seismic data, even when it has relatively low resolutions, can still deliver a structural model for the reservoir. The availability and coverage area of the data provides valuable information to constrain the reservoir model. Geophysicists are now able to extract greater quantitative information about reservoir rocks and fluid parameters from seismic data by working backwards using seismic inversion methods. Described as the flip-side of forward modeling, seismic inversion is the process of characterizing rocks and fluids that have produced the seismic data. In a sense, the process involves trying to determine the input (rock properties) by looking at the output (seismic data). It starts with actual measured data, removes the wavelet and derives acoustic impedance from reflection coefficient (Figure-1) with a low frequency model to infer rock properties and deliver earth model.

Quantitative seismic methods are crucial for understanding shale gas reservoirs. This study introduces a dynamic adaptive rock physics model (DARPM) designed to systematically quantify the relationship between physical parameters and elastic parameters within shale formations. The DARPM uniquely adapts to changes in formation dip angle, allowing adaptive reservoir property assessment. An innovative adaptive rock physics inversion methodology is subsequently proposed to compute values for reservoir physical and seismic anisotropy parameters. This is achieved using well log data and building upon the foundation laid by the established DARPM. We introduce the RPM-FCNN (rock physics model—fully connected neural network) framework, seamlessly integrating the DARPM with the corresponding inversion results into a comprehensive model. This framework facilitates a quantitative analysis of the nonlinear relationship between elastic and reservoir physical parameters. Utilizing the trained RPM-FCNN framework, the spatial distribution of reservoir and seismic anisotropic characteristics can be precisely characterized. Within this framework, the organic matter mixture aspect ratio indicates the continuity of organic matter, while the organic matter porosity reveals the maturity of organic matter. Simultaneously, seismic anisotropy characteristics signify the degree of stratification within the reservoirs. This method, therefore, establishes a robust foundation for identifying favorable areas within shale gas reservoirs.

Seismic inversion is a quantitative analysis technique in reservoir geophysics to reveal subsurface physical properties from surface-recorded seismic data. But the most widely used inversion in oil and gas exploration for decades is PP-wave based. P-to-S converted wave, which has shown great success in the imaging of gas clouds, has a different response to rocks and pore-fluids from the PP-wave. A joint use of the PS-wave and PP-wave in the inversion can reduce the ill-posedness of the inverse problem and in particular enables simultaneous inversion for three independent elastic parameters. Conventionally, prestack seismic inversion is based on the incidence angle-dependent reflection coefficients. In my research, I define the seismic reflections and impedances along the ray paths of wave propagation, and these ray paths obey Snell’s law. I adopt the ray-impedance concept, which is a frequency-dependent parameter and is sensitive to fluid contents. Joined interpretation of PP- and PS-wave ray impedances can identify reservoirs, and also has potential in fluid discrimination. Joint inversion of PP- and PS-waves is performed on the constant ray parameter (CRP) profiles. For a constant ray parameter, a pair of PP- and PS-wave traces has exactly the same ray path between the source and the reflection point, which means the PP- and PS-wave reflection events represent exactly the same reflection point, in the horizontal direction. Therefore, PP and PS-wave calibration transforms PS

seismic inversion is the process of transforming seismic reflection data into a quantitative rock-property description of a reservoir. Seismic inversion In geophysics (primarily in oil-and-gas exploration/development), seismic inversion is the process of transforming seismic reflection data into a quantitative rock-property description of a reservoir. Seismic inversion may be pre- or post-stack, deterministic, random or geostatistical; it typically includes other reservoir measurements such as well logs and cores. In geophysics (primarily in oil-and-gas exploration/development), seismic inversion is the process of transforming seismic reflection data into a quantitative rock-property description of a reservoir. Seismic inversion may be pre- or post-stack, deterministic, random or geostatistical; it typically includes other reservoir measurements such as well logs and cores. pre-stack or post-stack seismic resolution or well-log resolution The combination of these categories yields four technical approaches to the inversion problem, and the selection of a specific technique depends on the desired objective and the characteristics of the subsurface rocks. Although the order presented reflects advances in inversion techniques over the past 20 years, each grouping still has valid uses in particular projects or as part of a larger workflow.