Rock Physics Model Research Articles

Fluid effect on elastic properties of carbonate and implication for CO2 geologic sequestration monitoring

Laboratory measurement of the elastic properties of carbonates saturated with different fluids is crucial for characterizing reservoirs and monitoring CO2 sequestration. However, it is challenging to distinguish the effects of different fluids. We measure eight carbonate samples under varying pressure and fluid saturation conditions and conduct quantitative analysis. The measurements indicate that water saturation can increase P-wave velocity ([Formula: see text]), whereas oil and CO2 saturation may lead to an increase, decrease, or no change in [Formula: see text]. Meanwhile, all fluid saturation reduces the S-wave velocity ([Formula: see text]). The velocities of the dolomite samples are higher and less pressure sensitive than that of limestone samples. Four attributes are adopted to distinguish the fluid effect, including elastic moduli, impedance, [Formula: see text]/[Formula: see text] ratio, and a newly designed fluid discrimination factor F, which is obtained by multiplying P-wave modulus and square value of density change. Among the attributes, the F factor performs the best as it achieved evident and consistent results for all the samples. Rock-physics models are used to simulate elastic behaviors with the differential effective medium model predicting [Formula: see text] the best. The Gassmann’s equation correctly indicates positive or negative changes in rock velocity after fluid saturation even though its prediction does not exactly match the data. This study highlights the impact of various pore fluids on the carbonate elastic properties through laboratory measurement and theoretical modeling and introduces a novel fluid identification factor, providing insights for reservoir seismic characterization and CO2 sequestration monitoring.

An experimental study of the anisotropic and dispersion signature of shale reservoirs: Implications for seismic well tie

To investigate velocity anisotropy and dispersion in lacustrine shales, we conducted laboratory measurements of acoustic properties at seismic frequencies (2–100 Hz) and ultrasonic frequencies (1 MHz) under varying effective pressures. Our study reveals significant transverse isotropy, accompanied by notable velocity dispersion and attenuation, with anisotropy coefficients ranging from 0.45 to 0.74 for P waves and 0.44 to 0.95 for S waves. Attenuation exhibited a weak dependence on frequency and pressure but showed a strong positive correlation with the clay content. We interpreted the data by integrating geologic and physical analyses. Using rock-physics modeling and the propagation matrix method, we found that velocity dispersion primarily caused a mismatch between seismic data and synthetic seismograms, whereas anisotropy had minimal impact on near-offset seismic data. Our findings suggest that anisotropy and dispersion observed at the core scale persist at the seismic scale. Incorporating these effects into seismic workflows is crucial for improving data processing, inversion, and reservoir characterization. This study provides valuable insights for exploration and production strategies in the study area and similar settings.

Shale pore pressure seismic prediction based on the Hydrogen generation and compaction-based rock physics model and Bayesian Hamiltonian Monte Carlo inversion method

The seismic prediction of formation pore pressure is critically important for the exploration and appraisal of shale oil and gas reservoirs, as well as for identifying optimal drilling targets within these formations. In particular, the complex pressure regimes of deep shale reservoirs introduce substantial challenges to traditional seismic prediction methodologies. A Bayesian Hamiltonian Monte Carlo pre-stack seismic inversion approach is proposed to enhance the accuracy of pore pressure estimations based on an advanced rock physics model that integrates compaction and hydrocarbon generation effects. Initially, a comprehensive petrophysical model is developed to account for the effects of hydrocarbon generation and compaction, establishing a robust framework for the normal compaction trend simulation. Subsequently, a novel formation pressure prediction model is derived based on integrating the Eaton model and the Bower stress hypothesis. Then, the Bayesian HMC seismic inversion method is proposed to predict formation pore pressure in shale reservoirs with elastic impedance data set. The proposed methodology is validated through application to actual well log and seismic data from the Fuling shale oil and gas reservoirs in the Sichuan Basin, which demonstrates significant potential of this method for enhancing the prediction of pore pressure in complex geological settings.

Optimizing seismic-based reservoir property prediction: a synthetic data-driven approach using convolutional neural networks and transfer learning with real data integration

Reservoir characterization through seismic data analysis is essential for exploration and production in the petroleum industry. However, seismic-to-well tie discrepancies, limited availability of high-quality well data, and resolution constraints pose a reliability challenge. While previous studies offer valuable insights, they still struggle to achieve high-resolution predictions in a complex geologically environment given high reliance on well data. This study integrates synthetic data-driven techniques with real data, including convolutional neural networks (CNN) and transfer learning, to improve seismic reservoir characterization. We utilize nearby well statistics and a rock physics model (RPM) to simulate pseudo wells representing various geological scenarios. Synthetic seismic gathers are generated from these pseudo wells, which are based on RPM and local well control, to train the CNN. Transfer learning is then applied to adapt the CNN to better distinguish between real and synthetic data, enhancing reservoir predictions. A comparative analysis of P-impedance predictions from three methodologies: theory-driven Pre-Stack-Seismic-Inversion (TDSI), Deep-Neural-Network (DNN), and our CNN approach, showed that CNN achieved nearly 97% prediction accuracy with low error rates, compared to relatively lower prediction accuracy rates of DNN (86.2%) and TDSI (81.5%) with high error rates, according to robust metrics including R-square, RMSE, MSE, and MAE. These results indicate that CNN not only enhanced resolution but also closely aligned with well data and superior lateral continuity, even in blind well scenarios. This study effectively integrates synthetic data-driven techniques with CNNs and transfer learning to advance seismic reservoir property prediction, offering a robust solution to overcome limitations in traditional and DNN-based approaches.

Anisotropic induced polarization modeling with neural networks and effective medium theory

Interpreting three-dimensional induced polarization (IP) data requires rock physics models that reflect the omnipresent anisotropy of the Earth’s crust. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) can model the IP signatures of rocks with polarizable mineral inclusions. However, it is computationally demanding because it requires numerically solving the depolarization tensors of each inclusion. We aim to streamline GEMTIP simulations by (1) integrating anisotropic background conductivity and triaxial ellipsoidal inclusions in the model and (2) estimating the depolarization tensors with a neural network. We validate the neural network predictions against known solutions for spherical and spheroidal inclusions, and we test our method using data from an actual rock sample. The neural network shows a relative sensitivity of 56% to inclusion shape and 44% to host rock anisotropy and is up to 100,000 times faster than numerical integration. We release the pre-trained neural network implementation as an open-source Python package, thereby providing a new method to interpret the IP signatures of anisotropic rocks.

Carbonate rock physics model using different approaches to estimate rock frame stiffness

Deepwater carbonate reservoirs in the Brazilian pre-salt have emerged as significant hydrocarbon plays in the region and globally. Seismic monitoring of these reservoirs is crucial to provide information for well placement assessments, to reduce uncertainty in reservoir models, and to enhance model-based decision making. To integrate seismic data quantitatively, a petroelastic model (PEM) is required to estimate elastic properties. The modeling of the rock frame stiffness is an essential part in the study of PEM. However, this becomes more complex for carbonate rocks given their diverse pore-types. One way for estimating stiffness is to model each distinct pore-type and include them into the overall rock frame (different inclusion models). In this research, an alternative approach is explored to estimate the rock frame stiffness. A proxy model is employed, and its coefficients are subsequently calibrated with well-log information. Two PEMs were developed using different approaches for rock frame stiffness modeling. The first was inspired by inclusion models and incorporated two pore types (compliant and stiff pores) for the modeling process. The second considered a mathematical regression model as a proxy to relate reservoir porosity to the rock frame stiffness. These two PEMs were tested on the Brazilian pre-salt carbonate rocks of the Barra Velha (BVE) formation using well-log data from three wells and core sample measurements for one well. The accuracy of the PEMs’ performances was assessed by measures (such as, mean absolute error and root mean square error) and visual comparisons of their predictions. The results showed that both PEMs predicted velocities (compressional and shear) within an acceptable match with the actual well-log data. Similarly, when tested on the core samples, the PEMs produced comparable results, which indicates the validity of the proxy, not only at the well-log scale but at the core scale. The visual comparison and the accuracy analyses of the results for all three wells confirmed that the two PEMs had comparable predictions. This is significant given that the proxy simplifies the modeling process and facilitates the representation of various pore-types in the prediction of elastic properties in carbonate rocks. Overall, the proxy for the rock frame stiffness modeling is a computationally less expensive model compared to the inclusion models which involve modeling of various pore-types. It also offers a straightforward model which enables the quantitative integration of seismic data in a multidisciplinary team of geosciences and petroleum engineers (for instance, early engagement of petroleum engineers in 3D/4D seismic history matching).

Water flow pattern recognition and fracture characterization in near-surface chalks using time-lapse ground-penetrating radar: An experiment

Predicting subsurface water flow is inherently complex due to the heterogeneity of geological formations. Water infiltration occurs through various pathways, such as pore throats, fractures, or faults, depending on the sediment type. In an effort to enhance our understanding of water flow within heterogeneous, fractured chalks formations, we integrate ground penetrating radar (GPR), acoustic measurements, and photogrammetric surveys to examine the flow patterns. We first conduct a field experiment of injecting water into chalks and monitor the water flow through a time-lapse GPR reflection survey. Then we identify the water-affected zone by analyzing the GPR data, and correlate the water-affected zone with the identified fractures on the exposed strata. Finally, we verify the water effect through measured acoustic velocities of acquired samples. Our analysis of the GPR data encompasses direct subtraction, traveltime delay, electromagnetic (EM) velocity changes, and water saturation variations. These analyses consistently indicate a predominant southeastward flow direction. This finding is corroborated by the fractures identified through photogrammetry, which closely match the water-affected zone, suggesting that water flow is indeed fracture-driven. The measured acoustic velocities are utilized to estimate water saturation using an iso-frame rock physics model, which shows consistency with the saturation estimated through EM velocity, further validating the monitoring results. These findings advance the understanding of fluid flow and its interaction with fracture system in heterogeneous chalks, and provide a reference for future studies by demonstrating the efficacy of integrating photogrammetry-identified fractures, EM velocity, and acoustic velocity to verify monitoring outcomes.

Regionalized multiple-point statistical simulation for calibrating process-based geologic models to seismic data

Calibrating process-based geologic models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geologic model is used as a training image for statistical pattern recognition. First, we define a rock-physics model from the process-based geologic model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geologic pattern recognition and regeneration under seismic data constraints. The method differs from the conventional MPS method in several ways: (1) the training image is a process-based geologic model of the reservoir of interest, thus defined on the same grid of the reservoir model; (2) the training image is generally nonstationary but there is no need to partition the nonstationary training image into pseudostationary ones; (3) the geologic facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; (4) multiple-point statistics are based on Bayes’ law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; and (5) the method does not involve an iterative optimization procedure. Therefore, it also differs from the neural-network-based machine-learning approach, where the data conditioning is achieved through an iterative optimization procedure. These differences make our method advantageous for calibrating process-based geologic models. The two examples with synthetic data illustrate the effectiveness of the method. The first one is derived from a satellite image of a river delta, and the second one from the Stanford VI reservoir model.

Estimating permeability of rock fracture based on geometrical aperture using geoelectrical monitoring

The permeability of rock fracture is essential for fluid flow and storage stability in subsurface engineering and geological fluid resource development. Estimating this permeability using geoelectrical monitoring offers a promising approach. However, quantitatively interpreting the relationship between the resistivity (ρ) and permeability (k) in saturation zone is limited by the scarcity of rock-physical models for rock fracture. In this study, the three-dimensional (3-D) fractures, including six groups of fractal dimensions, 25 fracture apertures and six contact areas, were generated using the Weierstrass function method. The resistivity and the k-ρ relationship in these 3-D fractures were investigated through experiments and numerical simulations. Then, the effects of aperture, fractal dimension, and contact area on resistivity were examined. The results show that the resistivity is dominated by contact area when the aperture is below a critical fracture aperture, and by fracture aperture when it is above the critical fracture aperture. Additionally, higher fractal dimensions are found to increase the critical fracture aperture. An empirical formula for describing the k-ρ relationship is established. This study demonstrates that geoelectrical monitoring has potential as a long-term technology for evaluating rock fracture permeability in subsurface geoengineering projects. Once fracture resistivity is monitored in advance, the empirical formula can be efficiently applied in the field for rapid permeability estimation.

Estimating pore pressure in tight sandstone gas reservoirs: A comprehensive approach integrating rock physics models and deep neural networks

Pore pressure serves as an important driving power for subsurface fluid migration and therefore has a significant impact on gas accumulation and enrichment in tight sandstone reservoirs. Tight gas is typically produced in overpressure regions, where pressure coefficients are notably elevated. Thus, it is crucial to establish an effective methodology for precise pore pressure estimation. This study introduces an approach to improve pore pressure prediction by incorporating rock physical modeling and deep neural networks (DNNs) into the classical Eaton method. Compared to conventional techniques relying on empirical correlations between pressure coefficients and elastic properties, the proposed method considers the influence of porosity, fluids, and lithology, which could enhance reliability in pore pressure prediction. Meanwhile, a prediction model is developed using logging data and DNNs to estimate mineralogical volumetric fractions based on elastic properties. This prediction model allows improved estimation of rock matrix elastic properties using seismic-inverted data, which is crucial for estimating normal compaction velocity to extend pore pressure prediction from individual boreholes to the whole study area. Real data applications demonstrate that the predicted pressure coefficients derived from seismic data using the method presented in this paper align well with the gas enrichment estimated in previous studies for the tight sandstone reservoirs. Furthermore, regions with high values of pressure coefficients correspond to high gas content. These findings validate the effectiveness of the proposed methodology, which can provide valuable insights for identifying potential tight sandstone reservoirs.

Shale reservoir rock physics modeling and “sweet spot” prediction based on digital core

Abstract With the increasing advancement in shale exploration and development, the complex pore structure and organic-rich characteristics of shale have gradually become the focus of rock physics models. This study combined digital core technology to obtain detailed information on the shale mineral composition, content, pore and crack contents, and composition. The isotropic self-compatible approximation (SCA) model was used to couple the shale minerals and hard pores to construct a brittle mineral framework. The VRH model was used to mix kerogen and clay, and the SCA-DEM (differential equivalent medium) model was used to add organic pores to construct a clay organic matter mineral framework. The anisotropic SCA model treated the clay organic matter mineral framework as an inclusion added to the brittle mineral framework to construct the shale mineral framework. The Eshelby–Cheng model was used to add fracture to the mineral framework and establish a physical shale model. This model was used to optimize the selection of sensitive elastic parameters for physical properties such as brittle mineral and kerogen content, fracture density, and porosity, and the optimization results were combined to construct an explanatory quantity template. In addition, according to actual data from a study area in southwestern China, we combined to the interpretation chart established by the model to perform isotropic inversion. Then, we analyzed and interpreted the brittleness index and total organic carbon content of the reservoir and predicted the sweet spot area of the shale reservoir.

Shale anisotropic rock-physics model incorporating the effect of compaction and nonplate mixtures on clay preferred orientation

Compared with other sedimentary rocks, the strong elastic anisotropy of shale is extremely prominent, which is mainly caused by the preferred orientation and platy nature of its clay minerals. Especially in seismic reservoir characterization, a suitable and correct estimation of the shale elastic anisotropy can improve the accuracy of the shale seismic inversion and prediction. Due to the long-term compaction of shale and the rearrangement of minerals, its microstructure and macrostructure are more complex, resulting in obvious anisotropic characteristics of shale. Existing methods do not incorporate the impact of nonplate particles on clay platelets, or indirectly incorporate it through empirical formulas, resulting in poor applicability and errors in the rock-physics models. To reveal the main causes of the anisotropy of clay minerals, a theoretical model incorporating the effect of compaction and nonplate particles on the preferred orientation of clay platelets is developed using experimental data and electronic scanning results. Based on theoretical analysis, an orientation distribution function (ODF) based on the effect of compaction and nonplate particles is derived, which not only incorporates the influence of compaction but also further incorporates the effect of other nonplate particles, such as quartz, which makes the established shale anisotropic rock-physics model more reasonable and accurate. Then, an improved anisotropic shale rock-physics model is developed using the compaction and nonplate particles-based ODF. The prediction results indicate that the presence of nonplate particles has an inhibitory effect on the preferred orientation of clay platelets, which is verified by the measured experimental data and indicates that our method is reliable and effective.

Quantification of soil water content by machine learning using enhanced high-resolution ERT

The accurate acquisition of soil water content is a fundamental cornerstone of research into hydrological processes and agricultural engineering. Electrical Resistivity Tomography (ERT) has been validated for hydrological studies and soil monitoring. The establishment of a quantitative relationship between ERT resistivity data and soil water content is usually based on rock physics models. However, the applicability of such models in complex environments and the acquisition of the relevant parameters pose a certain challenge. In addition, the spatial resolution of ERT limits its application in soil moisture assessment. Therefore, a machine learning-based approach is proposed in this study to determine the quantitative relationship between resistivity and soil water content. We investigate the integration of three machine learning models (KNN, RF, XGBOOST) with ERT to predict the water content of clay soils. The results show that the RF model achieves an R2 of 0.92 with an RMSE of 0.41. To improve the ERT resolution, a new data collection method (MRU) is introduced in this study by increasing the density of ERT data collection. A comparative analysis is conducted between traditional ERT data collection methods and the MRU approach in terms of soil water content prediction accuracy. The results show that the MRU method of data collection improves the accuracy of soil water content prediction by an average of 57% compared to traditional methods. This study confirms the feasibility of using machine learning models to establish mappings between resistance and water content and shows that the MRU data collection method for ERT effectively improves the accuracy of predicting soil water content. These results provide a new perspective for hydrological process research and agricultural monitoring technology.

Laboratory measurements of water saturation effects on the acoustic velocity and attenuation of sand packs in the 1–20 kHz frequency range

AbstractWe present novel experimental measurements of acoustic velocity and attenuation in unconsolidated sand with water saturation within the sonic (well‐log analogue) frequency range of 1–20 kHz. The measurements were conducted on jacketed sand packs with 0.5‐m length and 0.069‐m diameter using a bespoke acoustic pulse tube (a water‐filled, stainless steel, thick‐walled tube) under 10 MPa of hydrostatic confining pressure and 0.1 MPa of atmospheric pore pressure. We assess the fluid distribution effect on our measurements through an effective medium rock physics model, using uniform and patchy saturation approaches. Our velocity and attenuation (Q−1) are accurate to ±2.4% and ±5.8%, respectively, based on comparisons with a theoretical transmission coefficient model. Velocity decreases with increasing water saturation up to ∼75% and then increases up to the maximum saturation. The velocity profiles across all four samples show similar values with small differences observed around 70%–90% water saturation, then converging again at maximum saturation. In contrast, the attenuation increases at low saturation, followed by a slight decrease towards maximum saturation. Velocity increases with frequency across all samples, which contrasts with the complex frequency‐dependent pattern of attenuation. These results provide valuable insights into understanding elastic wave measurements over a broad frequency spectrum, particularly in the sonic range.

Petrophysical parameter estimation using a rock-physics model and collaborative sparse representation

The estimation of petrophysical parameters is key in the identification of underground reservoirs. Current petrophysical parameter estimation methods are typically constrained by the choice of particular rock-physics models, necessitating the use of distinct models for various reservoirs. Furthermore, the inherent pronounced nonlinearity of these models presents significant challenges to the solution process in reservoir parameter inversion. Although linearized petrophysical inversion methods can simplify the solving process, they can introduce errors due to linearization. To address these limitations, we develop a petrophysical parameter estimation method driven by a rock-physics model and collaborative sparse representation (CSR). In this approach, the rock-physics model governs the fundamental pattern of the petrophysical parameters, with the CSR introducing perturbations to minimize model errors. Our method maximizes the use of well-log data and the rock-physics model, thereby reducing errors associated with linearized rock-physics (LRP) models and enhancing the method’s adaptability. We use the joint dictionary method to learn features and correlations among multiple parameters from the existing well-log data. This learned dictionary is then used as a CSR regularization constraint and applied to refine the LRP inversion objective function. Finally, the objective function is minimized using the block coordinate descent method to predict the petrophysical parameters. The effectiveness of this method in enhancing the adaptability and accuracy of petrophysical parameter estimation is confirmed through synthetic and field data tests.

Tuning effect in time-lapse seismic inversion for CO2 plume monitoring at Sleipner field

Over 20 million tons of CO2‌ have been injected into the sandy Utsira formation of the Sleipner field in the North Sea basin. The thin layering of sands, CO2-saturated sands, and intra-formation thin shales cause interference effects in the seismic response of the monitor surveys. Initial analysis of the amplitude change suggests over 60 % change in the relative acoustic impedance of the reservoir between the 1996 and 2010 surveys. This study follows a multi-stage inversion scheme applied to time-lapse seismic monitoring of the Sleipner field. At each stage, the errors and uncertainties caused by noise and tuning are deeply analyzed. Time-shift estimation from seismic data shows spurious features caused by tuning, specifically using the window-based methods. The time-strain inversion builds a low-frequency initial model for the subsequent model-based inversion but is contaminated by remnants of noise and interference. The synthetic wedge modelling and analysis provides the origins and severity of the tuning error in time-shift and time-strain estimations at the Sleipner field. The model-based inversion removes noise and injects high-frequency components into the results, improving the outcome of time-strain inversion. However, it fails to fully eliminate the tuning imprints and leaves strong traces of error on the results. Afterward, the time-lapse Bayesian seismic inversion slightly adjusts the outcome and shows how deep the influence of interference effect on the time-lapse inversion is. In addition, the complementary discussion on rock physical models in estimating the saturation changes highlights how the tuning error can lead to flawed quantitative interpretation.

Influence of ice properties on wave propagation characteristics in partially frozen soils and rocks: A temperature-dependent rock-physics model

A better understanding of the temperature effects on the propagation characteristics of elastic waves in frozen soils and rocks is imperative for accurately quantifying their freezing degrees. Although the existing rock-physics models based on the three-phase Biot (TPB) theory adeptly interpret observed velocity variation with temperature (VVT) curves, they often lack a comprehensive understanding of the mechanisms underlying attenuation variation with temperature (AVT) curves. In this study, we first extend the TPB theory to incorporate the temperature-dependent properties of ice, such as changes in volumetric fraction, morphology, and viscoelasticity, by integrating the relevant thermodynamic laws. Model parameters related to ice properties and interactions, such as rigidity, shear moduli, density, and friction, are redefined. Then, using a numerical rock-physics modeling approach, we examine influential factors and modes of the wave VVT and AVT responses. Our results indicate that the P- and S-wave velocities increase with source frequency, consolidation degree, and frame-supporting ice content, whereas they decrease with temperature and pore-floating ice content. The P- and S-wave attenuation factors increase with the frame-supporting ice content and decrease with the consolidation degree. Rising temperatures tend to amplify the peak magnitude of the P-wave attenuation factors and shift the central frequency of the S-wave attenuation factors. Finally, within a temperature-controlled laboratory environment, we conduct ultrasonic wave transmission testing on brine-saturated sediment and rock specimens. The results demonstrate that as the temperature increases from −15°C to −3°C, the P- and S-wave velocities decrease, whereas the P-wave attenuation factors decrease and the S-wave attenuation factors initially rise before declining. Our viscoelastic TPB theory outperforms the existing ones in interpreting the S-wave AVT observations. This temperature-dependent rock-physics model holds promise for interpreting the sonic logging data in the time-lapse monitoring of permafrost, glaciers, and Antarctica.

ROCK PHYSICS SIMULATION OF RESERVOIR SAND_K2 IN ‘KUTI’ FIELD, DEEP OFFSHORE NIGER DELTA

Rock physics modelling has been employed in studying the elastic behaviors of reservoir SAND_K2 in the ‘KUTI’ Field, Niger Delta under a plausible production scenario of increasing water saturation. The study aimed to investigate how variation in reservoir fluid saturation will influence seismic attributes and determine the characteristics of expected time-lapsed (4-D) seismic signals that may be generated due to the effects of production. Multivariate cross-plot analyses of petro-elastic parameters were carried out to establish the seismic characteristics of the target reservoir with different pore fluids in its natural state before production. Fluid replacement modelling was applied to calculate measurable changes in the reservoir’s seismic characters by steadily increasing the water saturation from its initial value to 100%. Synthetic logs of petro-elastic parameters were created for different values of water saturation and combined into property cross-plots to predict the dynamic seismic response of the reservoir as brine gradually replaced hydrocarbon. Results showed that fluid changes from hydrocarbon to brine produced significant and quantifiable seismic signatures for time-lapse monitoring investigation. At some future points in the production history of the field under increasing water saturation conditions, the density, P-wave velocity and acoustic impedance of SAND_K2 increase and produce a negative seismic difference data of P-wave amplitude. Based on the responses of reservoir SAND_K2 under modelled production conditions, the study concluded that rock physics analysis can help to optimize and enhance the success of time-lapse monitoring of the KUTI Field.

Advances in rock physics for pore pressure prediction: A comprehensive review and future directions

Advances in rock physics have significantly enhanced pore pressure prediction, a critical aspect of subsurface exploration and drilling operations. This comprehensive review delves into the latest developments in rock physics methodologies, integrating empirical, theoretical, and computational approaches to predict pore pressure more accurately. Traditional pore pressure prediction methods often rely on well log data and seismic attributes, but recent advancements have introduced innovative techniques that leverage the physical properties of rocks to provide more reliable predictions. Key advances include the development of improved rock physics models that better account for the complexities of subsurface environments, such as heterogeneity and anisotropy. These models integrate data from various sources, including well logs, core samples, and seismic surveys, to create a more comprehensive understanding of the subsurface. Additionally, the application of machine learning and artificial intelligence to rock physics has opened new avenues for analyzing large datasets, identifying patterns, and refining predictive models. This review also examines the role of laboratory experiments and field studies in validating and calibrating rock physics models. High-pressure and high-temperature experiments have provided valuable insights into the behavior of rocks under different conditions, which are essential for accurate pore pressure prediction. Field studies, on the other hand, offer real-world data that help in fine-tuning models and methodologies. Future directions in rock physics for pore pressure prediction include the integration of advanced geophysical techniques, such as full-waveform inversion and distributed acoustic sensing, which offer higher resolution data and more detailed subsurface imaging. The use of cloud computing and high-performance computing platforms is also expected to enhance the processing and analysis of large datasets, making predictive models more efficient and scalable. The comprehensive review concludes by highlighting the importance of interdisciplinary collaboration in advancing rock physics methodologies. By combining expertise from geophysics, petrophysics, geomechanics, and data science, the field can continue to innovate and improve the accuracy and reliability of pore pressure predictions, ultimately enhancing exploration and production efficiency in the oil and gas industry. Keywords: Advances, Rock Physics, Pore Pressure, Prediction, Future Directions.

Bayesian linearized inversion for petrophysical and pore-connectivity parameters with seismic elastic data of carbonate reservoirs

Abstract Carbonate reservoirs are important targets for promoting the oil and gas reserve exploration and production in China. However, such reservoirs usually contain developed complex pore structures, which heavily affect the precision in seismic prediction of petrophysical parameters. As one of the most important parameters to characterize reservoir rock, pore-related parameters can not only describe the pore structure, but also be used to evaluate the oil/gas-bearing capabilities of potential reservoirs. The conventional rock-physics models (e.g. Gassmann's model) are formulated assuming fully connected pores, which is unable to accurately capture the geometrical complexity in real rocks. To characterize the influences of multiple pores on the elastic properties, this work presents a rock-physics modeling method for carbonates, wherein the percentage composition of connected pores is equivalently quantified as the pore-connectivity factor. The method treats the pore-connectivity factor as an objective variable to characterize the spatial variations of pore structure. Specifically, the method combines the differential equivalent medium theory and Gassmann's model, and derives a linearized forward operator to quantitatively link porosity, water saturation, and pore-connectivity factor to seismic elastic parameters. According to the Bayesian linear inverse theory, the simultaneous estimation of petrophysical and pore-connectivity parameters is achieved. To characterize the statistical variations with multiple lithofacies, the Gaussian mixture model is employed to quantify the prior distribution of the objective variables. The posterior distribution of the objective variables is analytically expressed with the linearized forward operator. Numerical experiments show that the accuracy of the proposed method in predicting elastic parameters is improved. Compared with the conventional Xu–White model and the varying pore aspect-ratio method, the accuracy of predicted P-wave velocity increases by 10.29% and 1.33%, respectively, and the predicted S-wave velocity increases by 6.44% and 0.03%, in terms of correlation coefficient. The application to the field data validates the effectiveness of the method, wherein the porosity and water saturation results help indicating the spatial distribution of potential reservoirs.