Compressional Wave Velocity Research Articles

Impact of Pulse Plasma-Based Shockwave Technology on Sandstone Porosity and Pore Connectivity Using Ultrasonic Compressional Wave Velocity and Microtomography

Impact of Pulse Plasma-Based Shockwave Technology on Sandstone Porosity and Pore Connectivity Using Ultrasonic Compressional Wave Velocity and Microtomography

Cement-based and alkali-activated controlled low-strength materials made from cup lump rubber for use as road materials

This research explores the use of cup lump rubber (CLR), an agricultural by-product, as a component in controlled low-strength material (CLSM) for pavement applications in road construction. Two distinct CLSM mixtures were developed: one based on cement and the other on alkali activation. The study evaluated the workability, mechanical properties, and microstructures of both CLSM formulations. Key fresh properties, including slump flow, setting time, and bleeding, were analysed to assess their impact on the self-compaction process. Mechanical characteristics such as unconfined compressive strength, resilient modulus, and wave velocities were also measured. Some CLSM mixtures, both cement-based and alkali-activated, were found to meet the requirements for soil cement bases and subbases. Notably, the resilient modulus values showed significant improvement after 28 days, with certain mixtures achieving subbase-quality gravel standards. The study concludes by recommending the use of both cement-based and alkali-activated CLSMs in pavement design, highlighting their potential to enhance the field of pavement engineering.

Simultaneous inversion of four physical parameters of hydrate reservoir for high accuracy porosity estimation

AbstractEstimation of the porosity of a hydrate reservoir is essential for its exploration and development. However, the estimation accuracy was usually less certain in most previous studies that simply assumed that there is a linear relationship between the porosity and single‐elastic wave velocities or other rock physical parameters, thus affecting the evaluation of the reserves. In the three‐phase Biot‐type equations that are fundamental to model a hydrate‐bearing reservoir, porosity, alongside hydrate saturation, mineral constituent proportions and hydrate–grain contact factor, is non‐linearly responded by density, compressional and shear wave velocities. To improve porosity estimation, we propose to invert simultaneously four‐parameter (porosity, hydrate saturation, mineral constituent proportions and hydrate–grain contact factor) using an iteratively nonlinear interior‐point optimization algorithm to solve a nonlinear objective function that is a summation of the squared misfits between the well log and three‐phase Biot‐type equation–modelled density, compressional and shear wave velocities. A test in Mount Elbert gas hydrate research well was conducted for the case of a gas hydrate stratigraphic test well where elastic wave velocities, density, porosity and mineral composition analysis data are available. The four‐parameter inversion yielded a lower root mean square error for porosity (0.0245) across the entire well‐logging section compared to previous estimations from the linear relationship, post‐stacked and pre‐stacked seismic traces as well as the pore‐filling effective medium theory model applied to other well cases. Additionally, the other three parameters demonstrated good agreement with well logs. Inversion tests conducted at three additional hydrate sites also produced accurate results. Consequently, the new method surpasses previous approaches in porosity estimation accuracy.

Developing some models to predict the uniaxial compressive strength of various sedimentary rocks (Case studies: Large dam site and mine in Southeast China)

Direct determination of uniaxial compressive strength (UCS) is time-consuming, expensive, and challenging. Therefore, this study aims to indirectly predict UCS using statistical and machine learning methods. First, laboratory measurements were conducted to determine carbonate percentage, water absorption percentage, moisture content, porosity percentage, P-wave velocity, and UCS of sandstone, dolomite, dolomitic limestone, marl limestone, and limestone samples. Subsequently, various models were established to predict UCS using multivariate linear regression (MVLR), random forest regression (RFR), Gaussian process regression (GPR) based on squared exponential kernel function, and adaptive neuro-fuzzy inference system (ANFIS). The influence of Jurassic limestone texture on physical and mechanical properties in stable and unstable slopes in the Three Gorges Dam site and reservoir (TGDSR) showed that the percentage of mudstone texture in unstable slopes is higher than in stable slopes. The UCS, density, compressional wave velocity, and percentage of carbonate in unstable slopes is less than stable slopes. The results indicated that the average measured UCS is 60.60 MPa. Moreover, the average predicted UCS based on the three intelligent methods was 60.55 MPa. The percentage difference between the measured and predicted UCS was −0.09 %, demonstrating that the average error of the three employed methods is less than one percent, highlighting the high accuracy of these methods in estimating rock UCS. Notably, based on error level, Taylor diagram, Wilmot agreement index (WAI), A10 and A20 indices, Nash-Sutcliffe efficiency (NSE), and PM (performance metric) criteria the RFR exhibited superior precision (R2=99 %, and RMSE= 0.06 MPa) compared to the other used methods. The Kruskal-Wallis test (KWT) results showed that there is no significant difference between the measured and predicted values.

Deep learning-based inversion of soil and rock compaction density utilizing Falling Weight Deflectometer (FWD) and Surface Waves

Methods relying on a single dynamic parameter to estimate the material density for the compaction quality testing of earth-rock dams and roadbed projects, present certain limitations in terms of applicability, testing accuracy, and work efficiency. The theory of elastic wave propagation reveals that the compaction density of the medium results from the combined interaction of multiple material properties. This study proposes a method that utilizes the transient surface wave method and a vehicle-mounted Falling Weight Deflectometer (FWD) to jointly enhance the accuracy of soil and rock material compaction detection. The transient surface wave method is employed to extract surface wave velocity and compressional wave velocity. The analysis of the FWD signal involves extracting dynamic parameters closely related to material density, including stiffness coefficients, central frequencies, and stiffness impedances, from time-domain, frequency-domain, and mechanical impedance analyses. A fully connected deep neural network is introduced to intelligently estimate the compaction density. The deep learning model is optimized by selecting the optimal activation function and optimization algorithm, as well as additional tuning. Extensive testing shows that deep learning model produce results closely approximating the true compaction density values. The average relative errors for the wet density and the dry density are 2.9% and 2.85%, respectively, meeting the quality control requirements for density testing. The proposed approach enables rapid detection and control of the compaction quality of soil and rock materials, providing a new method for the in-situ rapid detection of compaction quality.

New and Highly Accurate Static Young's Modulus Model Using Machine Learning Techniques.

Static Young's modulus (E s) is a critical property required in numerous petroleum calculations. Various models to forecast E s have been proposed in the literature. However, existing models, by and large, lack precision and are confined to specific data set ranges. This study proposes an alternative approach for E s determination, utilizing different machine learning methods, such as an adaptive neuro-fuzzy inference system (ANFIS). In these proposed methods, the predictor variables include bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc). The models were trained on a data set comprising 1853 hydrocarbon reservoir rock samples from globally diverse locations. They were evaluated using trend, group error, and statistical error analyses. To test the efficacy of the proposed models, the optimally performing model was identified and used to detect the rock types along with the previously published models. Results indicated that ANFIS is the optimum model and can predict E s with an average absolute percentage relative error (AAPRE) of 5.1% and a correlation coefficient (R) of 0.9602. The ANFIS method has some benefits over other machine learning approaches insofar as its superiority in reaching a quicker decision about the mapped relationship between the inputs and outputs because it combines artificial neural networks and fuzzy logic in one tool. The ANFIS can perform a highly nonlinear mapping and displays a better learning ability. The proposed ANFIS model demonstrates its ability to capture accurate physical relationships between input rock properties and E s through trend analysis, which shows that increasing the RHOB increases the E s. Contrarily, increasing the DTc and DTs reduces the E s. Furthermore, the ANFIS model can accurately detect the rock types based on its E s determinations. This research demonstrates the importance of accurately predicting E s for the proper identification of rock types. Thus, this study offers potential advancements in geological assessments of hydrocarbon reservoirs and improvements in many petroleum engineering applications.

Elasticity of epidote at high pressure and its implications for the velocity anomaly in subduction zone

Hydrous minerals play a critical role in modifying the physical and chemical properties of the Earth’s interior. Among those, epidote is an important hydrous mineral in greenschist and blueschist phases of the metamorphosed subducting crust at shallow depth (30-60 km). Here, we measured the compressional (P) and shear (S) wave velocities of a polycrystalline epidote sample at pressures up to 7 GPa and room temperature by means of ultrasonic interferometry. The obtained sound velocities and elastic moduli of epidote increase monotonically with pressure. Finite strain analysis on those data set yielded the elastic moduli and their pressure derivatives of epidote at ambient condition as follows: KS0=115.2GPa, G0=66.7GPa, Ks′=4.6, G′=1.1. Using the elastic properties of epidote, we set up a model to better understand the velocity jumps in the subducted oceanic crusts concerning the blueschist-eclogite transition at 60-90 km depths. Our results indicate that the calculated P and S wave velocity jumps are in good agreement with those seismic observations in the typical subduction zones such as northeastern Japan and southwestern Japan. The eclogitization from epidote bearing blueschist may provide an explanation for the wave velocity anomalies occurred in those regions.

Ice matrix composites for Cryo-ultrasonic testing

Cryo-ultrasonic testing utilizes polycrystalline ice coupling to enable the inspection of metallic components with complex shape. The relatively high velocity of compressional waves in ice (approximately 4000 m s−1) and its ability to support the propagation of shear waves, significantly strengthen the ultrasonic transmission through curved interfaces over conventional water coupling. This paper explores the possibility of further enhancing the ultrasonic properties of ice by dispersing solid particles in water before it is frozen. Complex physicochemical phenomena occur when aqueous dispersions freeze which can lead to a solid material with microstructural characteristics that may be unfavorable to the propagation of ultrasonic waves. Here, these effects are controlled to produce a composite material consisting of alumina nanoparticles in an ice matrix. The composite exhibits compressional and shear wave velocities of approximately 4800 m s−1 and 2700 m s−1 , respectively. Importantly, the mass density of the material is more than twice as large as the density of water. Finally, it is shown that a phenomenon similar to a glass transition occurs during freezing which results in low ultrasonic attenuation when the temperature approaches – 100 °C.

Determining the geomechanical units using rock physics methods

The stability of the drilled wellbores in carbonate formations is of great importance. This paper is a new approach to determine the elastic properties of the wellbore based on rock physics methods using the Hashin-Shtrikman bounds to determine the wellbore stability. Initially, the static elastic parameters were determined by two different methods, namely the rock physics method and DSI log, followed by clustering through Multi-Resolutional Graph-Based clustering (MRGC) and calculating geomechanical-units (GMUs) of each method separately. The obtained results from DSI log and rock physic methods were then compared followed by determining the wellbore stability. The results showed that the correlation between shear and compressional wave velocity obtained from the petrophysical method with the measured values of shear and pressure wave velocity in the well was 0.94 and 0.90, respectively, which shows that the petrophysical method can estimate these two logs with high accuracy, and it can be a suitable alternative instead of using the Dipole Shear Sonic Imager (DSI). It was observed that the geomechanical units of elastic parameters calculated by proposed rock physics method are in good agreement with those obtained from DSI log. Rock-physic method can be a good alternative for when expensive DSI logs are missing.

Pemodelan Bawah Permukaan Berdasarkan Nilai Kecepatan Gelombang Kompresi (vp) di Kawasan Rembesan Hidrokarbon Sangubanyu, Jawa Tengah

Sangubanyu is located in Central Java, Indonesia, besides having some hot springs it also has crude oil seepages that have been known since 1971, to understand the cause of these seepages subsurface structure analysis is needed by recording microtremor data at 63 points. The HVSR Method is used to process micrometer data to find subsurface conditions by wave velocity, Compressional wave velocity (vp) is part of this method. vp data could be used to interpret the layer types of subsurface conditions. The southern of the study area has about 112 to 1182 m/s of vp, and the northern has about 506 to 2944 m/s, the differential of these values could be interpreted as a fault with the southern relatively moving down from the northern, there is a little distance between this fault and the crude oil seepages, it is possible that the fracture that occurs by faults was creating secondary porosity in volcanic rock and becomes a migration route for the crude oil flows to the surface.

Estimation of antigorite wave velocities in subduction conditions based on first-principles thermoelasticity

The most abundant serpentine mineral in subduction settings, antigorite has one of the highest water storage capacities and is involved in seismicity. Seismic wave velocities of antigorite are important for detecting and quantifying serpentinization within the mantle wedge and the subducting oceanic plate. At present, the elastic properties of antigorite at high pressures and temperatures are unclear. In this study, we have investigated pressure-volume-temperature (P-V-T) data and thermodynamic properties of antigorite using first-principles molecular dynamics (FPMD) simulations. Using these simulations results, we computed the relevant thermoelastic parameters and estimated compressional and shear wave velocities (vP and vS) of antigorite in subduction conditions. A simplified velocity model of antigorite with its coexisting mantle anhydrous phases was introduced to help us understand the potential effect of serpentinization on the seismic velocity of mantle rocks. Combined with seismic observations, we re-evaluated some velocity anomalies within forearc mantle wedges and established reliable serpentinization budgets. These results can provide preliminary evaluations and reliable constraints on serpentinization and water content in mantle rocks, which has important implications for understanding global plate dynamics and the deep water cycle.

Compressional and shear wave velocities of Fe-bearing silicate post-perovskite in Earth’s lowermost mantle

The bridgmanite (Bgm) to silicate post-perovskite (PPv) phase transition is believed to be the main cause for the distinct seismic features observed in the D'' layer, the lowermost region of the Earth’s mantle. However, the transition depth and elasticity of the PPv phase have been highly debated, as the chemical complexity within the D'' layer can largely affect the Bgm-PPv transition pressure and the associated velocity contrast. Experimental measurements of sound velocities for PPv with different chemical compositions under relevant lowermost-mantle conditions are essential but remain limited. In this study, we have reliably measured both compressional wave velocity (VP), shear wave velocity (VS), and density, for two Fe-bearing PPv compositions [(Mg0.85Fe0.15)SiO3 and (Mg0.75Fe0.25)SiO3] at lowermost mantle pressures using Impulsive Stimulated Light Scattering (ISS), Brillouin Light Scattering (BLS), and X-ray Diffraction (XRD) in diamond anvil cells. Our results indicate that the velocities of Fe-bearing PPv at 120 GPa can be described by the following relationships: VS (km/s) = 7.65–2.8XFe and VP (km/s) = 14.11–3.8XFe, where XFe represents mole fraction of the Fe content. The variations in the Fe content of PPv may provide one of the explanations for the seismic lateral variations observed at the Earth’s core mantle boundary. By comparing our results with the high-pressure velocities of Bgm, our velocity model suggests significant discontinuities across the Bgm-PPv transition, characterized by a reduction in both VP and VΦ, and an increase in VS. These findings highlight the importance of considering the influence of chemical composition, particularly Fe content which could vary significantly at the D'' region, on the seismic properties of the PPv phase. The observed velocity contrasts across the Bgm-PPv transition may contribute to the complex seismic signatures observed in the D'' layer, underscoring the potential role of this phase transition in interpreting the seismic features of the lowermost mantle region.

P-Wave Velocity Log Simulation for Petroelastic Inversion Using Gardner’s Equation, Neural Networks And Ensemble Methods Based on Trees: A Case Study of the Santos Basin, Brazil

One of the main tools for reservoir characterization is analyzing well-log data. The importance of such methods stems from petrophysical properties estimation, such as porosity, which is very important to the oil and gas industry. In scenarios where data is hard to collect, data loss and technical failures during the acquisition impose an extra challenge. Thus mathematical and petrophysical models are good candidates to fill information gaps in the well-log dataset. In such a way, the rock’s petroelastic and petrophysical properties can be successfully estimated. Several studies correlate the velocity of compressional waves (VP) to other basic well data. In this study, we used the Gardner equation and Machine Learning methods such as Neural Networks, Random Forest and Gradient Boosting regressions to generate VPlogs. We used real-world data acquired from twenty wells of the pre-salt formation from Santos Basin in Brazil to train and test the Machine Learning methods and evaluated the data estimated by those models using statistical metrics. We calculated the acoustic impedance from the estimated logs and used it to create a prior model for a petroelastic inversion, which allowed us to estimate the natural logarithm of the acoustic impedance for a seismic volume. The Machine Learning methods presented lesser errors between estimated and measured velocities when compared to Gardner’s equation.

Characterization of petrophysical and seismic properties for CO2 storage with sensitivity analysis

Saline aquifers are considered as highly favored reservoirs for CO2 sequestration due to their favorable properties. Understanding the impact of saline aquifer properties on the migration and distribution of CO2 plume is crucial. This study focuses on four key parameters—permeability, porosity, formation pressure, and temperature—to characterize the reservoir and analyse the petrophysical and elastic response of CO2. First, we performed reservoir simulations to simulate CO2 saturation, using multiple sets of these four parameters to examine their significance on CO2 saturation and the plume migration speed. Subsequently, the effect of these parameters on the elastic properties is tested using rock physics theory. We established a relationship of compressional wave velocity (Vp) and quality factor (Qp) with the four key parameters, and conducted a sensitivity analysis to test their sensitivity to Vp and Qp. Finally, we utilized visco-acoustic wave equation simulated time-lapse seismic data based on the computed Vp and Qp models, and analysed the impact of CO2 saturation changes on seismic data. As for the above numerical simulations and analysis, we conducted sensitivity analysis using both homogeneous and heterogeneous models. Consistent results are found between homogeneous and heterogeneous models. The permeability is the most sensitive parameter to the CO2 saturation, while porosity emerges as the primary factor affecting both Qp and Vp. Both Qp and Vp increase with the porosity, which contradicts the observations in gas reservoirs. The seismic simulations highlight significant variations in the seismic response to different parameters. We provided analysis for these observations, which serves as a valuable reference for comprehensive CO2 integrity analysis, time-lapse monitoring, injection planning and site selection.

Ballistic impact wave and bulge profile propagation characteristics and blunt injury assessment of UHMWPE laminate composite

Amid escalating localized conflicts worldwide, the elucidation of the mechanisms and quantification blunt trauma the emergency services and military personnel confronted becomes imperatively. However, the understanding of bullet-induced bulge formation on the laminate's back face, impact wave propagation, and consequent trauma assessment methods remains incomplete. This study applies 3-dimension digital image correlation (3D-DIC) method to reveals these phenomena by utilizing a lead-core bullet traveling at 334.93 m/s to impact an ultra-high molecular weight polyethylene (UHMWPE) laminate. The results show that the initial transverse propagation velocity of bulge profile is 24.79% smaller than the theoretical calculation, which indicates the reduction caused by cohesive matrix. The transverse wave demonstrates a double exponential attenuation pattern, which could be decomposed into two single exponential attenuation curves. The bulge profile shows hyperbolic pattern, which reveals the stability of the bulge shape and the temporal evolution pattern. Increasing the laminate-body gap leads to a decline in Blunt Criterion (BC) and the Abbreviated Injury Scale (AIS) values reduce from 2 to 0 correspondingly. The numerical model confirms the compression wave velocity as 1447.77 ± 123.10 m/s in the thickness direction and 9542.86 ± 3087.75 m/s in the fiber direction. These findings reveals and quantified the propagation patterns of back bulge and compressive wave, and thus could provide data-driven insights to improve body armor performance and blunt trauma assessment for individual soldiers.

Research on triaxial compressive mechanical properties and damage constructive model of post-thawing double-fractured quasi-sandstones with different angles

Joints and fractures lead to different failure mechanisms in rock masses under different environments. The mechanical properties and failure mechanisms of rocks with fissures are key problems in rock mass engineering. Parallel double-fracture quasi-sandstone specimens with different dip angles were prepared and subjected to triaxial compression tests after a single freeze-thaw cycle. Pore development, crack propagation, damage evolution, and failure characteristics were analysed. Combined with the strength distribution theory of microelements and the static elastic modulus theory, a damage constitutive model of double-fracture quasi-sandstone under freeze-thaw cycles and loads was established. This study explored the pore development, fracture propagation, damage evolution, and failure characteristics of fractured sandstone after thawing. The results showed that the compression wave velocity of the thawed specimens decreased, the nuclear magnetic resonance (NMR) T2 curve shifted to the right, and the frost heave force promoted the development of the internal porosity in the specimens. With an increase in the crack dip angle, peak stress, expansion stress, cohesion and internal friction angle, the specimen showed a ‘U’ shaped change trend, compression cracks, and rock bridge penetration rate after failure decreased, and mixed failure of tension and shear gradually changed into shear failure. When the dip angles were 30° and 60°, the double fractured quasi-sandstone had larger total damage and more obvious brittle failure characteristics.

A Comparison of Three Geophysical Methods for Determining the Depth to Bedrock

The location of the study site for the presented research here is at Illinois in USA. Geophysical investigations were conducted to characterize the subsurface. At the site, borehole control, downhole seismic (DHS), seismic refraction tomography (SRT) and multichannel analysis of surface waves (MASW) data were acquired for the purpose of seismic site characterization. Shear wave and compressional wave velocities were used to estimate depth to bedrock. The data analyses showed the depth to bedrock determined by DHS, MASW and SRT is in good agreement with borehole data.

The Biomechanics of Musculoskeletal Tissues during Activities of Daily Living: Dynamic Assessment Using Quantitative Transmission-Mode Ultrasound Techniques.

The measurement of musculoskeletal tissue properties and loading patterns during physical activity is important for understanding the adaptation mechanisms of tissues such as bone, tendon, and muscle tissues, particularly with injury and repair. Although the properties and loading of these connective tissues have been quantified using direct measurement techniques, these methods are highly invasive and often prevent or interfere with normal activity patterns. Indirect biomechanical methods, such as estimates based on electromyography, ultrasound, and inverse dynamics, are used more widely but are known to yield different parameter values than direct measurements. Through a series of literature searches of electronic databases, including Pubmed, Embase, Web of Science, and IEEE Explore, this paper reviews current methods used for the in vivo measurement of human musculoskeletal tissue and describes the operating principals, application, and emerging research findings gained from the use of quantitative transmission-mode ultrasound measurement techniques to non-invasively characterize human bone, tendon, and muscle properties at rest and during activities of daily living. In contrast to standard ultrasound imaging approaches, these techniques assess the interaction between ultrasound compression waves and connective tissues to provide quantifiable parameters associated with the structure, instantaneous elastic modulus, and density of tissues. By taking advantage of the physical relationship between the axial velocity of ultrasound compression waves and the instantaneous modulus of the propagation material, these techniques can also be used to estimate the in vivo loading environment of relatively superficial soft connective tissues during sports and activities of daily living. This paper highlights key findings from clinical studies in which quantitative transmission-mode ultrasound has been used to measure the properties and loading of bone, tendon, and muscle tissue during common physical activities in healthy and pathological populations.

Przewidywanie dyfuzyjności termicznej na podstawie prędkości P-fal i oceny porowitości dla zbiorników piaskowca

Petrophysical heterogeneities of sandstone reservoirs which are generated by rock internal variability resounded to the magnitude of the rock thermal diffusivity. This is expected mostly to variation of rock density, porosity, reservoir temperature and its thermal conductivity. New methodology for calculating thermal diffusivity in a sandstone rock formation is intended and effectively employed some laboratory thermophysical measurements for sandstone reservoirs. The proposed petrophysical model establishes thermal diffusivity if both the effective porosity and acoustic (compressional) wave velocity of the rock are known. Some reliable petrophysical models (El Sayed, 2011 and Ahmed, 2019) concerned to both the Baharyia (Egypt) and Szolnok (Hungary) sandstone formations are used with only some modifications to build an innovative nomography. It permitted precise quantification and determination of the thermal diffusivity for both dry and saturated sandstone samples normalized to reservoir temperature (300K-1060 K). Verification of the proposed model is achieved with applying study cases of laboratory measured thermophysical properties (i.e., porosity, thermal diffusivity/or conductivity and longitudinal wave velocity) for different sandstone types, geological ages and geographic locations. A regression analysis of thermal diffusivity between laboratory measured and predicted data for dry (Ҡ-dry) rock samples yield a plausible coefficient of correlations as (R =0.73; 0.86 and 0.98) for three different sandstones obtained from Permo-Carboniferous in Germany (Aretz, 2016) and of dissimilar geologic age in Switzerland (Pimienta, 2018) respectively while, the average standard error equals 0.011. Then again, the laboratory measured and predicted thermal diffusivity (Ҡ-sat) of saturated samples display an appropriate coefficient of correlation (R = 0.76) and average standard error (0.0089).

Prędkość ultradźwiękowa analizy wpływu zawartości wody na kredę fosfatyczną

Water content may have a crucial effect on the petrophysical and mechanical properties of high porosity rocks such as chalk. Consequently, the water fluctuations may significantly influence the behaviour of engineering structures that are constructed in chalks. It is the aim of this study to illustrate how the elastic and strength properties of chalk can evolve considering water content variation using P-wave velocity and analytical analyses. To study the influence of water on the ultrasonic compressional wave velocity (VP), measurements of travel time of three samples subjected to different water content were performed. The Uniaxial Compressive Strength (UCS) in dry and saturated chalk specimens was also estimated. To clarify the possible influence of lithology on the physicomechanical properties of rocks, three samples of phosphatic chalk were studied in thin sections. Automated mineral analysis and textural imaging of the samples were performed using an FEI QEMSCAN®. The analyses of the Ultrasonic tests reveal that changes in the water content are associated with variations in the P-wave velocity. Based on P-wave velocity changes as a function of water content, analytical models have been used to predict the elastic and strength properties. The data indicate that the presence of water significantly reduced the elastic and strength parameters of the chalk, a result that is in agreement with the UCS laboratory tests. The derived equations can be used for predicting the elastic and strength properties of high-porous chalk from the P-wave velocity as a function of water content. This approach may avoid the necessity for time-consuming laboratory testing.