Petro-elastic Model Research Articles

Petro-elastic model of the multiple pore-crack structure of carbonate rocks based on digital cores

Petro-elastic model of the multiple pore-crack structure of carbonate rocks based on digital cores

Petroelastic modeling of Vereiskian and Bashkirian deposits on example of an oil field in the Republic of Tatarstan

Petroelastic modeling of Vereiskian and Bashkirian deposits on example of an oil field in the Republic of Tatarstan

Petroelastic Models for Santos Basin Pre-Salt Carbonates

Petroelastic Models for Santos Basin Pre-Salt Carbonates

Reservoir characterization in coastal-continental environment of the Jurassic complex based on seismic data inversion

A modern seismic interpretation of coastal-continental deposits in the Jurassic complex of one of the fields in the north of the West Siberian Oil and Gas Province, including simultaneous stochastic inversion of seismic data was performed. The main object of the study was the interval of the group of J2 reservoirs. Petro-elastic modeling was carried out on the project. Modelling includes six wells with geophysical well logs. Feasibility study shows possibility of predicting lithology by stochastic inversion. In addition, optimal parametrization of elastic properties was determined to separate water-saturated and gas-saturated reservoirs from clay deposits. Interpretation of well and seismic data, as well as the results of stochastic inversion, made it possible to identify play zones in target interval. Probability cubes of gas-saturated reservoirs, made it possible to map the lenticular structure of the reservoirs and substantiate hydrodynamic barriers between individual gas plays.

Refinement of the geological model of Jurassic deposits accounting the results of stochastic inversion and facies modeling

The paper presents the results of a comprehensive interpretation showing the effective way to integrate seismic data into a three-dimensional geological model. Stochastic inversion was used to increase the reliability of forecasts of productive thicknesses. A comprehensive interpretation of the geological and geophysical information of the Yu21 formation deposits of the Malyshevskaya formation was carried out, including sedimentological analysis of core data, petroelastic modeling of well logging curves for the purposes of stochastic inversion and stochastic inversion of seismic data. An areal forecast of sedimentation environments (facies) was carried out. The resulting three-dimensional geological model, in more detail, compared to the model without taking into account the spatial seismic forecast, emphasizes the heterogeneity of the distribution of properties in the geological environment, which is especially important when planning production drilling with horizontal wells.

Time‐lapse VSP integration and calibration of subsurface stress field utilizing machine learning approaches: A case study of the morrow B formation, FWU

AbstractThis study aims to develop a methodology for calibrating subsurface stress changes through time‐lapse vertical seismic profiling (VSP) integration. The selected study site is a region around the injector well located within Farnsworth field unit (FWU), where there is an ongoing CO2‐enhanced oil recovery (EOR) operation. In our study, a site‐specific rock physics model was created from extensive geological, geophysical, and geomechanical characterization through 3D seismic data, well logs, and core assessed as part of the 1D MEM conducted on the characterization well within the study area. The Biot‐Gassmann workflow was utilized to combine the rock physics and reservoir simulation outputs to determine the seismic velocity change due to fluid substitution. Modeled seismic velocities attributed to mean effective stress were determined from the geomechanical simulation outputs, and the stress‐velocity relationship developed from ultrasonic seismic velocity measurements. A machine learning‐assisted workflow comprised of an artificial neural network and a particle swarm optimizer (PSO) was utilized to minimize a penalty function created between the modeled seismic velocities and the observed time‐lapse VSP dataset. The successful execution of this workflow has affirmed the suitability of acoustic time‐lapse measurements for 4D‐VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

An Integrated Workflow for the Probabilistic Estimation of Pressure and Saturation Changes from 4D Seismic Data: Application to the Catcher Fields, Central North Sea

We present 4D seismic inversion to reservoir pressure and saturation changes applied to data from the Catcher fields. The inversion workflow integrates data from reservoir simulation, well logs and production volumes, time-lapse time shifts and angle-stacked 4D seismic amplitudes as well as machine learning and Bayesian inversion methods. It begins with a petro-elastic model and reservoir pressure sensitivity calibration step, using well log data and time-lapse time-shifts. A machine learning inversion is then used to create an initial estimate of the reservoir property changes. This estimate is then used as prior information, in conjunction with reservoir simulation pressure data, for a stochastic Bayesian inversion workflow. We show that the Bayesian inversion benefits from the use of machine learning prior, leading to improvements in the match to the observed 4D seismic signal as well as the injected and produced water volumes. In addition to a most probable solution, stochastic sampling of the Bayesian posterior distribution also produces uncertainty estimates which are valuable in such inversion problems. With this result, we extract multiple equiprobable realisations and define conditional bounds for the pressure and saturation changes, which we recognise as helpful for reservoir management and 4D seismic data assimilation into reservoir simulation models.

Carbonate Pore Shape Evaluation Using Digital Image Analysis, Tomography, and Effective Medium Theory

Carbonate rocks have a wide variety of pore shapes and different types of grains, which greatly affect the elastic properties and characteristics of the reservoir. This causes certain difficulties in petroelastic modeling. One of the problems is the scale of the input data, which is then used to build the rock physics model. The paper presents the results of studying three core samples of carbonate rocks of the Upper Devonian and Lower Carboniferous age, which are located in the South Tatar arch (Volga-Ural oil and gas basin (Russia)). To evaluate the structural characteristics of the pore space, the effective medium theory is used. The input data are the results of laboratory studies that include measurements of the velocities of longitudinal and transverse waves, porosity, and thin section and computed tomography analysis. When using the computed tomography, the core samples are analyzed at different resolution (12–37 µm/voxel). The tomography studies of pore space at different scales provide rather different values of porosity and pore aspect ratio. The tomography-based porosity estimations also differ from the experimentally measured porosity (up to 10%). The pore space characteristics provided by different datasets are used to build a rock physics model for the studied rocks that helps to estimate the elastic wave velocities with three different methods of effective medium theory (self-consistent approximation, differential effective medium (DEM), and the Kuster–Toksöz method). A comparison of the velocity estimations with their experimental analogs for dry rocks may indicate the presence of microcracks whose size is beyond the tomography resolution. Improved rock physics models incorporating both pores and microcracks are then used to predict the elastic wave velocities of fluid-saturated rock in a wide porosity range. It is demonstrated that the predicted values significantly differ (up to 30%) from those provided by the rock physics (RP) models constructed without the support of the tomography results. Moreover, other types of models are considered in which the difference in experimental and theoretical velocities is attributed to changes in the host matrix properties as compared to the calcite polycrystal, which are caused by various reasons.

All-in-one proxy to replace 4D seismic forward modeling with machine learning algorithms

Time-lapse seismic data provides valuable information to assist reservoir monitoring, model-based reservoir management, and development activities. 4D seismic information can be used quantitatively to estimate saturation-pressure changes or update reservoir simulation models through a data assimilation process, which traditionally requires a 4D seismic forward model that includes two steps: (1) petro-elastic model to relate rock and fluid properties to elastic attributes and (2) seismic model to depict wave propagation in the reservoir. The traditional forward model brings some complications in quantitative applications, such as time-lapse seismic history matching. Its multidisciplinary nature is a significant bottleneck to update simulation models, once observed 4D seismic data is used with production data in the assimilation process. Furthermore, it is time-consuming, especially in three-dimensional simulation models within data assimilation with several iterations. Here, the traditional forward model needs to be run hundreds or even thousands of times. Aside from being time-consuming, it requires substantial computer memory for complex geology, where more sophisticated seismic models are needed. In 4D seismic quantitative applications, the timely use of the information and its full benefits fade away due to the computational cost and time of the 4D seismic forward model. This research proposes a proxy (we call it, S4D-Proxy) to replace the traditional approach. The S4D-Proxy leverages machine learning models to detect hidden patterns and learn relations between input features (e.g., porosity and saturation-pressure changes) and the target (time-lapse difference of seismic amplitude). Training data are prepared and fed to the machine learning algorithms to relate the inputs and the desired output. The performances of the machine learning models are evaluated based on a numerical measure (coefficient of determination) and visual comparison of the results with the traditional forward model. Our results show that the average coefficient of determination for the test dataset is in an acceptable range. Moreover, the visual comparison of the proxy predictions with those from the traditional approach shows high similarity. Most hardening and softening 4D signals are reproduced with the proxy models. The main advantage of our approach is that it lowers the computational cost and time. The application of the proxy in data assimilation process could offer a significant speed advantage with faster 4D seismic forward modeling for data assimilation iterations. Unlike the traditional forward model, which is one step (petro-elastic model) then another (seismic model), our approach performs the forward modeling at once (all-in-one proxy model). It also offers an alternative 4D seismic forward model for time-lapse seismic feasibility study. All these advantages make the S4D-Proxy valuable and practical in permanent reservoir monitoring (PRM), where the reduced turnaround time in the forward model indicates the timely use of the 4D seismic information.

Quantifying thin heterogeneous gas sand facies of Rehmat gas field by developing petro elastic relationship in fine stratigraphic layers through bayesian stochastic seismic inversion

The Lower Goru Formation (LGF) sand intervals are prolific producers in the Central Indus Basin (CIB), Pakistan. The B-Interval of LGF is a proven reservoir of the Rehmat gas field, having heterogeneous and thin sand layers (14–17 m) below seismic tuning thickness. To quantify these potential layers, a petro-elastic relationship is developed using seismic and well data. Well data although having high resolution, lacks in many aspects, such as poor density logs due to bad boreholes along with the missing shear sonic (Vs), adding more challenges to characterize a complex reservoir. Therefore, an extended cemented sandstone model is established by incorporating the petro-elastic models (PEMs) of the reservoir's litho-fluid facies in the rock physics modeling (RPM) framework. RPM compensated for the lithological variability, quartz cementation impact, missing and bad density logs. The missing low and high frequencies of band-limited seismic data are retrieved through constraining wells by geostatistical parameters for enhanced visualization of thin-bedded heterogeneous sand. Bayesian stochastic seismic inversion (BSSI) addressed reservoir challenges by incorporating the PEMs responses along with consigning the frequencies limitation, through a low-frequency model and variograms from the sparse well data for high frequencies. For optimized reservoir characterization, a 3D seismic dataset is inverted in a fine-scale stratigraphic grid of 0.1 ms interval yielding high-resolution elastic properties. These enhanced elastic attributes are integrated with petrophysical relationships simulated through probability density functions that permit elastic attributes to be translated into reservoir characteristics (geological facies). So, an integrated technique of rock physics modeled logs with BSSI successfully identified thin heterogeneous gas sands through several high-resolution realizations with associated uncertainty.

4D petroelastic model calibration using time-lapse seismic signal

In the last two decades, 4D seismic monitoring has become a widely used technique for oil and gas field production. Modeling studies are a standard for defining reservoir monitoring plans, optimizing survey design, and justifying the expense of data acquisition. Discrepancies between 4D seismic data and synthetic results can be analyzed through petroelastic modeling of reservoir simulations. However, assuming that a history match is available and that the reservoir model and fluid-flow simulation results can be trusted, characterization of pressure and fluid changes in the field remain challenging. A workflow is proposed to adjust the 4D petroelastic model (PEM) to better fit 4D seismic attributes with the dynamic behavior of the reservoir. The input data for 4D inversion consist of multiple broadband 4D-compliant processed base and monitor surveys recorded in a highly depleted clastic field offshore Africa. The broadband inversion results greatly reduce the background noise level, enhance the signal-to-noise ratio, and improve the definition of 4D signals. Due to various production effects all over the field, a new global calibration workflow to speed up the 4D petroelastic model adjustment is proposed. The combination of good 4D seismic inversions and a well-calibrated PEM is expected to have a significant impact on the reservoir monitoring. During the calibration process, reservoir model discrepancies with 4D seismic attributes can be identified, suggesting some updates of the reservoir model. In addition, when further monitors are considered, the calibrated 4D PEM provides more reliable predictability.

Quantitative interpretation of time‐lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time‐lapse velocity responses

AbstractThis study investigates the contribution of fluid saturation variation to the time‐lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time‐lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO2–water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site‐specific petro‐elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time‐lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time‐lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO2‐WAG projects. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

Petroelastic modeling of carbonate reservoirs in the view of different void types

Background. Fractures present in carbonate rocks facilitate fluid flow within void spaces. Each type of voids can have a different effect on rock elastic properties, which, in turn, allow the characteristics of voids to be estimated by assessing the velocity of elastic waves. In this paper, an effect of pores and fractures on the velocity of elastic waves is analyzed by petroelastic modeling. An example of solving an inverse problem is presented, involving the determination of fracture porosity and relative fracture opening using the data on elastic wave velocity and hydrocarbon reservoir density obtained during geophysical well logging.Aim. To increase the accuracy of experimental data on the velocity of elastic waves that propagate across hydrocarbon carbonate reservoirs by assessing the pore and fracture characteristics of voids.Materials and methods. The effective medium theory was used as an efficient way to evaluate physical, including elastic, properties based on rock composition and microstructure. Using Berryman’s self-consistent method, we created a model of rock elastic properties, or a petroelastic model, the void space in which was represented by isometric pores and fractures. This model was applied to a well in a petroleum province in West Siberia.Results. The effect of characteristics determining the void space of porous-fractured rocks on the velocity of elastic waves was defined. Velocity surfaces were constructed in accordance with changing parameters of the model, i.e. fracture porosity and fracture aspect ratio. The obtained theoretical and experimental data were compared in order to determine fracture development zones and to evaluate volume concentration and relative fracture opening in these zones.Conclusion. The effect of pores and fractures on elastic wave velocity in porous-fractured carbonate reservoirs was studied by petroelastic modeling. The zones of excessive fracturing were defined and the parameters of volume concentration and relative fracture opening were determined using the data obtained by geophysical well logging in a petroleum province in West Siberia.

Rock Acoustics of Diagenesis and Cementation

We simulate the effects of diagenesis, cementation and compaction on the elastic properties of shales and sandstones with four different petro-elastic theories and a basin-evolution model, based on constant heating and sedimentation rates. We consider shales composed of clay minerals, mainly smectite and illite, depending on the burial depth, and the pore space is assumed to be saturated with water at hydrostatic conditions. Diagenesis in shale (smectite/illite transformation here) as a function of depth is described by a fifth-order kinetic equation, based on an Arrhenius reaction rate. On the other hand, quartz cementation in sandstones is based on a model that estimates the volume of precipitated quartz cement and the resulting porosity loss from the temperature history, using an equation relating the precipitation rate to temperature. Effective pressure effects (additional compaction) are accounted for using the Athy equation and the Hertz–Mindlin model. The petro-elastic models yield similar seismic velocities, despite the different levels of complexity and physics approaches, with increasing density and seismic velocities as a function of depth. The methodology provides a simple procedure to obtain the velocity of shales and sandstones versus temperature and pressure due to the diagenesis-cementation-compaction process.

Substituting petro-elastic model with a new proxy to assimilate time-lapse seismic data considering model errors

Dynamic data from oil and gas reservoirs (such as well-production or 4D seismic data) have been used often to reduce uncertainty in reservoir simulation models. These data are assimilated in the simulation models separately or jointly. Assimilation of 4D seismic data conventionally involves with a petro-elastic model (PEM) to transform outputs from the simulation models to elastic properties. The PEM is a set of different equations with uncertain parameters and its inclusion in assimilation algorithms calls on multidisciplinary teams of geoscientists and engineers. Moreover, PEM requires extraction of different outputs from the simulation models for the seismic forward model calculations. The extraction process can be costly for large-scale simulation models of giant reservoirs. This research presents a new petro-elastic proxy model (named DAI-Proxy) with a novel formulation to substitute the PEM and integrate 4D seismic data. DAI-Proxy relates time-lapse acoustic impedance to a summation of saturation and pressure changes with two coefficients which are functions of porosity. As the proxy is an approximation of the PEM, its application is affected by model error. We introduce two approaches to account for the proxy model error: (1) considering uncertain coefficients for the DAI-Proxy and (2) using fixed coefficients in the proxy while estimating model errors statistics from the prior ensemble of models. We incorporate these two approaches with a data assimilation algorithm to assimilate simultaneously 4D seismic and well-production data. A benchmark case is used with different cases of data assimilation to compare the DAI-Proxy and the PEM applications. Results show that data match quality for 4D seismic and well-production have similar responses for the PEM and DAI-Proxy implementations. In terms of production forecast, using fixed coefficients in the proxy with its model error treatment create a data assimilation framework comparable to the PEM case. Our results indicate that the traditional PEM application to integrate jointly 4D seismic and well-production data can be replaced with our new DAI-Proxy application. Given the degree of uncertainty in the PEM, related to the rock and fluid models, our proxy provides similar results with fewer uncertain inputs. The proxy offers further advantage as it needs less outputs from the simulation models for seismic forward model calculations. In addition, it helps petroleum engineers to use a computationally less expensive model (light model) as a substitute for the PEM to assimilate 4D seismic data.

Enhancing vertical resolution with 4D seismic inversion

In this work, we applied a Bayesian time-lapse (4D) seismic inversion to a deep-water heavy oil field located in the Campos Basin (Brazil). In this procedure, the differences of seismic amplitudes of baseline and repeated surveys are the input to calculate the changes in acoustic impedances. To ensure the quality of the results, the parametrization of the inversion was performed by analyzing the anomalies resolution and signal-to-noise ratio with different values of input parameters, and the estimates are crosschecked with the results of a petroelastic modeling applied to the dynamic model. Results show a relevant improvement in the vertical resolution of estimated acoustic impedances in comparison to the amplitude data in a thin reservoir (with thickness ranging from 15 to 45 m). This improvement was possible because of the high-quality of acquired seismic amplitudes (permanent reservoir monitoring) and because of the data variability that is controlled in the Bayesian 4D inversion algorithm. The high resolution of Bayesian inverted results enabled a more detailed vertical zoning of the reservoir, revealing important geological features, such as channels in the main interval and below the reservoir's base, and improved the understanding of the fluid movement over time, enabling the analysis of the injection efficiency and by-passed oil zones as well as the aquifer movement. This information is important to update the static model, by improving the definition of geological features, and to calibrate dynamic models, in the future, by adding the inverted acoustic impedance maps into the data assimilation procedure.

Gas-bearing sands appraisal for Zamzama Gas field in Pakistan through inverted elastic attributes assisted with PNN approximation of petrophysical properties

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited by water encroachment in producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on a relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), are calculated from the simultaneous prestack seismic inversion, allowing the identification of gas sands in the field. Furthermore, inverted elastic attributes and well-based lithologies are incorporated into the Bayesian framework to evaluate the probability of gas sands. To better determine reservoir quality, bulk volumes of PHIE and clay are estimated using elastic volumes trained on well logs employing Probabilistic Neural Networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas-sands passing through existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess litho-facies and achieve maximum production with minimal risk.

Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study

Abstract Rock physics provides a dynamic tool for quantitative analysis by developing the basic relationship between fluid, lithological, and depositional environment of the reservoir. The elastic attributes such as impedance, density, velocity, V p/V s ratio, Mu-rho, and Lambda-rho are crucial parameters to characterize reservoir and non-reservoir facies. Rock physics modelling assists like a bridge to link the elastic properties to petrophysical properties such as porosity, facies distribution, fluid saturation, and clay/shale volume. A robust petro-elastic relationship obtained from rock physics models leads to more precise discrimination of pay and non-pay facies in the sand intervals of the study area. The Paleocene aged Lower Ranikot Formation and Pab sandstone of Cretaceous age are proven reservoirs of the Mehar gas field, Lower Indus Basin. These sands are widely distributed in the southwestern part of the basin and are enormously heterogeneous, which makes it difficult to distinguish facies and fluid content in the reservoir intervals. So, an attempt is made in this paper to separate the reservoir facies from non-reservoir facies by using an integrated approach of the petro-elastic domain in the targeted sand intervals. Furthermore, missing logs (S-sonic and P-sonic) were also synthesized in the wells and missing intervals along with improving the poor quality of the density log by captivating the washouts and other side effects. The calibrated rock physics model shows good consistency between measured and modelled logs. Petro-elastic models were predicted initially using petrophysical properties and incorporated at true reservoir conditions/parameters. Lithofacies were defined based on petrophysical cut-offs. Rock physics modelled elastic properties (Lambda-rho versus Mu-rho, impedance versus V p/V s ratio) were then cross-plotted by keeping lithofacies in the Z-axis. The cross-plots clearly separated and demarcated the litho-fluid classes (wet sand, gas sand, shale, and limestone) with specific orientation/patterns which were randomized in conventional petrophysical analysis.

Assimilating time-lapse seismic data in the presence of significant spatially correlated model errors

Time-lapse seismic data is becoming a common information source in reservoir model calibration workflows to improve production forecasts. The standard process compares a forward model's results with the observed data to update the model's parameters from the existing deviations. Ensemble-based methods are popular choices for this process. However, the so-called forward model is always a simplification of the real phenomena. These simplifications may significatively influence the relation between simulated and observed data and possibly yield inconsistent updates of the parameters and uncertainty underestimation. In the conventional approach for this problem, the so-called strong-constraint formulation neglects the model's limitations, causing unphysical updates of the parameters to reduce the distance between simulated and observed data. In this work, we propose a methodology to apply a weak-constraint formulation to the time-lapse seismic data assimilation to mitigate the above problem. We consider the forward model error with an additive term and update it during the data assimilation workflow. By adopting this approach, we reduce the impact of the model errors in the calibrated parameters. Also, the proposed methodology handles model bias as a type of general model error. The inclusion of the additive term weakens the updates of the model due to the time-lapse seismic data. We show that this procedure significantly benefits the data assimilation results when there are substantial spatially correlated model errors, and it has a minor impact when applied to a low-error case. We apply the proposed method to assimilate time-lapse seismic data using the Ensemble Smoother with Multiple Data Assimilations in a simple 2D case and in a realistic benchmark synthetic case based on a real offshore reservoir. In the former case, we consider model error related to the pressure sensitivity in the petroelastic model. In the latter, we consider a realistic synthetic time-lapse seismic and the correlated errors result from seismic modeling and inversion. Moreover, in the latter, we also assimilate well data. The results indicate that our methodology improved the reservoir characterization and the production forecast using relatively low-resolution time-lapse seismic data.

Catalog of Coefficients for Estimating Bulk and Shear Moduli as a Function of Lithology

The purpose of this paper is to present correlation coefficients for a variety of rock types that can be used in a suitable petroelastic model (PEM). The correlation coefficients for different rock types facilitate the application of a petroelastic model in reservoir flow models. By combining the correlation coefficients and the PEM, it is possible to obtain low-cost estimates of reservoir geophysical attributes. The rock types include dolomite, limestone, high porosity sandstone, poorly consolidated sandstone, tight gas sandstone, and well consolidated Gulf Coast sandstone.