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

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

  • Urooj Shakir EMAIL logo , Aamir Ali , Muhammad Raiees Amjad und Muyyassar Hussain
Veröffentlicht/Copyright: 2. Dezember 2021
Artikel downloaden (PDF)
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Open Geosciences
Aus der Zeitschrift Open Geosciences Band 13 Heft 1

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.

Keywords: petrophysics; rock physics; cross-plots; Lambda-rho versus Mu-rho; impedance versus V p/V s ratio

1 Introduction

Exploration is the crucial step in the production planning of any petroleum industry which is considered to be an expensive, challenging, and time-consuming job. Estimation of probable facies along with other elastic rock properties is sufficiently required to reduce the risk and enhance the reliable data acquisition in any petroleum reservoir scenario [1,2,3]. Assessment of rock attributes such as lithology, texture, fluid saturation, porosity, and permeability is more challenging in clastic rocks due to their heterogeneous character [4,5]. The initial phase is to determine the best suitable parameters defining the complicated behavior of rocks. The heterogeneity comes due to primary and secondary minerals, pore texture, fluid type, porosity, pressure/temperature variations, and different formation processes [6]. These parameters mainly control the elastic properties which are being interpreted using a seismic data set. The conventional seismic interpretation provides a qualitative understanding of the geological structure and stratigraphic patterns with variations in density which is primarily controlled by velocity [7].

In the real world, geophysical observations can be predicted or interpreted in terms of rock properties by building a model of the rock that is consistent with the known data [8,9]. So, there is a need to find a potential model that links the geological properties and geophysical observations. For this, rock physics provides a bridge between well log and seismic to decipher the rock properties in elastic domain [10].

Rock physics is an interdisciplinary glue that plays a vital role and holds all the geoscience domains to fill the gaps present in each discipline and extract the core amount of rock information to draw a high-resolution picture of the reservoir [2,11]. This approach utilizes the concept of elastic properties measured from seismic data to predict rock attributes yielding the information about the reservoir rocks [12,13]. It also helps to quantify the sensitive reservoir properties of not only seismic data (e.g., velocity and seismic reflection amplitude), but also well log data of critical reservoir properties like porosity, lithology, pore fluid type, saturation, and pore pressure which are more vital for the quantitative analysis of a reservoir [14,15].

As well logs generally have temporal (vertical) resolution, quality of log data is more consistent and accurate at in situ so conditions and matters a lot to infer the true reservoir properties [16]. Petrophysical analysis of the zone of interest provides an insight to discriminate the fluid saturated and non-saturated reservoir intervals [17]. However, multiple factors such as missing log data, bad borehole conditions, mud cake, washouts, temperature, and salinity usually can affect the quality of log data leading to inaccurate petrophysical interpretation. Moreover, analysis of individual wells is reliable and consistent with the petrophysical interpretation of a particular zone, and hence, lacks in providing the interpolation of results between the wells in petro-elastic domain [18]. Here, the integrated petrophysics and rock physics modelling help to gain confidence in the quality of well log curves by comparing the difference between original (measured) and predicted (modelled) curves along with distinguishing the reservoir facies from the non-reservoir facies [19,20].

Rock physics modelling provides more accurate and reliable connection between seismic, petrophysics, and elastic reservoir properties [21,22]. Various authors have proposed a large number of rock physics models based on the particular mineral and reservoir conditions [23]. These models have been classified into different groups such as contact models, inclusion models, transformations, bounds, and computational models. The common log data required for these models are measured P-sonic (V p), S-sonic (V s), and density acquired during wireline logging [24,25]. As P-sonic and density are routinely acquired in wells as compared to S-sonic, main focus is given to shear sonic which is of pivotal importance in the success of these rock physics models. These logs (V p, V s and density) are either acquired from wireline logging or computed (V s) from Greenburg and Castagna’s equation during rock physics process. All three priorly stated logs are used to compute the bulk and shear moduli of rock, which further assists in estimating the fluid properties [26].

The quality of modelled shear sonic is controlled by comparing it with the existing models such as ref. [27] and theoretical ones such as ref. [28], or hybrid models such as [25,29,30,31]. Along with the other parameters, Mu-rho and Lambda-rho provide the additional information of the facies and fluid type because these are very sensitive to fluid prediction and categorization [19,32]. Once consistent rock physics modelled logs are available, petrophysical and elastic properties (V p, V s, Lambda-rho, Mu-rho) can not only be linked in a spurious way, but their output will also provide a robust way for reservoir properties estimation determined through seismic inversion [33].

The sands of Pab and Ranikot formations are proven targets in the Mehar gas field chosen as a study area in this research paper. These sand intervals are composed of quartz, clay, feldspar, and a minor amount of calcite [37]. Through well log and core data, it has been deduced that the mineral composition of these sands is very much heterogeneous from one well to another, creating difficulty in using a single petrophysical model for reservoir characterization [8,9].

This paper seeks to demonstrate the rock physics application to model the missing shear sonic and density log to enhance the relationship between petro-elastic properties and to demarcate the reservoir facies in an optimized way that will help in the prediction of lithology and fluid distribution over the entire volume. To accomplish this task, an integrated approach of petrophysics and rock physics modelling is adopted. Petrophysical analysis has been utilized to identify the reservoir intervals in the targeted formations, while rock physics helped out in prediction of missing log data and further to segregate the facies as output model based on P-sonic, S-sonic, Lambda-rho, and Mu-rho.

2 Geological information

Mehar gas field is located in south eastern part of the Kirthar Fold Belt (KFB) which is 150–200 km wide, north-south trending, east verging deformation zone [39,40]. It is bounded by Kirthar foredeep in the east, Chaman Fault in the west, Sibi trough in the north, and Indus delta in the south (see Figure 1). The Kirthar foredeep is considered as a major kitchen for the generation of hydrocarbons in the KFB [34]. Lower Indus Basin is rich in hydrocarbon and several oil and gas fields were explored in the KFB, being host by Foredeep zone [35]. Late Cretaceous Pab Sandstone and Early Paleocene Lower Ranikot Formation (see Figure 2) are the primary target reservoirs in the southern KFB and hence in the study area [36]. The uplifting of the emergent Indian continent in the Early Cretaceous age triggered the deposition of sediments in the passive margin setting [37]. These sands were deposited in these environments which are acting as main reservoirs. These deposits are mainly of deltaic and shoreface origin having dominant unconsolidated sandstone, mixture of shale with some stringers of limestone. Organic rich shales of Mughalkot Formation, upper Cretaceous age, are the main source rock in the Mehar area. These shales are thermally matured and consist of type-III kerogen, making them more favorable for gas generation [38]. Great variations are observed in the depositional trend of the Pab sandstone and Lower Ranikot Formation from south to north. Pab sandstone majorly consists of thick clean sandstone beds with small striations of clay, while Ranikot consists of two parts, i.e., Lower Ranikot and Upper Ranikot Formation. Upper Ranikot is predominantly shale, while Lower Ranikot is composed of mixed carbonate-siliciclastic lithofacies, which can further be divided into three units in the study area. The lower division is carbonate-dominant towards the north and siliciclastic towards the south, the middle division is dominated by offshore muds and shale. At the same time, the upper-division contains sand units coarsening upward towards the north-northeast and is offshore mud dominated towards the south-southwest [39,40].

Figure 1 Map of the study area (Mehar Block). Blue rectangle in the tectonic map shows the cultural boundary of Mehar E&P lease with three wells (black dots) Mehar-01 (north), Mehar-02 (middle), and Mehar-03 (south) from north to south.
Figure 1

Map of the study area (Mehar Block). Blue rectangle in the tectonic map shows the cultural boundary of Mehar E&P lease with three wells (black dots) Mehar-01 (north), Mehar-02 (middle), and Mehar-03 (south) from north to south.

Figure 2 Generalized Stratigraphical column of Kirther Fold and Thrust Belt showing the lithostratigraphic overview and petroleum elements (Hinsch et al., 2018).
Figure 2

Generalized Stratigraphical column of Kirther Fold and Thrust Belt showing the lithostratigraphic overview and petroleum elements (Hinsch et al., 2018).

3 Methodology

3.1 Data used

For this project, data of three wells Mehar-01, Mehar-02, and Mehar-03 are utilized. All three wells penetrated the targeted Paleocene and Cretaceous reservoirs. Well data include complete log suites of gamma ray (GR), spontaneous potential (SP), calliper (CALI), sonic logs (DT4P, DT4S), neutron porosity (NPHI), resistivity (MSFL, LLS and LLD), and density (RHOB) logs.

The adopted methodological scheme to carry out the work of petrophysics and rock physics is given below (see Figure 3).

Figure 3 Schematic workflow adopted to perform rock physics modelling. Starting from quality checking the logs and involving the petrophysics and rock physics model to predict missing logs after removing the artifacts. Facies are well-resolved in the petro-elastic domain.
Figure 3

Schematic workflow adopted to perform rock physics modelling. Starting from quality checking the logs and involving the petrophysics and rock physics model to predict missing logs after removing the artifacts. Facies are well-resolved in the petro-elastic domain.

3.2 Methodology

3.2.1 Petrophysics

Petrophysical interpretation provides insight for estimating the physical properties of the reservoir formations [41]. Formation evaluation and log facies are main parameters to link the rock properties with elastic (seismic) properties at the well locations [42]. In this study, detailed well log analysis is carried out using GVERSE petrophysics software to predict the reservoir characteristics utilizing the workflows presented by ref. [43] and [44,45]. The basic objective to perform well log interpretation is to identify the hydrocarbon-bearing sand intervals along with the porosity and fluid saturation [45]. More explicitly, the boundary of clean zones, estimation of petrophysical properties, and net payable thicknesses are calculated based on the criteria/formulae defined by the authors mentioned above.

Different logs with depth have been shown in the first four tracks like gamma ray, resistivity, and porosity along with the computed properties (porosity, lithology, and saturations) in the last four tracks from left to right. Correlation, resistivity, porosity, and track 4 have the input logs used for the petrophysical properties evaluation, while tracks 5, 6, 7, and 8 show the output properties computed using the equations (1)–(7) given below. Gamma ray log curve is used to differentiate between shaly and sandy lithology, while porosities were calculated using porosity (neutron and density) curves. Fluid saturation has been calculated by using resistivity curves. As Lower Ranikot Formation was having more shale content as compared to Pab Sandstone, water saturation was computed by applying both Archie’s and Indonesian equations. Archie’s equation includes the bound water and utilizes the resistivities for finding the water saturation, while the Indonesian equation removes the bound or irreducible water content which is associated with the clays and involves the volume of shale for water saturation along with the resistivity information. The petrophysical properties including volume of shale (equation (1)), average and effective porosities (equations (2)–(4)), saturation of water (equations (5) and (6)), and saturation of hydrocarbon (equation (7)) have been calculated for the three wells at the desired targeted depths by using the formulae taken from [46,47].

Volume of shale was calculated by using the formula given in equation (1)

(1) V shl = ( GR log –  GR min ) / ( GR max –  GR min ) ,

where, V shl is volume of shale; GRlog is gamma ray reading of formation; GRmin is minimum gamma ray (clean sand); and GRmax is maximum gamma ray (shale).

While for density porosity, formula given in equation (2) was utilized

(2) φ D = ( ρ m − ρ b ) / ( ρ m − ρ f ) ,

where, ρ m is density of matrix; ρ f is density of fluid; ρ b is log response in the zone of interest

Total porosity was calculated using equation (3)

(3) Φ T = ( Φ D + Φ N ) / 2 ,

where, ΦT is total porosity; ΦD is density porosity; ΦN is neutron porosity

Effective porosity was computed using the formula given below in equation (4)

(4) Φ E = Φ T ( 1 − V sh ) ,

where, ΦE is effective porosity; ΦT is total porosity; V sh is volume of shale

The formula used for calculating water saturation using Indonesian equation (5):

(5) S w Indonesia  = 1 R t V sh ( 1 − 0.5 V sh ) R sh + ϕ e m a ⋅ R w ( 2 / n ) ,

where, S w is water saturation (Indonesian); R t is deep resistively; R w is the resistivity of the formation water; V sh is volume of shale; R sh is resistivity of shale; Φe is effective porosity; n is the saturation exponent; and m is the cementation exponent

Water saturation using Archie’s equation (6) is given by

(6) S w = F × R w R t 1 n ,

where, R w is water resistivity; R t is true resistivity; n is saturation exponent and with value 2; F is formation factor ( a φ m ) ; a is tortuosity factor with value; φ is effective porosity; m is cementation factor with a constant value 2. All the factors of Archie’s equation are calculated from well header information. After calculating water saturation, saturation of hydrocarbon was computed using formula given below because sum of the two saturations is 100%, so following equation (7) formula gives the hydrocarbon saturation

(7) S h = 1 − S w ,

where, S h is saturation of hydrocarbon; S w is saturation of water.

3.2.2 Rock physics

In each well, Pab and Ranikot formations have been targeted and interpreted. In Mehar-01 well, P-sonic and S-sonic are missing in the lower part of the reservoir, while a poor-quality density log is present in the whole zone. Mehar-02 well contains complete coverage of P-sonic and density log while missing S-sonic. Initially, the Shear sonic log is generated using [27] relationship and later modelled to optimize it. The splice zone is also present in the P-wave in Mehar-02 in the reservoir depth. Mehar-03 well contains a complete suite of elastic and petrophysics logs restricted to reservoir zone only. The data quality of the density log is comparatively poor in all three wells due to washed out zones. Preliminary data (de-spiking) conditioning and depth matching have been performed earlier. The source wavelet utilized was a statistical wavelet extracted from seismic data with a frequency range from 5 to 30 Hz having a constant zero phase. Initially, density and p-wave measured (raw) logs were convolved for synthetic generation. Later, modelled logs after rock physics application were utilized for synthetic generation. Finally, the two synthetics obtained were compared to analyze the best correlation. Synthetic generated using modelled logs showed a much better correlation as compared to synthetics generated using measured (raw) logs. Both the logs have been shown in the Figure 6a–c with blue (modelled) and red (measured) colors.

Rock physics analysis has been carried out by using the RockSI utility of HampsonRussell Software. According to [48], rock physics analysis requires four main components: minerals, fluids, the rock frame, and their assemblage (see Figure 4). In this study, the initial framework for the unconsolidated sandstone model, saturated with brine and gas, is utilized using equation (8), where the Ruess-Voigt equation [49] is used to compute the elastic moduli, while density is computed as their volumetric average.

Figure 4 Granular model is established by incorporating rock matrix in the dry rock frame which later is saturated by fluids using model inputs/parameters. Adopted workflow utilized to perform rock physics modelling.
Figure 4

Granular model is established by incorporating rock matrix in the dry rock frame which later is saturated by fluids using model inputs/parameters. Adopted workflow utilized to perform rock physics modelling.

The pressure-dependent effective dry rock bulk and shear moduli are computed with Hertz-Mindlin contact theory [31] at the critical porosity. The lower Hashim-Shtrikman lower bound [28] is applied here for an unconsolidated sand reservoir.

(8) 1 K m = V shale K shale + ( 1 − V shale ) K quartz ,

where, K m , K shale , and K quartz  are bulk moduli of matrix, shale, and quartz, while V shale  is volume of shale

Then dry rock moduli (equation (9)) at effective pressure and critical porosity were computed using Hertz-Mindlin theory using the formula given below. Dry rock moduli using Hashin-Shtrikman bounds were also computed over a range of porosities [45]. Both upper and lower bounds were applied to see the compaction and cementation trends.

(9) 1 K dry = 1 K m + φ K φ ,

where, K dry is bulk modulus of dry rock frame; K m is bulk modulus of matrix; K φ is bulk modulus at given porosity; and φ is porosity.

Afterward, Gassmann’s fluid substitution for saturated rock frame was performed using equations (10) and (11) and moduli were calculated by assuming that the pore spaces are fully saturated with the fluids. The shear moduli of dry and saturated are same.

(10) 1 K sat = 1 K m + φ K ˜ φ , K ˜ φ = K φ + K m K f K m − K f ,

(11) G sat = G dry ,

where, K sat, K m, K ˜ φ , and K f are bulk moduli while G sat and G dry are shear moduli of saturated rock, matrix, porosity at given temperature, and fluid, respectively.

Elastic parameters of fluids used in the models have been shown in Table 1.

Table 1

Elastic, and PVT properties at Reservoir Interval (Babasafari et al., 2020)

Mineral/Fluid Density (g/cc) Bulk modulus (GPa) Shear modulus (GPa)
Quartz 2.65 37 45
Clay 2.6 15 5
Coal 1.8 6 1.5
Water 1 2.46 0
Oil 0.75 1 0
Gas 0.1 0.02 0
Oil gravity 30API
Gas gravity 0.6
Pressure 2500PSI

Batzle & Wang’s [50] equations are used to compute the density and bulk modulus of the different fluids, while the effective fluid density and bulk modulus are derived using volumetric average and Wood’s equation given in equations (12) and (13), respectively.

(12) ρ sat  = ρ matrix  ( 1 − φ ) + ρ fluid  φ ,

(13) ρ fluid  = ρ water  S water  + ρ gas  S gas  ,

where, ρ sat , ρ matrix , and ρ fluid are densities of saturated rock, matrix, and fluid, respectively. Finally, velocities of P and S waves at saturated rock conditions are calculated using the formulae given below in equations (14) and (15), respectively.

(14) V P − sat = K sat + 4 / 3 G sat ρ sat ,

(15) V S − sat = G sat ρ sat ,

where, K sat and G sat are bulk and shear moduli of saturated rocks, respectively.

Model input reservoir conditions including temperature, pore and litho-static pressures, brine salinity, gas water ratio (GWR), and gas gravity are taken from the core and well log reports (see Table 1), while calculated petrophysical properties including effective porosity (Phie), shale volume (V sh), and water saturation (S w) have been used from petrophysical analysis (see Table 2). The model parameters including coordination number (C), critical porosity (Phic), friction coefficient (FC), grain angularity (RR), and matric stiffness index (MSI) along with the density, bulk, and shear moduli for quartz and clay have been optimized iteratively to match the model logs with the measured logs and then P-sonic, S-sonic, and density have resulted. Afterward, the rock physics template was overlaid on the cross-plots generated in the petro-elastic domain.

Table 2

Petrophysical properties of the reservoir zones in the wells Mehar-01, 02, and 03

Well name Shale volume (%) Effective porosity (%) Water saturation (%)
Mehar-01
Lower Ranikot 30 17 40
Pab 17 15 35
Mehar-02
Lower Ranikot 18 12 15
Pab 19 15 15
Mehar-03
Lower Ranikot 20 12 30
Pab 18 10 40

4 Results

4.1 Petrophysics

Petrophysical analysis has been performed on selected zones of Pab sandstone and lower Ranikot formations in the three wells Mehar-01, 02, and 03. The reservoir intervals have been chosen after applying zones of interest criteria (low gamma ray value, Separation between LLD and MSFL, cross over between Nphi and RhoB) and cut-off values (volume of clay >30%, water saturation ≥50%, and volume of calcite >60%) shown in Table 3. The petrophysical properties calculated have been shown in the last four tracks with lithology, porosity, and saturation. Pab sandstone is cleaner and has less clay content as compared to the lower Ranikot Formation. The porosity behavior is almost same for both formations having an average value between 12 to 15%, shale volume 17 to 20% on average, and water saturation 20 to 35% on average. The petrophysical analysis revealed that the upper part of the Ranikot Formation is not favorable for hydrocarbon production in any well as it exhibits a high volume of shale (V sh) (33%), low effective porosity (Phi) (6%), high water saturation (S w) (70%), and false crossover between density and neutron porosity logs, while the Pab sandstone has shown more promising character in the three evaluated wells with the high effective porosities and saturations. Good effective porosity, less volume of clay, and high saturations for the lower part of the Lower Ranikot make it more economically viable especially for well Mehar-02 (Table 2). Petrophysical interpretation of Pab and Lower Ranikot formations of well 03 has been shown in Figure 5a and b. The quantitative values of reservoir properties in both the formations of three wells are mentioned in Table 2. These calculated rock attributes have been utilized further in the rock physics analysis as an input parameter along with the other properties.

Table 3

Facies classification criteria based on petrophysical properties

Facies name Well log Lower limit Upper limit Facie color
Shale Volume of clay >30% Nil Green
Carbonates Volume of calcite >60% Nil Sky blue
Wet sand Water saturation ≥50% Nil Blue
Volume of clay Nil ≤30%
Gas sand Water saturation Nil <50% Red
Volume of clay Nil ≤30%
Figure 5 (a) Petrophysical analysis done on the complete reservoir interval of the Cretaceous sand (Pab) in well M-03. Calculated rock properties are shown along with the logs in multiple tracks from left to right. (b) Petrophysical analysis done on the reservoir interval of the Paleocene sand (Lower Ranikot) in well M-03. Calculated rock properties are shown along with the logs in multiple tracks from left to right.
Figure 5

(a) Petrophysical analysis done on the complete reservoir interval of the Cretaceous sand (Pab) in well M-03. Calculated rock properties are shown along with the logs in multiple tracks from left to right. (b) Petrophysical analysis done on the reservoir interval of the Paleocene sand (Lower Ranikot) in well M-03. Calculated rock properties are shown along with the logs in multiple tracks from left to right.

4.2 Rock physics modelling

The data quality of the measured and predicted elastic logs P-sonic, S-sonic, and density in all three wells (Mehar-01, 02 & 03) is quite evident in Figure 6a–c. At the depth from 3,235–3,675 m, 3,805–3,965 m, and 3,710–3,868 m in wells 01, 02, and 03, respectively, multiple sand intervals are acting as a reservoir due to low shale volume, good porosities, and low water saturation. Splice zones observed in the measured logs are highlighted by blue circles and ovals in Figure 6a–c. The first three columns having V p (1st column), V s (2nd column), and density (3rd column) show a good match between the rock physics modelled logs (red) with the measured/conditioned logs (blue), while the reservoir zone consists of multiple facies containing gas sand (red), wet sand (blue), shales (green), and carbonates (sky blue) shown as a litho-log in the last column (see Figure 6a–c). These facies are classified based on the ranges defined in estimating petrophysical properties using equations (1)–(7) (see Table 3).

Figure 6 (a) P, S, and density logs quality with rock physics modelled logs (blue) and measured logs (red) in Maher-01 well. Spiking behavior of curve has been removed after rock physics modelling (circle 1). Bad density log conditions due to rouges hole have been improved after modelling shown in circle 2 and oval 3. Missing P and S logs in the lower part of well have been successfully modelled (oval 4). (b) P, S, and density logs quality with rock physics modelled logs (blue) and measured logs (red) in Maher-02 well. Bad density log conditions due to rouges hole have been improved after modelling (oval 1). (c) P, S, and density logs quality with rock physics modelled logs (blue) and measured logs (red) in Maher-03 well. Bad density log condition due to rouges hole has been improved after modelling (oval 1 & 2).
Figure 6

(a) P, S, and density logs quality with rock physics modelled logs (blue) and measured logs (red) in Maher-01 well. Spiking behavior of curve has been removed after rock physics modelling (circle 1). Bad density log conditions due to rouges hole have been improved after modelling shown in circle 2 and oval 3. Missing P and S logs in the lower part of well have been successfully modelled (oval 4). (b) P, S, and density logs quality with rock physics modelled logs (blue) and measured logs (red) in Maher-02 well. Bad density log conditions due to rouges hole have been improved after modelling (oval 1). (c) P, S, and density logs quality with rock physics modelled logs (blue) and measured logs (red) in Maher-03 well. Bad density log condition due to rouges hole has been improved after modelling (oval 1 & 2).

4.3 Mu-rho and Lambda-rho cross-plot analysis

The cross-plots of measured and predicted Mu-rho and Lambda-rho elastic properties are shown below (see Figures 7–9). As Mu-rho and Lambda-rho are very sensitive towards fluids and lithology, the Lamé’s parameters, rigidity (μ) and lambda (λ), were utilized for the discrimination of brine sand from hydrocarbon-bearing sand. Lambda (incompressibility) is a P-wave-derived parameter and is more sensitive for lithology prediction. Since fluids show resistance against compression, their presence and saturation lead to a significant reduction in the incompressibility measurement. Lamda-rho, thus being a product of lambda with bulk density, is a good lithology indicator. As hydrocarbon-bearing sand is denser than water-filled sand, low values of Lambda-rho show a good gas sand effect [51,52].

Figure 7 Rock physics modelled cross-plot of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale in Mehar-01 well in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.
Figure 7

Rock physics modelled cross-plot of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale in Mehar-01 well in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.

Figure 8 Rock physics modelled cross-plot of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale in Mehar-02 well in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.
Figure 8

Rock physics modelled cross-plot of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale in Mehar-02 well in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.

Figure 9 Rock physics modelled cross-plot of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale in Mehar-03 well in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.
Figure 9

Rock physics modelled cross-plot of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale in Mehar-03 well in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.

Mu (rigidity), on the other hand, is a shear wave-derived parameter. As sand is more rigid than shale, it exerts more resistance to shearing. Mu alone is not mainly affected by fluid type, so its values in hydrocarbon and brine sand most likely remain relatively constant. Mu-rho, thus being the product of Mu with bulk density, is a good fluid indicator with high values being consistent with gas sand [53].

In every cross-plot, Lambda-rho is kept on X-axis, while Mu-rho is plotted on Y-axis with the lithological facies on Z-axis within the reservoir zone. There is no pattern/orientation observed for facies in the elastic measured domain, rather few abnormal patterns are present in the measured logs. Since well log data are considered to be affected by many parameters and borehole conditions, it is difficult to discriminate lithology and fluid content from elastic log data alone. Furthermore, it is unable to separate particular facies in measured petro-elastic domain, thereby missing the prior relationship that is the core step for reservoir characterization. Thus, the rock physics template needs to be taken into account along with the modelled elastic properties to reduce risk for the interpretation. In the predicted logs, input parameters are well-controlled, making it more reliable and effective to discriminate different types of facies. The cross-plots with measured logs parameters (left) and improved modelled logs (right) are shown in the Figures 7–9 for the wells Mehar-01, 02, and 03, respectively, and for combined wells (see Figure 10), depicting proper facies orientation with clear demarcation between the pay and non-pay facies.

Figure 10 Rock physics modelled cross-plot for combined wells of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale after rock physics modelling in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.
Figure 10

Rock physics modelled cross-plot for combined wells of Lambda-Rho versus Mu-Rho for reservoir interval, clearly demarcating the gas sand, wet sand, limestone, and shale after rock physics modelling in the right cross-plot. Measured logs are plotted in left cross-plot where separation between the facies is obscured.

The predicted cross-plots defined four facies present in the reservoir interval. Zone with medium Mu-rho and high Lambda-rho corresponds to the limestone facies (sky blue cluster points), while the zone with low to moderate Mu-rho and moderate Lambda-rho values depicts shale facies occurrence (green cluster points). Low to medium Lambda-rho and Mu-rho values are defined as brine sand (blue cluster points), whereas low to medium Mu-rho with very low Lambda-rho values represents typical of a gas sand (red cluster points). In conclusion, Lambda-rho on the horizontal axis provided good separation of the shale, limestone, wet sand, and gas sand facies, which is a typical lithological indicator. Hence, rock physics modelled logs with the above said attributes helped to clearly differentiate the objective facies. Hence, Lame’s parameters are effectively used to characterize lithology and fluid for prospect evaluation.

4.4 Impedance versus V p/V s ratio cross-plot analysis

Rock properties such as acoustic impedance and V p/V s ratio also play a vital role to separate hydrocarbon saturated facies from the rest of non-pay facies. Cross-plots of P-imp and V p/V s ratio are generated overlain by rock physics template with facies displayed on the Z-axis for the three wells (see Figures 11–13). Four zones/clusters of facies are clearly separated on these cross-plots. The points with low acoustic impedance (1,000–14,000 m/s*g/cc), low V p/V s (1.52–1.57), high porosity, and high gas saturation depict the gas sand facies highlighted in red polygon [31,54]. However, green polygon with high values of V p/V s ratio, water saturation (100% saturation), and impedance represents shale facies without gas saturation. The area lying between the two separated zones (sand and shale) is an indicator of water saturation. These wet sands highlighted by blue polygon are showing relatively high values of impedance and water saturation compared to gas sand. The high P-impedance and moderate to high V p/V s ratio exhibit limestone highlighted by sky blue color polygon. The facies segregated through different polygons are clearly demarcating the similar facies present in the litho-log shown previously (see Figure 6a–c) for the three wells in the reservoir zone.

Figure 11 Rock physics modelled logs cross-plot of V p/V s ratio versus impedance for reservoir depicting a clear demarcation between gas sand, wet sand, limestone, and shale in Mehar-01 well overlain by rock physics template, i.e., showing the behavior of porosity and saturation trends.
Figure 11

Rock physics modelled logs cross-plot of V p/V s ratio versus impedance for reservoir depicting a clear demarcation between gas sand, wet sand, limestone, and shale in Mehar-01 well overlain by rock physics template, i.e., showing the behavior of porosity and saturation trends.

Figure 12 Rock physics modelled logs plot of V p/V s ratio versus impedance versus for reservoir depicting a clear demarcation between gas sand, wet sand, limestone, and shale in Mehar-02 well overlain by rock physics template, i.e., showing the behavior of porosity and saturation trends.
Figure 12

Rock physics modelled logs plot of V p/V s ratio versus impedance versus for reservoir depicting a clear demarcation between gas sand, wet sand, limestone, and shale in Mehar-02 well overlain by rock physics template, i.e., showing the behavior of porosity and saturation trends.

Figure 13 Rock physics modelled logs plot of V p/V s ratio versus impedance versus V p/V s Ratio for reservoir depicting a clear demarcation between gas sand, wet sand, limestone, and shale in Mehar-03 well overlain by rock physics template, i.e., showing the behavior of porosity and saturation trends.
Figure 13

Rock physics modelled logs plot of V p/V s ratio versus impedance versus V p/V s Ratio for reservoir depicting a clear demarcation between gas sand, wet sand, limestone, and shale in Mehar-03 well overlain by rock physics template, i.e., showing the behavior of porosity and saturation trends.

Figures 14–16 show the comparison of well to seismic tie between the measured and modelled logs and an improvement in seismic-synthetics correlation was observed. The correlation coefficient calculated using measured logs is 64, 75, and 73% at well Mehar −01, 02, and 03, respectively (see Figures 14a–16a). This coefficient improved robustly after using modelled logs reaching up to 82, 80, and 88% for the above said wells (see Figures 14b–16b).

Figure 14 In Mehar-01 well, synthetic seismogram has been generated using measured logs on the left plot (a). In the right plot (b), synthetic seismogram has been generated using rock physics modelled logs where along with the improvement in the quality of logs, well to seismic correlation coefficient is also improved.
Figure 14

In Mehar-01 well, synthetic seismogram has been generated using measured logs on the left plot (a). In the right plot (b), synthetic seismogram has been generated using rock physics modelled logs where along with the improvement in the quality of logs, well to seismic correlation coefficient is also improved.

Figure 15 In Mehar-02 well, synthetic seismogram has been generated using measured logs on the left plot (a). In the right plot (b), synthetic seismogram has been generated using rock physics modelled logs where along with the improvement in the quality of logs, well to seismic correlation coefficient is also improved.
Figure 15

In Mehar-02 well, synthetic seismogram has been generated using measured logs on the left plot (a). In the right plot (b), synthetic seismogram has been generated using rock physics modelled logs where along with the improvement in the quality of logs, well to seismic correlation coefficient is also improved.

Figure 16 In Mehar-03 well, synthetic seismogram has been generated using measured logs on the left plot (a). In the right plot (b), synthetic seismogram has been generated using rock physics modelled logs where along with the improvement in the quality of logs, well to seismic correlation coefficient is also improved.
Figure 16

In Mehar-03 well, synthetic seismogram has been generated using measured logs on the left plot (a). In the right plot (b), synthetic seismogram has been generated using rock physics modelled logs where along with the improvement in the quality of logs, well to seismic correlation coefficient is also improved.

5 Discussion

An integrated approach of petrophysics and rock physics is utilized in this paper to characterize the reservoir properties in the elastic domain that can absolutely differentiate the pay facies from the non in the Mehar Gas Field of the Central Indus Basin, Pakistan.

The technique applied in this paper can easily be adopted to segregate the pay and non-pay facies in the basins having the complex and heterogenous clastic rocks. Magoba and Opuwari [18] classified facies and hydrocarbon prospects in shallow marine sandstone, offshore south Africa, using petrophysics and fluid substitution modelling. Likewise, Yasin et al. [55] performed rock physics analysis to develop a relationship between elastic and petrophysical properties using inversion technique to classify extremely heterogeneous clastic reservoirs rocks. An integrated approach of petrophysics and rock physics has been applied in the gas field of lower Indus basin by Azeem et al. [9] by integrating petrophysics and rock physics to characterize cretaceous sand intervals. Fitch et al. [56] suggested that rock physics modelling technique is suitable to bridge the gap between core and seismic data to enhance the hydrocarbon facies.

The adopted methodology assisted in developing a consistent and reliable rock physics model. In the initial phase, well log interpretation was carried out to highlight the targeted reservoir zones in the used wells. The calculated petrophysical properties were then used as an input to build the rock physics models. The predicted logs were in good match and acceptable range with the measured elastic logs. Moreover, litho-fluid facies were efficiently differentiated with the modelled cross-plots. The prior information of core, geology, and well logs helped to build the rock physics template with the inclusion of fluid type, pressure, and temperature conditions. The predicted model proved fruitful not only in accurate estimation of true elastic parameters (V p, V s, density), but also helped to model S-sonic log in that well where it is missing in the targeted zones. Furthermore, washout and spiking effects were removed from the measured density log. Cross-plots of Mu-rho, Lambda-rho, Impedance, and V p/V s ratio efficiently segregated the fluid-filled facies from the water saturated and shale facies. Based on cross-plots analysis, the quantitative values of elastic parameters have been defined to differentiate between the gas sand, shale, wet sand, and limestone. As the Mu-rho is sensitive towards fluids, they provided good separation of gas sands on the Y-axis, while Lambda-rho on the X-axis effectively segregated the lithological facies. On the other hand, V p/V s ratio is more sensitive to gas-bearing sand accompanying the impedance. Moreover, the modelled logs helped to improve the well to seismic tie that improved the reservoir characterizations in a better way. Thus, rock physics modelled logs provided a robust way to calculate the missed logs as well as effectively separated the litho-fluid facies in the heterogeneous sands of the Mehar gas field.

6 Conclusion

A successful study has been carried out to highlight the litho-fluid classification and enhanced reservoir depiction in the Mehar gas field using petrophysical analysis and rock physics modelling. The reservoirs selected for this work were sandstone of Lower Ranikot and Pab formations. Three wells (Mehar-01, 02, and 03) data have been used to perform petrophysical interpretation and rock physics modelling. The integrated and iterative log data conditioning, seismic petrophysics interpretation, rock physics template, and seismic well ties workflow enabled the generation of a rock physics model that has been successfully used to correct poor-quality data and synthesize missing elastic logs, especially P-sonic and S-sonic in the utilized wells. Cross-plot analysis of petro-elastic attributes fairly discriminated the facies into gas sand, wet sand, shale, and limestone. The modelled logs show improved data quality and consistency between the wells of the Mehar gas field.

Acknowledgments

All the authors would like to thank the Directorate General of Petroleum Concessions (DGPC), Pakistan, for being a data source for this work. The authors would also like to thank the Department of Earth Sciences, Quaid-i-Azam University (QAU), Islamabad, Pakistan, Department of Earth and Environmental Sciences, Bahria University, Islamabad, Pakistan, Compagnie Générale de Géophysique (CGG), and LMK Resources (LMKR) Pakistan for providing the necessary software and laboratory support to complete this work.

  1. Authors contributions: Urooj Shakir did the manuscript writing along with the technical work. Aamir Ali supervised the research and provided guidance and valuable assistance in technical writing. Muyyassar Hussain supported the technical workflow execution of the software. Muhammad Raiees Amjad helped in literature review and petrophysical analysis.

  2. Conflict of interest: Authors state no conflict of interest.

References

[1] Li G, Li G, Wang Y, Qi S, Yang J. A rock physics model for estimating elastic properties of upper Ordovician-lower Silurian mudrocks in the Sichuan Basin, China. Eng Geol. 2020;266:105460.10.1016/j.enggeo.2019.105460Suche in Google Scholar

[2] Ghosh D, Babasafari A, Ratnam T, Sambo C, editors. New workflow in reservoir modelling-incorporating high resolution seismic and rock physics; 2018. Offshore Technology Conference Offshore Technology Conference Asia.10.4043/28388-MSSuche in Google Scholar

[3] Bredesen K, Jensen EH, Johansen TA, Avseth P. Quantitative seismic interpretation using inverse rock physics modelling. Pet Geosci. 2015;21(4):271–84.10.1144/petgeo2015-006Suche in Google Scholar

[4] Babasafari AA, Ghosh DP, Salim AM, Kordi M. Integrating petroelastic modeling, stochastic seismic inversion, and Bayesian probability classification to reduce uncertainty of hydrocarbon prediction: Example from Malay Basin. Interpretation. 2020;8(3):SM65–82.10.1190/INT-2019-0077.1Suche in Google Scholar

[5] Dvorkin J, Gutierrez MA, Grana D. Seismic reflections of rock properties. England, UK: Cambridge University Press; 2014.10.1017/CBO9780511843655Suche in Google Scholar

[6] Saberi MR. A closer look at rock physics models and their assisted interpretation in seismic exploration. Iran J Geophysics. 2017;10(5):71–84.Suche in Google Scholar

[7] Dou Q, Sun Y, Sullivan C. Rock-physics-based carbonate pore type characterization and reservoir permeability heterogeneity evaluation, Upper San Andres reservoir, Permian Basin, west Texas. J Appl Geophysics. 2011;74(1):8–18.10.1016/j.jappgeo.2011.02.010Suche in Google Scholar

[8] Azeem T, Chun WY, Khalid P, Ehsan MI, Rehman F, Naseem AA. Sweetness analysis of lower Goru sandstone intervals of the Cretaceous age, Sawan gas field, Pakistan. Epis J Int Geosci. 2018;41:235–47.10.18814/epiiugs/2018/018019Suche in Google Scholar

[9] Azeem T, Chun WY, Khalid P, Qing LX, Ehsan MI, Munawar MJ, et al. An integrated petrophysical and rock physics analysis to improve reservoir characterization of Cretaceous sand intervals in middle Indus Basin, Pakistan. J Geophysics Eng. 2017;14(2):212–25.10.1088/1742-2140/14/2/212Suche in Google Scholar

[10] Babasafari A, Ghosh D, Salim AM, Alashloo SM. Rock physics modeling assisted reservoir properties prediction: case study Malay Basin. Int J Eng Technol. 2018;7(3):24–8.10.14419/ijet.v7i3.32.18385Suche in Google Scholar

[11] Jensen EH, Johansen TA, Avseth P, Bredesen K. Quantitative interpretation using inverse rock-physics modeling on AVO data. Lead Edge. 2016;35(8):677–83.10.1190/tle35080677.1Suche in Google Scholar

[12] Sohail GM, Hawkes CD. An evaluation of empirical and rock physics models to estimate shear wave velocity in a potential shale gas reservoir using wireline logs. J Pet Sci Eng. 2020;185:106666.10.1016/j.petrol.2019.106666Suche in Google Scholar

[13] Avseth P, Skjei N. Rock physics modeling of static and dynamic reservoir properties – a heuristic approach for cemented sandstone reservoirs. Lead Edge. 2011;30(1):90–6.10.1190/1.3535437Suche in Google Scholar

[14] Karimpouli S, Khoshlesan S, Saenger EH, Koochi HH. Application of alternative digital rock physics methods in a real case study: a challenge between clean and cemented samples. Geophys Prospecting. 2018;66(4):767–83.10.1111/1365-2478.12611Suche in Google Scholar

[15] Sayers CM, den Boer LD. Rock physics-based relations for density and S-velocity versus P-velocity in deepwater subsalt Gulf of Mexico shales. Lead Edge. 2011;30(12):1376–81.10.1190/1.3672482Suche in Google Scholar

[16] Dou Q, Sun Y, Sullivan C. Rock-physics-based heterogeneity characterization of a carbonate reservoir in the Permian Basin. SEG Technical Program Expanded Abstracts 2009: Society of Exploration Geophysicists; 2009. p. 1945–9.10.1190/1.3255236Suche in Google Scholar

[17] Ajisafe Y, Ako B. 3-D seismic attributes for reservoir characterization of “Y” field Niger Delta, Nigeria. IOSR J Appl Geol Geophysics. 2013;1(2):23–31.10.9790/0990-0122331Suche in Google Scholar

[18] Magoba M, Opuwari M. Petrophysical interpretation and fluid substitution modelling of the upper shallow marine sandstone reservoirs in the Bredasdorp Basin, offshore South Africa. J Pet Exploration Prod Technol. 2020;10(2):783–803.10.1007/s13202-019-00796-1Suche in Google Scholar

[19] Grana D. Bayesian linearized rock-physics inversion. Geophysics. 2016;81(6):D625–41.10.1190/geo2016-0161.1Suche in Google Scholar

[20] Babasafari AA, Khoshdel H, editors. Mapping reservoir fluid using AVO inversion. In: 6th EAGE Saint Petersburg International Conference and Exhibition. Houten, The Netherlands: European Association of Geoscientists & Engineers; 2014.Suche in Google Scholar

[21] Ahmed N, Khalid P, Shafi HMB, Connolly P. DHI evaluation by combining rock physics simulation and statistical techniques for fluid identification of Cambrian-to-Cretaceous clastic reservoirs in Pakistan. Acta Geophysica. 2017;65(5):991–1007.10.1007/s11600-017-0070-5Suche in Google Scholar

[22] Xu S, Payne MA. Modeling elastic properties in carbonate rocks. Lead Edge. 2009;28(1):66–74.10.1190/1.3064148Suche in Google Scholar

[23] Dvorkin J. Seismic reflections of rock properties. Hart’s E & P. 2004;77(11):59–61.10.1017/CBO9780511843655Suche in Google Scholar

[24] Liu Z, Sun SZ. The differential Kuster–Toksöz rock physics model for predicting S-wave velocity. J Geophysics Eng. 2015;12(5):839–48.10.1088/1742-2132/12/5/839Suche in Google Scholar

[25] Avseth P, Mukerji T, Mavko G, Dvorkin J. Rock-physics diagnostics of depositional texture, diagenetic alterations, and reservoir heterogeneity in high-porosity siliciclastic sediments and rocks − A review of selected models and suggested work flows. Geophysics. 2010;75(5):75A31–47.10.1190/1.3483770Suche in Google Scholar

[26] Waters KD, Kemper MA. Find the rocks and the fluids will follow – AVO as a tool for lithology classification. Interpretation. 2014;2(2):SC77–91.10.1190/INT-2013-0118.1Suche in Google Scholar

[27] Greenberg M, Castagna J. Shear‐wave velocity estimation in porous rocks: theoretical formulation, preliminary verification and applications1. Geophys Prospecting. 1992;40(2):195–209.10.1111/j.1365-2478.1992.tb00371.xSuche in Google Scholar

[28] Hashin Z, Shtrikman S. A variational approach to the elastic behavior of multiphase materials. J Mech Phys Solids. 1963;11(2):127–40.10.1016/0022-5096(63)90060-7Suche in Google Scholar

[29] Wyllie MRJ, Gregory AR, Gardner LW. Elastic wave velocities in heterogeneous and porous media. Geophysics. 1956;21(1):41–70.10.1190/1.1438217Suche in Google Scholar

[30] Wyllie MRJ. The fundamentals of well log interpretation. Prentice Hall, New York: Academic Press; 1963.Suche in Google Scholar

[31] Avseth P, Bachrach R. Seismic properties of unconsolidated sands: Tangential stiffness, Vp/Vs ratios and diagenesis. SEG Technical Program Expanded Abstracts 2005: Society of Exploration Geophysicists; 2005. p. 1473–6.10.1190/1.2147968Suche in Google Scholar

[32] Babasafari AA, Bashir Y, Ghosh DP, Salim AMA, Janjuhah HT, Kazemeini SH, et al. A new approach to petroelastic modeling of carbonate rocks using an extended pore-space stiffness method, with application to a carbonate reservoir in Central Luconia, Sarawak, Malaysia. Lead Edge. 2020;39(8):592a1–10.10.1190/tle39080592a1.1Suche in Google Scholar

[33] Rathore R, Hoo C, editors. Rock physics modeling in oil and gas field development: a methodology for reservoir characterization in low salinity pay. Seismic Driven Reservoir Characterization and Production Management. Houten, The Netherlands: European Association of Geoscientists & Engineers; 2015.10.3997/2214-4609.201412301Suche in Google Scholar

[34] Szeliga W, Bilham R, Schelling D, Kakar DM, Lodi S. Fold and thrust partitioning in a contracting fold belt: Insights from the 1931 Mach earthquake in Baluchistan. Tectonics. 2009;28(5):1–13.10.1029/2008TC002265Suche in Google Scholar

[35] Arshad K, Imran M, Iqbal M, editors. Hydrocarbon prospectivity and risk analysis of an under-explored Western Kirthar fold belt of Pakistan. Offshore Mediterranean Conference and Exhibition; 2013. Offshore Mediterranean Conference.Suche in Google Scholar

[36] Fitzsimmons R, Buchanan J, Izatt C. The role of outcrop geology in predicting reservoir presence in the Cretaceous and Paleocene successions of the Sulaiman Range, Pakistan. AAPG Bull. 2005;89(2):231–54.10.1306/08040403083Suche in Google Scholar

[37] Ahmad N, Fink P, Sturrock S, Mahmood T, Ibrahim M. Sequence stratigraphy as predictive tool in lower goru fairway, lower and middle Indus platform, Pakistan. PAPG, ATC. 2004;1:85–104.Suche in Google Scholar

[38] Wandrey CJ, Law B, Shah HA. Sembar Goru/Ghazij composite total petroleum system, Indus and Sulaiman-Kirthar geologic provinces. Pakistan and India: US Department of the Interior, US Geological Survey; 2004.Suche in Google Scholar

[39] Hinsch R, Asmar C, Hagedorn P, Nasim M, Rasheed MA, Stevens N, et al. Structural modelling in the kirthar fold belt of pakistan: from seismic to regional scale. AAPG/SEG, International Conference and Exhibition, London, UK; 2018.Suche in Google Scholar

[40] Zafar ZA, Shoaib K, Afsar F, Raja ZA, Tanveer A, Burley S, editors. A radical seismic interpretation re-think resolves the structural complexities of the Zamzama Field, Kirther Foredeep, Pakistan. PAPG. In: Islamabad, Pakistan: SPE Annual Technical Conference; December 2018.Suche in Google Scholar

[41] Ogilvie SR, Isakov E, Glover PW. Fluid flow through rough fractures in rocks. II: A new matching model for rough rock fractures. Earth Planet Sci Lett. 2006;241(3–4):454–65.10.1016/j.epsl.2005.11.041Suche in Google Scholar

[42] Grana D, Pirrone M, Mukerji T. Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis. Geophysics. 2012;77(3):WA45–63.10.1190/geo2011-0272.1Suche in Google Scholar

[43] Akhter G, Ahmed Z, Ishaq A, Ali A. Integrated interpretation with Gassmann fluid substitution for optimum field development of Sanghar area, Pakistan: a case study. Arab J Geosci. 2015;8(9):7467–79.10.1007/s12517-014-1664-8Suche in Google Scholar

[44] Shah MS, Khan MHR, Rahman A, Islam MR, Ahmed SI, Molla MI, et al. Petrophysical evaluation of well log data for reservoir characterization in Titas gas field, Bangladesh: a case study. J Nat Gas Sci Eng. 2021;95:104129.10.1016/j.jngse.2021.104129Suche in Google Scholar

[45] Wawrzyniak-Guz K. Rock physics modelling for determination of effective elastic properties of the lower Paleozoic shale formation, North Poland. Acta Geophys. 2019;67(6):1967–89.10.1007/s11600-019-00355-6Suche in Google Scholar

[46] Ali A, Alves TM, Saad FA, Ullah M, Toqeer M, Hussain M. Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques. J Nat Gas Sci Eng. 2018;49:41–55.10.1016/j.jngse.2017.10.010Suche in Google Scholar

[47] Rider MH. The geological interpretation of well logs. USA: Halsted Press; 1986.Suche in Google Scholar

[48] Reine C. Discovering a supermodel-A rock-Physics tutorial. CSEG Recorder. 2017;42(2):20–5.Suche in Google Scholar

[49] Mavko G, Mukerji T, Dvorkin J. The rock physics handbook. England, UK: Cambridge University Press; 2020.10.1017/9781108333016Suche in Google Scholar

[50] Batzle M, Wang Z. Seismic properties of pore fluids. Geophysics. 1992;57(11):1396–408.10.1190/1.1443207Suche in Google Scholar

[51] Ogbamikhumi A, Igbinigie NS. Rock physics attribute analysis for hydrocarbon prospectivity in the Eva field onshore Niger Delta Basin. J Pet Exploration Prod Technol. 2020;10(8):3127–38.10.1007/s13202-020-00975-5Suche in Google Scholar

[52] Abbey CP, Okpogo EU, Atueyi IO. Application of rock physics parameters for lithology and fluid prediction of ‘TN’field of Niger Delta basin, Nigeria. Egypt J Pet. 2018;27(4):853–66.10.1016/j.ejpe.2018.01.001Suche in Google Scholar

[53] Omudu L, Ebeniro J. Cross-plotting of rock properties for fluid discrimination using well data in offshore Niger Delta. Nigerian J Phys. 2005;17(1):16–20.10.4314/njphy.v17i1.37986Suche in Google Scholar

[54] Da‐Xing W. A study on the rock physics model of gas reservoir in tight sandstone. Chin J Geophysics. 2017;60(1):64–83.10.1002/cjg2.30028Suche in Google Scholar

[55] Yasin Q, Sohail GM, Khalid P, Baklouti S, Du Q. Application of machine learning tool to predict the porosity of clastic depositional system, Indus Basin, Pakistan. J Pet Sci Eng. 2021;197:107975.10.1016/j.petrol.2020.107975Suche in Google Scholar

[56] Fitch P, Davies S, Lovell M, Pritchard T. Reservoir quality and reservoir heterogeneity: petrophysical application of the Lorenz coefficient. Petrophysics. 2013;54(5):465–74.Suche in Google Scholar
Received: 2021-05-08
Revised: 2021-10-21
Accepted: 2021-10-24
Published Online: 2021-12-02

© 2021 Urooj Shakir et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Regular Articles
  2. Lithopetrographic and geochemical features of the Saalian tills in the Szczerców outcrop (Poland) in various deformation settings
  3. Spatiotemporal change of land use for deceased in Beijing since the mid-twentieth century
  4. Geomorphological immaturity as a factor conditioning the dynamics of channel processes in Rządza River
  5. Modeling of dense well block point bar architecture based on geological vector information: A case study of the third member of Quantou Formation in Songliao Basin
  6. Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
  7. Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
  8. Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data
  9. Spatiotemporal evolution of single sandbodies controlled by allocyclicity and autocyclicity in the shallow-water braided river delta front of an open lacustrine basin
  10. Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation
  11. Impulse noise treatment in magnetotelluric inversion
  12. Application of multivariate regression on magnetic data to determine further drilling site for iron exploration
  13. Comparative application of photogrammetry, handmapping and android smartphone for geotechnical mapping and slope stability analysis
  14. Geochemistry of the black rock series of lower Cambrian Qiongzhusi Formation, SW Yangtze Block, China: Reconstruction of sedimentary and tectonic environments
  15. The timing of Barleik Formation and its implication for the Devonian tectonic evolution of Western Junggar, NW China
  16. Risk assessment of geological disasters in Nyingchi, Tibet
  17. Effect of microbial combination with organic fertilizer on Elymus dahuricus
  18. An OGC web service geospatial data semantic similarity model for improving geospatial service discovery
  19. Subsurface structure investigation of the United Arab Emirates using gravity data
  20. Shallow geophysical and hydrological investigations to identify groundwater contamination in Wadi Bani Malik dam area Jeddah, Saudi Arabia
  21. Consideration of hyperspectral data in intraspecific variation (spectrotaxonomy) in Prosopis juliflora (Sw.) DC, Saudi Arabia
  22. Characteristics and evaluation of the Upper Paleozoic source rocks in the Southern North China Basin
  23. Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques
  24. Input/output inconsistencies of daily evapotranspiration conducted empirically using remote sensing data in arid environments
  25. Geotechnical profiling of a surface mine waste dump using 2D Wenner–Schlumberger configuration
  26. Forest cover assessment using remote-sensing techniques in Crete Island, Greece
  27. Stability of an abandoned siderite mine: A case study in northern Spain
  28. Assessment of the SWAT model in simulating watersheds in arid regions: Case study of the Yarmouk River Basin (Jordan)
  29. The spatial distribution characteristics of Nb–Ta of mafic rocks in subduction zones
  30. Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
  31. Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
  32. Detection and modeling of soil salinity variations in arid lands using remote sensing data
  33. Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images
  34. Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China
  35. Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
  36. Comparing LiDAR and SfM digital surface models for three land cover types
  37. East Asian monsoon during the past 10,000 years recorded by grain size of Yangtze River delta
  38. Influence of diagenetic features on petrophysical properties of fine-grained rocks of Oligocene strata in the Lower Indus Basin, Pakistan
  39. Impact of wall movements on the location of passive Earth thrust
  40. Ecological risk assessment of toxic metal pollution in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, NW China
  41. Seasonal color matching method of ornamental plants in urban landscape construction
  42. Influence of interbedded rock association and fracture characteristics on gas accumulation in the lower Silurian Shiniulan formation, Northern Guizhou Province
  43. Spatiotemporal variation in groundwater level within the Manas River Basin, Northwest China: Relative impacts of natural and human factors
  44. GIS and geographical analysis of the main harbors in the world
  45. Laboratory test and numerical simulation of composite geomembrane leakage in plain reservoir
  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
https://doi.org/10.1515/geo-2020-0311
Schlagwörter für diesen Artikel
petrophysics; rock physics; cross-plots; Lambda-rho versus Mu-rho; impedance versus V p/V s ratio
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. Lithopetrographic and geochemical features of the Saalian tills in the Szczerców outcrop (Poland) in various deformation settings
  3. Spatiotemporal change of land use for deceased in Beijing since the mid-twentieth century
  4. Geomorphological immaturity as a factor conditioning the dynamics of channel processes in Rządza River
  5. Modeling of dense well block point bar architecture based on geological vector information: A case study of the third member of Quantou Formation in Songliao Basin
  6. Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
  7. Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
  8. Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data
  9. Spatiotemporal evolution of single sandbodies controlled by allocyclicity and autocyclicity in the shallow-water braided river delta front of an open lacustrine basin
  10. Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation
  11. Impulse noise treatment in magnetotelluric inversion
  12. Application of multivariate regression on magnetic data to determine further drilling site for iron exploration
  13. Comparative application of photogrammetry, handmapping and android smartphone for geotechnical mapping and slope stability analysis
  14. Geochemistry of the black rock series of lower Cambrian Qiongzhusi Formation, SW Yangtze Block, China: Reconstruction of sedimentary and tectonic environments
  15. The timing of Barleik Formation and its implication for the Devonian tectonic evolution of Western Junggar, NW China
  16. Risk assessment of geological disasters in Nyingchi, Tibet
  17. Effect of microbial combination with organic fertilizer on Elymus dahuricus
  18. An OGC web service geospatial data semantic similarity model for improving geospatial service discovery
  19. Subsurface structure investigation of the United Arab Emirates using gravity data
  20. Shallow geophysical and hydrological investigations to identify groundwater contamination in Wadi Bani Malik dam area Jeddah, Saudi Arabia
  21. Consideration of hyperspectral data in intraspecific variation (spectrotaxonomy) in Prosopis juliflora (Sw.) DC, Saudi Arabia
  22. Characteristics and evaluation of the Upper Paleozoic source rocks in the Southern North China Basin
  23. Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques
  24. Input/output inconsistencies of daily evapotranspiration conducted empirically using remote sensing data in arid environments
  25. Geotechnical profiling of a surface mine waste dump using 2D Wenner–Schlumberger configuration
  26. Forest cover assessment using remote-sensing techniques in Crete Island, Greece
  27. Stability of an abandoned siderite mine: A case study in northern Spain
  28. Assessment of the SWAT model in simulating watersheds in arid regions: Case study of the Yarmouk River Basin (Jordan)
  29. The spatial distribution characteristics of Nb–Ta of mafic rocks in subduction zones
  30. Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
  31. Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
  32. Detection and modeling of soil salinity variations in arid lands using remote sensing data
  33. Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images
  34. Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China
  35. Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
  36. Comparing LiDAR and SfM digital surface models for three land cover types
  37. East Asian monsoon during the past 10,000 years recorded by grain size of Yangtze River delta
  38. Influence of diagenetic features on petrophysical properties of fine-grained rocks of Oligocene strata in the Lower Indus Basin, Pakistan
  39. Impact of wall movements on the location of passive Earth thrust
  40. Ecological risk assessment of toxic metal pollution in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, NW China
  41. Seasonal color matching method of ornamental plants in urban landscape construction
  42. Influence of interbedded rock association and fracture characteristics on gas accumulation in the lower Silurian Shiniulan formation, Northern Guizhou Province
  43. Spatiotemporal variation in groundwater level within the Manas River Basin, Northwest China: Relative impacts of natural and human factors
  44. GIS and geographical analysis of the main harbors in the world
  45. Laboratory test and numerical simulation of composite geomembrane leakage in plain reservoir
  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 11.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/geo-2020-0311/html
Button zum nach oben scrollen