Available online at http://ijcpe.uobaghdad.edu.iq and www.iasj.net 

Iraqi Journal of Chemical and Petroleum 
 Engineering  

Vol.22 No.3 (September 2021) 1 – 9 
EISSN: 2618-0707, PISSN: 1997-4884 

 

Corresponding Authors:  Name: Qahtan A. Abdul-Aziz , Email: qahtan.aziz@coeng.uobaghdad.edu.iq, Name: Hassan A. Abdul-Hussein, Email: 

hassanaltee@coeng.uobaghdad.edu.iq  
IJCPE is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. 

 

Development a Statistical Relationship between Compressional 

Wave Velocity and Petrophysical Properties from Logs Data for 

JERIBE Formation ASMARI Reservoir in FAUQI Oil Field 

 
Qahtan A. Abdul-Aziz and Hassan A. Abdul-Hussein 

 
Petroleum Engineering Department, College of Engineering, University of Baghdad, Baghdad, Iraq 

 

Abstract 

 
   The Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing 

employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, 

more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave 

velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, 

which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity 

from petrophysical parameters and any pair of shear waves velocity, porosity, density, and fluid saturation in carbonate rocks. A 

strong linear correlation between P-wave velocity and S-wave velocity and between P-wave velocity and density rock was found. 

The resulting linear equations can be used to estimate P-wave velocity from the S-wave velocity in the case of both. The results of 

multiple regression analysis indicated that the density, porosity, water-saturated, and shear wave velocity (VS) are strongly related to 

Vp. 
         
Keywords: Porosity, Water saturation, Multiple regression, compressional wave 
 

Received on 18/04/2021, Accepted on 04/07/2021, published on 30/09/2021 
 
https://doi.org/10.31699/IJCPE.2021.3.1  

 
1- Introduction 
 

   Since carbonate rocks are essential components of the 

world's oil and gas reservoirs, further research into their 

physical characteristics is required. When combined with 

shear wave velocity (Vs), compressional wave velocity 

becomes a useful parameter for seismic analysis, 

lithologic identification [1,2], and pore fluid and pore 

pressure information [3,4,5].  

   A logical relationship between inputs (well log data) 

and outputs (Vp) is required for selecting input data. 

Many factors influence compressional wave velocity; four 

of the most significant in carbonate rocks are neutron 

porosity (NPHI) and bulk density (RHOB), s-wave, and 

pour water. In addition, weathering, alteration zones, 

bedding planes, and joint properties (filling materials, 

roughness, water, dip, and strike, and so on) all affect the 

sound velocity [6, 7,8,9,10,11].  

   Velocity is determined by the ratio of rock moduli to 

density. The elastic moduli by density term, dependent on 

rock properties such as fluid saturation and rock texture, 

produce velocity variations.  The theory of elasticity in 

rocks demonstrates that seismic waves propagate in two 

mechanisms, each propagating independently [12]. The 

work of rock moduli over density can estimate the seismic 

velocities, Vp and Vs., of isotropic rock material.  

 

   When the rock is saturated with water, compressional 

waves move faster than when it is dry or gas-saturated.  

   Shear waves have the opposite behavior, with shear 

velocities higher in dry or gas-saturated cases than in 

water-saturated cases [13,14].  At 10% saturation, the 

strongest decrease in intensity was observed. The 

volumetric pressure of the cracks and the sample 

decreased as the water saturation decreased, owing to 

water easing crack initiation and propagation [14].  

   The compressional wave (Vp) can estimate the water 

absorption characteristic as an objective parameter. 

However, water absorption, which is an effective rock 

index, is dependent on the mineralogy and porosity of the 

rock [15, 16].  

   Several researchers [18, 19, 20, 21, and 22] established 

empirical correlations to estimate compressional wave 

velocities. The amount of data collected has a big impact 

on empirical predictions. Predictions of this kind can also 

be used for effective planning.  

   Table 1 shows a range of relationships for predicting 

compressional waves from petrophysical parameters that 

have been published. As a result, using Petrophysical 

properties to predict compressional waves is more 

accurate, particularly in carbonates rocks.  

 

 

 

 

http://ijcpe.uobaghdad.edu.iq/
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https://doi.org/10.31699/IJCPE.2021.3.1


Q. A. Abdul-Aziz and H. A. Abdul-Hussein / Iraqi Journal of Chemical and Petroleum Engineering 22,3 (2021) 1 - 9 

 

 

2 
 

Table 1. Previous empirical relationships for 

compressional wave prediction 
year. Relationshiof Vp (km/sec) Reference 

1963 Vp/Vs=1.8   or Vp=1.8Vs (Pickett, 1963) 

1993 Vp= 5.45-−6.17∅ Gist et al 1993 
2003 𝑉𝑝 = 2.8𝜌 − 1.5752  Christensen and Stanly 
2012 𝑉𝑝 = 0.09𝑉𝑠 + 1.95  Morteza Karam et al. 
2019 𝑉𝑝 = 2.7037𝜌 − 2.3668  Siddharth Garia 
2019 𝑉𝑝 = 6.12 − 0.444𝑊𝑎  Siddharth  Garia,et,al. 

 

   However, there are some studies in existence that use 

multiple regressions and come up with encouraging 

results. This research focus is developing a single and 

multiple regression Vp prediction in carbonates by 

utilization of conventional well logs. 

   This study presents a regression analysis (statistical 

method) used to create a correlation to predict 

compressional waves and among effective petrophysical 

properties in dolomite formation (carbonate reservoirs) 

using JMP software.  

   The development of empirical models in which the 

measurable well logs can estimate Vp will also be 

outlined multiple regression for accurate prediction of Vp 

in the investigated reservoir. 

 

2- Material and Methods 
 

   Missan Oilfield is located in southeastern Iraq and close 

to the Iraq-Iran border. The field consists of three 

producing oilfields, namely Abu Ghirab, Buzurgan, Fauqi 

oilfields. Structurally Fauqi oilfield ranges about 30km × 

7 km with two domes in the north and south, respectively; 

NW-SE long axis anticline has two sets of reservoirs, 

Tertiary Asmari and Cretaceous Mishrif. Asmari reservoir 

is divided into three pay zones.A,B,and C . The pay zone, 

A of Asmari consists of the main dolomite (Jeribe 

formation). The interval of Jeribe is from depth 2996 to 

3044 m; this interval is the area of the present study.   

   There is no depth on the compressional waves (VP), but 

the (VP) is affected by the petrophysical properties.  

   The pay zone B is dolomite intercalated with sandstone, 

limestone, and thin shale. The pay zone C is mainly the 

sandstone, intercalated with a few dolomite, mudstone, 

and limestone.  

   The study presents multivariate regression analysis 

using the JMP software to develop a new correlation to 

predict compressional waves and among effective 

petrophysical properties of a productive carbonate 

(dolomite) section of Southeast Iraq (FAUQI A field – 

JERIBE formation).  

   The JERIBE Formation is considered one of the 

important reservoirs in the Misan oilfields.  

   It will also be discussed how to construct empirical 

models that use observable well logs to estimate Vp. The 

relationship between the input data and the outcomes was 

verified using data analysis.  

   Logs and core plugs can also calculate sonic wave data, 

but logs provide more data and a better representation of a 

reservoir [23].  

 

 

   Because of the complexity of the relationship between 

(Vp) values and all rock and fluid properties, only the 

most essential and observable rock and fluid properties 

(as determined by well logging data) were chosen as the 

model's key input parameters.  

   As a result, the parameters chosen should have a 

significant impact on Vp.  

   To determine which variables have the most significant 

impact on (Vp), as well as to compare the relationship 

between compressional wave velocity and other 

parameters and petrophysical logs (Vs., NPHI, RHOB, 

SW), it was discovered that there is a close relationship 

between compressional and shear wave velocity, 

especially in carbonate rocks.  

 

3- Calculations and Analysis 
 

   To model the output function, a collection of 

observations from the reservoir, such as well logs or core 

measurements, can be linked to the regression analysis 

result. Simple regression analysis or a multiple regression 

analysis may be used to accomplish this. The relationship 

between two variables is often modeled using simple 

regression analysis.  

   On the other hand, multiple regression analysis uses 

more than one predictor variable and can be more reliable 

when analyzing p-wave data [ 24]. Table (2) represents 

log data of physical rock properties and elastic waves (P- 

and S-waves) velocity for Jeribe formation.  

   The data in table 2 is the sample data used in the 

program (JMP) software to obtain the relationships 

between (VP) and the physical properties. 

 

3.1. Single Linear Regression Analysis 

 

   It can be seen in many engineering problems that 

specific data varies in an ascending or descending pattern. 

Many studies have shown, for example, that as Vp rises, 

the number of rock samples rises as well.  

   Therefore, it is worthwhile to investigate whether there 

is a link between -p-wave velocity and petrophysical 

parameters when estimating carbonate rocks using single 

linear regression.  

 

a. Relation between P-Wave And S-Wave Velocity  

  

   The compressional wave velocity is linearly related to 

the shear wave velocity as shown in Fig. 2 the high 

regression R
2
= 0.946. The scatter of even the mean values 

around the regression lines is due to complex depositional 

environments and digenetic processes. Reveals a strong 

correlation between the two velocities which enables 

estimation of one velocity having another one; the 

equation defines this relationship shown below: 

 

𝑉𝑝 = 2.122𝑉𝑠 − 1.195                                                  (1) 
 

 

 

 

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Q. A. Abdul-Aziz and H. A. Abdul-Hussein / Iraqi Journal of Chemical and Petroleum Engineering 22,3 (2021) 1 - 9 

 

 

3 
 

b. Relation between P-Wave Velocity and Porosity 
 

   A similar trend is presented between compressional 

wave velocity (Vp) and porosity (Ø), plotted in Fig. 3 The 

p-wave velocity is declining as the porosity decreases.  

 

 

 

   An important point can be concluded from this figure 

there is a scattering around the curve which can be 

attributed to the heterogeneity of carbonate in which the 

void proportions and pore size distributions within the 

rock fabric are complex; the relationship between p-wave 

and porosity gives R
2
 = 0.759, and equations of the      

relation are;  

 

𝑉𝑝 = 6.947 − 0.1542𝑁𝑃𝐻𝐼                                          (2) 
 

 
Fig. 1. Flowchart of the computer software to estimate Vp 

 

 
Fig. 2. Compressional wave velocity versus shear wave 

 
Fig. 3. Compressional wave velocity versus NPHI 

 

c. Relation Between The P-Wave Velocity and Bulk 
Density  

 

   The relationships between the P-wave velocity and rock 

density have been extensively investigated.  

   P-wave velocity increases linearly with a bulk density, 

as shown in Fig. 4.  

   The results revealed that there is scattering around the 

bulk density –velocity curves.  

   This can be attributed to the heterogeneity of carbonates 

formation, mixed with dolomite intercalated with 

argillaceous dolomite.  

   Good relation between p- wave velocity and bulk 

density with R2 =0.833 According to Equation (3) of the 

relation is; 

 

𝑉𝑝 = 4.284𝜌 − 6.22                                                      (3) 
 

 

 

 

 

 

https://www.sciencedirect.com/science/article/pii/S2096249520300351#fig3
https://www.sciencedirect.com/science/article/pii/S2096249520300351#fig3


Q. A. Abdul-Aziz and H. A. Abdul-Hussein / Iraqi Journal of Chemical and Petroleum Engineering 22,3 (2021) 1 - 9 

 

 

4 
 

Table 2. Results of elastic waves (p-and s-wave velocity) 

with physical properties for jeribe formation 

depth M 
vp 
km/sec 

Vs 
km/sec 

Density 
gm/cc 

NPHI٪ Sw٪ 

2996 5.25 3.04 2.65 0.3 56 

2297 5.25 3.04 2.6 11.6 42 

2998 5.89 3.34 2.76 10.6 68 

2999 5.88 3.33 2.78 10.6 82 

3000 5.97 3.37 2.77 9 92 

3001 5.9 3.34 2.82 7.4 100 

3002 6.07 3.42 2.87 4.9 100 

3003 6.12 3.44 2.85 4.2 100 

3004 6.01 3.39 2.85 5.1 100 

3005 6.2 3.48 2.85 4.6 100 

3006 6.09 3.43 2.81 5.4 100 

3007 5.74 3.27 2.83 6.6 100 

3008 5.74 3.34 2.74 12.7 85 

3009 5.7 3.25 2.77 11.5 67 

3010 5.81 3.29 2.73 12 65 

3011 6.09 3.43 2.84 7.3 100 

3012 5.87 3.34 2.84 9.3 94 

3013 5.78 3.29 2.84 8.3 56 

3014 6.17 3.47 2.83 5.7 100 

3015 6.08 3.42 2.79 8.9 56 

3016 6.03 3.4 2.85 4.7 100 

3017 6.06 2.41 2.92 1.3 100 

3018 6 3.39 2.86 4.2 100 

3019 6.02 3.4 2.82 6.8 100 

3020 6.02 3.4 2.83 6.3 100 

3021 5.94 3.36 2.79 9.5 100 

3022 6.03 3.4 2.83 6.2 100 

3023 5.75 3.27 2.76 11.2 65 

3024 5.15 2.99 2.64 18.1 45 

3025 5.34 3.08 2.65 9.6 45 

3026 4.64 2.75 1.95 12.9 19 

3027 3.95 2.43 2.3 16 22 

3028 5.32 3.07 2.97 5.8 64 

3029 5.6 3.2 2.81 7.4 67 

3030 5.64 3.22 2.73 9.6 82 

3031 3.56 2.25 1.71 18.8 20 

3032 4.78 2.82 2.54 12.2 32 

3033 5.37 3.09 2.65 10.8 71 

3034 4.15 2.52 2.37 14.3 34 

3035 5.82 3.3 2.79 7.5 68 

3036 5.58 3.19 2.7 12.6 74 

3037 5.53 3.17 2.7 13.2 45 

3038 5.58 3.19 2.86 13 54 

3039 5.98 3.38 2.82 6.1 73 

3040 5.77 3.28 2.77 9.3 78 

3041 5.46 3.13 2.75 7.7 41 

3042 5.53 3.17 2.8 4.2 43 

3043 4.57 2.72 2.56 11.8 37 

3044 3.52 2.23 2.38 21.1 33 

 

d. Relation between P-Wave Velocity and Water 
Saturation  

                                                                        

   The Compressional wave (Vp) of rocks is influenced 

when exposed to fluids. The plot below showing in the 

relation between p- wave velocity and water saturation 

values figure. Five, the water saturation increases, and p-

wave velocity increase with R
2 
=0.703 for dolomite.   

   According to equation (4), p-wave velocity increase 

linearly with increasing water saturation. The equation of 

the relation is: 

  
 𝑉𝑝 = 4.063 + 0.02043 ∗ 𝑆𝑊                                                             (4) 

 
Fig. 4. Compressional wave velocity versus Bulk density 

 

 
Fig. 5. Compressional wave velocity versus water 

saturation 

 

e. Comparison Between New Correlations with The 
Previous Empirical Correlations 

           

   In this section, the developed correlations have been 

compared with some previous correlations in Table. 1. 

The reason is to check whether this study has improved 

the literature. Table 1 for validation purposes as shown in 

Fig. 6 

 

 
Fig. 6. Comparison between Equations (2, 3) and other 

empirical relations to predict Vp. for dolomite rock 

formation from log data 



Q. A. Abdul-Aziz and H. A. Abdul-Hussein / Iraqi Journal of Chemical and Petroleum Engineering 22,3 (2021) 1 - 9 

 

 

5 
 

 
Fig. 7. Measured and predicted Vp using Multivariate 

Regression. (Eq. 2,3) and other empirical relations 

 

3.2. Multiple Regressions  

 

   Instead of equations (1, 2, 3, and 4), density (RHOB) 

and S-wave velocity vs. were used to add to the Vp model 

to improve the accuracy of the regression (prediction).   

   The predictive capability of multiple regressions is 

greatly improved when different variables are used to 

predict the P-wave velocity. Multiple variables are used to 

obtain a more accurate prediction of a variable.  

   Using NPHI (the calculation of neutron porosity), 

RHOB (the bulk density), and vs. (shear wave velocity) as 

separate variables, this program uses multiple regression 

to estimate compressional wave velocity in JMP.  

   It would help if you started by investigating the relation 

between compression wave velocity and the parameters 

you will be controlling (NPHI, RHOB, Sw, and Vs), then 

look for coefficients (a, b, c, d, e, and f) in the equation 

above to discover that:  

 

𝑉𝑝 =  𝑎 +  𝑏 ∗  𝑉𝑠 +  𝑐 ∗  𝑁𝑃𝐻𝐼 +  𝑑 ∗  𝑅𝐻𝑂𝐵 +  𝑒 ∗  𝑆𝑊       (5) 

 

   NPHI is neutron porosity expressed as a fraction, 

RHOB is bulk density in gm/cc, Vp is compressional 

wave velocity in km/s, and Vs. is shear wave velocity in 

km/s, and SW is a fraction. 

   It should be pointed out that using the complete set of 

input data as parameters in multiple regression models 

also leads to the use of all of the data available wells. A 

new model was retimed, and the resulted equation was:  

 
𝑉𝑝 = 0.1565 +  1.6087 ∗  𝑉𝑠 − 0.0350 ∗  𝑁𝑃𝐻𝐼 +  0.20409 ∗

 𝑅𝐻𝑂𝐵 + 0.0000403 ∗  𝑆𝑊                                                    (6) 

 

 

 

The suggested equation could be as follow:  

 
𝑉𝑝 =  𝑎 +  𝑏 ∗  𝑉𝑠 +  𝑐 ∗ (𝑉𝑠 + 𝑑)2 + 𝑒 ∗  𝑁𝑃𝐻𝐼 + 𝑓(𝑁𝑃𝐻𝐼 +
𝑔)2 + ℎ ∗ 𝑅𝐻𝑂𝐵 + 𝐽 ∗ 𝑆𝑊  

 

And the eq. 6 using the JMP software can be written as: 

 
𝑉𝑝 = 1.362 + 1.395𝑉𝑠 − 0.647(𝑉𝑠 − 3.179)2 − 0.034 ∗ 𝑁𝑃𝐻𝐼 +

0.002(𝑁𝑃𝐻𝐼 − 9.348)2 − 0.018 ∗ 𝑅𝐻𝑂𝐵 + 0.0014 ∗ 𝑆𝑊            (7) 

 

   The estimated Vp using the equation (7) provides a 
suitable match measured Vp as shown in Fig. 8 with R

2
 of 

about 0.93, while Fig. 9 presents the computed Vp using 

equation (7) and log compressional wave velocity from 

the Real field data versus depth for well 28 in FAUQI oil 

field.   

   Fig. 9 shows the continuous profiles of the actual and 

predicted compressional wave velocities, in which 

developed equation (7) has shown a reasonably accurate 

Vp prediction along the whole section.  

   Multiple regressions presented a robust correlation to 

predict compressional wave velocity from well log data. 

The multiple linear regressions of the presented variables 

show a strong correlation among Vp values predicted 

from well logging data.  

   All two methods, empirical and multiple regressions 

were applied log data to predict compressional wave 

velocity for the carbonate reservoir (dolomite).  

   The results show that the statistical method performs 

better than empirical models, which can be used only to 

obtain an order of magnitude for compressional wave 

velocity, as shown in Fig. 8, the excellent agreement 

between filed data and new correlation. For more 

validation, we compare the new correlation with field data 

and other mentioned correlations as shown in Fig. 10. 

 

 
Fig. 8. Plots of predicted Vp using Multivariate regression 

equation versus measured Vp from the log for well 

FAUQI-27 (Equation 7) 

 

 

 

 

 

 

 



Q. A. Abdul-Aziz and H. A. Abdul-Hussein / Iraqi Journal of Chemical and Petroleum Engineering 22,3 (2021) 1 - 9 

 

 

6 
 

 
Fig. 9. Measured and predicted Vp using Multivariate 

regression equation (Equation 7) 

 

 
Fig. 10. Comparison between new correlation (Eq. 7) and 

other correlation with the actual data for FAUQI-27 

 

3.3. Error Analysis 

 

   To further analyze the errors between calculated p-wave 

and actual p-wave, the following formula was adopted to 

calculate the absolute errors: Two criteria were used to 

evaluate the accuracy of this correlation, eq 7 compared to 

three correlations. These criteria are [25]; 

 

a. The Average Absolute Relative Error, Eq. 
  

𝐴𝐴𝑅𝐸 =
1

𝑁 
 ∑ [|

𝑋𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑(𝑖)−𝑋𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑(𝑖)

𝑋𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑(𝑖)
|] 𝑁𝑖=1 ∗ 100%                 (8) 

 

 

 

b. The Standard Deviation Error is Given By Eq.  
                  

𝑆𝐷 = √(
1

𝑁−1
 ∑ [|

𝑋𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑(𝑖)−𝑋𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑(𝑖)

𝑋𝑚𝑒𝑎𝑒𝑜𝑟𝑒𝑑(𝑖)
|] − 𝐴𝐴𝑅𝐸𝑁𝑖−1 )

2

            (9) 

 
   Fig. 11 shows that the new correlation provides the 

most accurate results than the other correlations. It gives 

(0.000451) standard deviation error compared with the 

other correlations, which give at least double standard 

deviation error, the new correlation also gives a lower 

average absolute relative error than the other correlations, 

as shown in Fig. 12. 

 

 
Fig. 11. SD% for the new and the other three correlations 

for FAUQI-28 

 

 
Fig. 12. AARE % for the new and the other three 

correlations for FAUQI-28 

 

4- Conclusions 
 
   This study presents a set of relationships to estimate the 
P-wave velocity and petrophysical properties based on 

well log data using the multiple regression analysis 

techniques.  

   In addition, a field case (FAUQI oil field, Jeribe 

formation), located in southeast Iraq, was conducted to 

correlate the compressional wave velocity with 

petrophysical properties.   

    

 

 

 



Q. A. Abdul-Aziz and H. A. Abdul-Hussein / Iraqi Journal of Chemical and Petroleum Engineering 22,3 (2021) 1 - 9 

 

 

7 
 

   The most significant difference between this study and 

previous studies is that the new model to predict (VP) 

depend on four-parameter (MR), including porosity, 

density, water saturation, and shear wave velocity but 

previous study, the relationship between the two variables 

are the essential parameters to influence P-wave 

prediction. The conclusion of this study can be 

summarized in the following points:  

 

1- It presents a more accurate correlation to estimate 
compressional wave velocity in Jeribe formation 

Eastern South of Iraq FAUQI oil field using 

conventional well log data. 

2- The analysis revealed that Vp could be estimated by 
correlation with Vs. and NPHI and SW Density. P-

wave shows a very strong correlation with this 

parameter, with a coefficient of 0.93 avoiding the 
dipole substitution; if dipole substitution is not 

available as the learned in the abovementioned 

regression, the predictors include Vp, which was 

developed to improve the accuracy of Vp prediction in 

the studied field.  

3- Therefore, this model can be applied accurately, given 
the observed consistency between the results of the 

model and data. predict carbonate concentration in the 

reservoir  

4- The Comparison of the results of three   correlation 
(Siddharth, Gist, and Pickett) with new model 

illustrate that the Multiple regression model-new 

equation (7) is the most accurate model for Vp 

prediction it was able to estimate the Vp for the 

validation data of Well-27 with an AARE of 0.019 

compared with an AARE of more than 0.033 for all 

available correlations 

 
 

Nomenclature  

 

Vp: compressional wave  

Vs.: shear wave velocity 

UV: ultrasonic velocities 

SW: Water saturation 

Wa: water absorption 

MR: Multiple regressions 

 

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االحصائية بين سرعة الموجة االنضغاطية والخصائص البتروفيزيائية  ةتطوير العالق
 باستخدام بيانات تسجيل االبار لتكوين، الجريبي مكمن االسمري في حقل الفكة

 
 

 قحطان عدنان عبدالعزيز و حسن عبدالهادي عبد الحسين
 

راققسم هندسة النفط، كلية الهندسة، جامعة بغداد، بغداد، الع  
 

 الخالصة
 

نعاشوعمليات  المكمن،تعد بيانات الموجة االنضغاطية مفيدة الستكشاف     وتوظيف التكسير  المكمن، الحفر وا 
اليجاد سرعة الموجة  أكثرالهيدروليكي، وخطط التطوير لمكمن معين. توجد بعض العالقات الالخطية المعقدة 

تطور هذه الدراسة العالقات اإلحصائية  االنضغاطية وذلك بسبب الطبيعة والسلوك المختلف للمعايير المختلفة.
بين سرعة الموجة االنضغاطية والخواص البتروفيزيائية من بيانات تسجيل االبار لنكوين الجريبي في حقل الفكة 
النفطي في الجنوب الشرقي من العراق باستخدام طريقة انحدار احصائية متعددة المتغيرات للتنبؤ بسرعة الموجة 

واص البتروفيزيائية وبين أي خاصيتين سرعة موجات القص والمسامية والكثافة وتشبع االنضغاطية من الخ
وجود ارتباط خطي قوي بين سرعة الموجة االنضغاطية  الدراسةية. وفد استنتجت نالسوائل في ألصخور الكاربو 

ت الخطية وبين سرعة الموجة االنضغاطية وكثافة الصخور. يمكن استخدام المعادال القصية وسرعة الموجة
. أضهرت نتائج أحدهما بداللة االخرىالناتجة لتقدير سرعة الموجة االنضغاطية من سرعة الموجة القصية 

تحليل االنحدار المتعدد إلى أن الكثافة، المسامية والتشبع بالماء وسرعة موجة القص ترتبط ارتباًطا وثيًقا 
 بالسرعة للموجةاالنضغاطية.

 
 الموجة االنضغاطية المتعدد،االنحدار  الماء،تشبع  المسامية،الكلمات الدالة: