This document outlines the process of building a 3D porosity model for the Buzurgan oil field in southern Iraq. The model characterizes and evaluates the reservoir porosity of the Mishrif Formation based on well log and core data from 9 wells. The total porosity of the Mishrif Formation ranges from 5-18% based on neutron and density logs. The model was built using Petrel software by inserting 7 zones and correlating log-derived porosity with core-derived porosity measurements. The results show the mB21 unit has the highest porosity values.

1. BUILDING A POROSITY MODEL
FOR BUZURGAN OIL FIELD
‫االس‬
‫م‬
‫ا‬
‫احمد‬ ‫هشام‬ ‫حمد‬

2. 1
Contents
Abstract ..........................................................................................................................................................2
Introduction .....................................................................................................................................................2
Aim.................................................................................................................................................................3
Tasks..............................................................................................................................................................4
Theory ............................................................................................................................................................5
Software..........................................................................................................................................................7
Data given.......................................................................................................................................................8
Data after treatment .......................................................................................................................................10
Work steps....................................................................................................................................................12
Results..........................................................................................................................................................15
Conclusion and Discussion .............................................................................................................................16
RF................................................................................................................................................................17

3. 2
Abstract
The Mishrif Formation is considered the main oil reservoir in the Buzurgan
oilfield, in southern Iraq. This study aims to characterize and evaluate the
reservoir porosity of the Mishrif Formation based on the interpretation of well
logs and core data. The data for nine wells have been analyzed and interpreted
by using multiple software.
Introduction
In the oil industry, reservoir modeling involves the construction of a computer
model for a petroleum reservoir to improve the estimation of reserves and make
decisions regarding the development of the field. The purpose of simulation
studies is to predict field performance under one or more producing schemes.
Observation of the model performance under different producing conditions aids
the selection of an optimal set of producing conditions for the reservoir.
The geological model is the main step of this study. It describes the
underground formations, explains fault or fold effect if found, and includes
petrophysical properties distribution (porosity, permeability, and water
saturation). Petrel, 2009 software was used to build this model by loading the
required data.
The study was built on using the logs and core data of nine wells of the
Buzurgan oilfield to determine the reservoir porosity of the Mishrif Formation.
eight of these wells (BU-3, BU-5, BU-6, BU-7, BU-10, BU-11, BU-13, BU-14,
BU-15) are located in the southern dome of the Buzurgan structure, whereas the
ninth well (BU-1) is located in the northern dome as the figure below.

4. 3
Aim
The aim is to construct a three-dimensional representation of a porosity model
for the Buzurgan oil field using multiple software that we going to mention later.

5. 4
Tasks
Building a porosity model for the Buzurgan oil field to characterize and evaluate
the reservoir porosity of the Mishrif Formation.

6. 5
Theory
Porosity is considered one of the important properties of hydrocarbon reservoirs,
due to it represents the ability and capacity of the reservoir to reserve fluids.
Mathematically, porosity is defined as the ratio of all pores volume to the bulk
volume of the rock. The porosity property of the Mishrif reservoir has been
determined based on the porosity logs (neutron & density) combined with the
core analysis.
Porosity model: Porosity data from the output of logging process interpretation
CPI of Mishrif reservoir, over the whole model (between node wells) were
created using the sequential Gaussian simulation as executed in Petrel.
Porosity is a fundamental property of reservoir rocks that determines
the potential storage capacity of fluids.
The total porosity of the Mishrif Formation was estimated based on the
interpretation of neutron and density logs. The results showed that the
total porosity or neutron-density porosity of the Mishrif Formation
ranges between (5% to 18%), and the MB21 unit of the formation has
the maximum value (about 18%).
The secondary porosity is defined as the type of porosity that developed after
the deposition of sediment because of the geological processes. This type of
porosity is more developed in carbonate rocks than in siliciclastics, and this is
can be related to the effect of the diagenesis processes on the carbonate rocks.
The effective porosity is considered more important than total porosity
because it represents the percentage of interconnected pore spaces that are
used in the estimation of recoverable hydrocarbon reserves.

7. 6
The porosity of the Mishrif Formation was calculated based on the interpretation
of different logs and using many equations.
The total porosity was calculated based on the neutron and density logs and
by using the following equation:
𝛟𝐍𝐃 = (𝛟𝐍 + 𝛟𝐃) / 𝟐
(Where: 𝛟𝑵𝑫 is the porosity that was calculated from neutron and density logs
(combination neutron-density log) (%), 𝛟𝑵 is the porosity that was derived from
neutron log (%), 𝛟𝑫 is the porosity that was derived from the density log (%).
The secondary porosity was calculated by the secondary porosity index (SPI)
𝐒𝐏𝐈 = 𝛟𝐍𝐃 – 𝛟𝐬
Where: SPI is the index of secondary porosity (%), 𝛟𝑵𝑫 is the total porosity
(%) and 𝛟𝒔 is the sonic (primary porosity) (%).
Effective porosity has been calculated by using the following equation that
represents the relationship between total and effective porosities
𝛟𝐞𝐟𝐟 = 𝛟𝐍𝐃 ∗ (𝟏 − 𝐕𝐬𝐡)
Where: 𝛟𝒆𝒇𝒇 is the effective porosity (decimal fraction), 𝛟𝑵𝑫 is the total
porosity (decimal fraction) and 𝐕𝒔𝒉 is the shale volume (decimal fraction).
To achieve accurate values of porosity, the log derived porosity values have
been correlated with the core measured porosity values that were calculated
from the core analysis. The values of core-measured porosity were plotted
against the log derived porosity (log effective porosity) on a linear-linear scale to
define the relationship between the two measured porosities.

8. 7
Software
 Petrel software 2009 has been used to build the 3D model. Petrel is a PC-
based workflow application for subsurface interpretation and modeling
 Didger is a digitizing and coordinate conversion software. You can digitize
maps, aerial photographs, graphs, or any other data with Didger
 Microsoft Excel is a spreadsheet developed by Microsoft,
It features calculation or computation capabilities, graphing tools, pivot tables,
and a macro programming language called Visual Basic for Applications. Excel
forms part of the Microsoft Office suite of software.

9. 8
Data given
1. CPI file contains porosity log reading.
Depth
Porosity log
Porosity log
reading

10. 9
2. Routine core analysis contains core lab measurement.
Type of porosity Depth
Vertical or horizontal porosity
Value

11. 10
Data after treatment
1. convert the CPI file into a Depth vs log reading using Didger software.
Digitizing

12. 11
2. transform the routine core file into an excel worksheet file.
Note: we take horizontal porosity only.

13. 12
Work steps
1. import the well log reading on the petrel structural model previous project.
2. insert zone 1 (Mishrif).

14. 13
3. Insert zone 2 (mA).
4. Insert zone 3 (mB11).
5. Insert zone 4 (mB12).

15. 14
6. Insert zone 5 (mB21).
7. Insert zone 6 (mC1)
8. Insert zone 7 (mC2).

16. 15
Results

17. 16
Conclusion and Discussion
The obtained results of the structural interpretation of the Buzurgan oil field
showed the following conclusions.
To achieve accurate porosity values of the Mishrif Formation, the neutron-
density (total) porosity values were first compared with the available core-
derived porosity values from core lab analysis.
The results showed the neutron density porosity matched the trend of core-
derived porosity but shows a bit lower values than the core-derived porosity.
The log-derived porosity values were correlated and integrated with the core-
derived porosity values, and the results showed a good correlation between the
porosity values. This correlation has been used to predict and estimate the
porosity values of the other stratigraphic units.
And as results show the mB21 had the most porosity value.

18. 17
RF
1. Al-Ameri, T.K., Al-Marsoumi, S.W. and Al-Musawi, F.A., 2015. Crude oil
characterization, molecular affinity, and migration pathways of Halfaya oil
field in Mesan Governorate, South Iraq. Arabian Journal of Geosciences,
Vol. 8, 7041-7058.
2. Al-Ameri, Thamer K., Al-Khafaji, Amer J. and Zumberge, J., 2009.
Petroleum system analysis of the Mishrif reservoir in the Ratawi, Zubair,
North and South Rumaila oil fields, southern Iraq. Geo Arabia, Petrolink,
Bahrain 14 (4), 91-108.
3. Al-Ali, M.M., Mahdi, M.M. and Alali, R.A., 2019. Microfacies and
depositional environment of Mishrif Formation, North Rumaila oil field,
southern Iraq. Iraqi Geological Journal, Vol. 52, No. 2, 91-104.
4. Al-Bulushi, N., King, P.R., Blunt, M.J. and Kraaijveld, M., 2009.
Development of artificial neural network models for predicting water
saturation and fluid distribution. Journal of Petroleum Science and
Engineering, Vol. 68, 197-208.
5. Dr.Dheiaa Alfarage lecture.