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Project Background
(State your research mission statement here in no more than 250 words) Describe the problem that your project is intended to address or solve. This is the "hook" that draws people into your proposal and helps them align with your ultimate goal behind your proposed research and expected outcomes؟
من النص التالي بالغة الأنجليزية




reservoir model objective is to describe the dynamic behavior of a reservoir in order to predict its future performance under different development and production (exploitation) strategies:
In development stage:
1. Estimating the OIP
2. Selecting the field development plan.
3. Selecting the optimal drilling plan.
4. select the basic design and size of the production facilities
5. Computing the production profiles plan (oil, gas, and water)
6. Estimating the oil and gas technical reserves
7. evaluate economic/technical implementation of IOR/EOR processes to increase the final recovery
8. Economic evaluation
In producing stage:
1. Calibrating the geological model
2. Identifying the un drained oil/gas bearing zones
3. Locating infilling wells
4. Optimizing the injection plan
5. Optimizing the well construction and completion design
6. Optimizing the production plan and the final recovery
7. Updating economics.
2.2. Reservoir Modeling Database
Modeling Raw data are classified in many forms. Below are illustrated two of classifications. In first one data are considered as two types – spatial data (to create a 3-D geometry model) and properties data.

Fig. 2.1 Data Input for Reservoir Modeling
In general, the data base consists of:
1. Well Log Data: (surface tops, rock type, φ, Κ) by zone
2. Seismic data: The results of seismic interpretation.
3. Stratigraphic interpretation data: for layering and definition of the continuity within each layer of the reservoir)
4. Geological data: Coordinates and boreholes, lithostratigraphy of drilled well sections and etc.
5. Laboratory fluids analyzes data: (oil, gas, water, condensate);
6. Field exploitation data: production, WC, GOR, temperature, BHP, etc.
The overall aim in reservoir model design is: to capture knowledge of the reservoir in a quantitative form (data) in order to evaluate and engineer the reservoir.
This definition combines knowledge capture, the process of collecting all relevant information, with the engineering objective – the practical outcome of the model:
• Making estimates of fluid volumes in place,
• Scoping reservoir development plans,
• Optimizing fluid recovery (usually for IOR/ EOR schemes).





2.3. Reservoir Modeling Workflow
Data derived from various sources are integrated by deterministic or geo statistical methods to construct the model.
Methodology for construction and using reservoir models
1. Define the problem.
2. Specify the objective.
3. Study the data available.
4. Determine the computing facilities available.
5. Specify the economic and social constraints.
6. Choose models.
7. Calibrate the model.
8. Evaluate the performance of the model.
9. Use the model for prediction purposes.

Figure (2.2) Workflows for integrated reservoir modeling
2.4-Static Geological Modeling
A static reservoir model is the one incorporating all the geological features of reservoir (i.e. structural, sedimental, petrophysical, etc.).
In general, the static model of a reservoir is the final integrated product of the structural, stratigraphic and lithological modeling activities.
A static reservoir study typically proceeds through four main stages.
2.5. Structural Modeling
Geometry modeling: the integration of soft information such as sedimental and depositional models, faults transmissibility etc. in two steps: development of a structural representation of the reservoir, and subsequently its subdivision, or “discretization” to provide control for the analytical computations within the numerical models used in the predictive modeling.
Structural modeling: Construction of the structural and geometrical representation of reservoir, suitable for visualization, (top structural map and faults) by integrating geophysical surveys interpretations and the available well data. It serves to define the spatial distribution of rock-properties.
The 2D structural maps are generated after field areal seismic interpretation and well log correlation. They are made between specific stratigraphic intervals.
Building a fault model is one of the manual steps in a modeling:
• Fault tends to be incomplete, e.g. faults may be missing in areas of poor seismic quality.
• Faults may not be joined due to seismic noise in areas of fault intersections.
• Horizon interpretations may stop short of faults due to seismic noise around the fault zone; Horizon interpretations may be extended down fault planes.
Although models made from such ‘raw’ seismic interpretations are honest reflections of that data, the structural representations are incomplete and, it is argued here, a structural interpretation should be overlain on the seismic outputs as part of the model design.
A structural model workflow is as follows:
1. Determine the structural concept (element)
2. Input the fault sticks and grid them as fault planes (Fig. 2.3a)
3. Link faults into a network consistent with the concept.
4. Import depth-converted horizon picks as points (Fig.2. 3c)
5. Edit the fault network to ensure optimal positioning relative to the raw picks; this may be an iterative process with the geophysicist, particularly if potentially spurious picks are identified
6. Grid surfaces against the fault network (Fig. 2.3d).

Fig. 2.3 A Structural Build Based on Fault Sticks from Seismic (A), Converted into A Linked Fault System (B), Integrated with Depth Converted Horizon Picks (C) To Yield a Conceptually Acceptable Structural Framework Which Honors All Inputs (D).




Fig. 2.4 Well Correlation and Structural Modeling


Fig. (2.5) A Structural Model Showing Faults And Layering


 





Chapter 3

Building A Static Model
(Petrel Software)


 
3.1 Introduction
The best technology for making reservoir performance predictions today is to model fluid flow in porous media using reservoir flow simulators.
The most important, from a business perspective, is establishing that the problem has economic importance.
3.2 Stratigraphic Modeling
(Defining the internal structure or internal layering from open hole logs and seismic: major boundaries)
Sedimentary geologic environments are modeled by creating surfaces that define the internal divisions (lithological units) within the model boundaries.
Construction of individual surfaces generally proceeds by one of three methods:
1) Using the borehole observations to create a triangles defining a surface
2) Applying surface generation and contouring procedures to borehole observations
3) Developing a series of interpretive cross-sections between boreholes

Fig. 3.1 Seismic Image
Correlation usually begins with markers picked from well data – well picks.
Important information also comes from correlation surfaces picked from seismic data.
If multiple stratigraphic correlations are available these may give surfaces which intersect in space. A selection process of surfaces is required as not all these surfaces are needed in reservoir modeling. As a guideline, the ‘correct’ correlation lines are generally those which most closely govern the fluid-flow gradients during production. The choice of correlation surfaces used hugely influences the resulting model architecture, as illustrated in Fig. 3.2

Fig. 3.3 Stratigraphic Correlations of The Observations in Three Wells.


Fig.3.4. Example of a 3D Stratigraphic Model
3.3 Faults Modeling
A fault and fracture system can also divide the geological model into subunits in which the internal structure can be defined independent of the other subunits in the model.
Rock on opposite sides of a fault may have similar or different characteristics depending on the type of fault and the depositional processes. Faults may provide preferential conduits for fluid flow, or they may act as barriers to flow. They typically add anisotropy to property distributions required by the numerical models. Faults can be defined by adding additional surfaces to the existing stratigraphic models.
The final step in building the structural model is to define the thickness and geometry of the layers within each zone in the 3D geological model. These layers, in combination with the pillars of the 3D grid, define the model cells in the final 3D grid.

Fig.3.5. Example of a 3D Geological Model
3.4 Rock (Facies Or Lithology) Model
Rock modeling is capturing contrasting rock types identified from lithology, sedimentology, and petrophysics and representing these in 3D.
This classification into facies is a convenient way of representing the geological characteristics of a reservoir, especially for the purposes of subsequent three-dimensional modeling. geostatistical tools are used to build facies model ( data trends and combine them with sedimentary environment to build realistic presentation of reservoir).

Fig.3.5. Example Of a Stochastic Model of Facies

3.5 Petrophysical modeling (porosity, water saturation, and permeability).

Facies model is used to build properties model.
Property distributions are generally modeled by applying discretization methods to subdivide the reservoir into a series of into discrete “volume elements” that are usually regular cubes. We use core / log data and the spatial correlations or statistics (variograms) to generate a rock type model and property models
A quantitative interpretation of well logs determines main petrophysical characteristics of the reservoir rock core data represent the essential basis for the calibration of interpretative processes.

Figure (3.6) A petrophysical model

Each cell of the 3D geomodel has exact continuous petrophysical facies properties; these properties represent the reservoir parameters as: Porosity, Permeability, Net/Gross Ratio, Bulk Volume, Fluid Saturation and etc.
We focus on modeling porosity (ϕ) and permeability (k) as these are the essential parameters in the flow equation (Darcy’s law), but the methods discussed here for handling ϕ and k can also be applied to other properties, such as formation bulk density (ρb) or Volume fraction of shale (Vshale).
Table 3.1 lists the most commonly modeled rock properties.

Table 3.1 List of Properties Typically Included in Reservoir Models

Permeability is generally the most challenging property to define because it is highly variable in nature and is a tensor property dependent on flow boundary conditions.
Permeability, in general, a non-additive property, that is:
, ……………………………… (1)
In contrast porosity is essentially an additive property:
, …………………..……………….. (2)
Where, ΔV is a large scale volume.
Which reservoir or rock unit do we want to average?
The best term for defining the rock units in reservoir studies is the Hydraulic Flow Unit (HFU), which is defined as representative rock volume with consistent Petrophysical properties distinctly different from other rock units.

Figure (3.7) Porosity Map (On the Left) And Permeability Map (On the Wright)
3.6 Handling Statistical Data
Our overall aim in reservoir modeling is to estimate and compare distributions for data (observations).
We must always remember not to confuse observations (data) with the model (a hypothesis) and both of these with the “ground truth” (an unknown).
This leads us to one of the most important axioms of reservoir modelling:
Data ≠ Model ≠ Truth
Of course, we want our models to be consistent with the available data (from wells, seismic, and dynamic data) and we hope they will give us a good approximation of the truth, but too often the reservoir design engineer tries to force an artificial match which leads inevitably to great disappointment.
A common mistake is to try to manipulate the data statistics to obtain an apparent match between the model and data. From probability theory we can establish that ‘most’ values lie close to the mean.
What we want to know is ‘how close’ – or how sure we are about the mean value. The fundamental difficulty here is that the true (population) mean is unknown and we have to employ the theory of confidence intervals to give us an estimate.
3.7 Upscaling

A) Upscaling of the geological models
Upscaling: the process of transferring information between scales.
It is basically a process by which a very heterogenous region of the reservoir rock described with a huge amount of “fine grid cells” is replaced by an equivalent less heterogeneous region made up of a number of single coarse-grid cells. The “upscaled geological model” must, however, maintain the same storage and transport properties of the reservoir rock described with detail by the “fine geological model”.
The upscaling process, therefore, is essentially an averaging procedure in which the static and dynamic characteristics of a fine-scale model are approximated by those of a coarse-scale model.

Figure (3.8) Conceptual Illustration Of The Upscaling Process

For upscaling we require representative geological models in which the geological elements (e.g. layers of sandstone, siltstone, and limestone) are represented as properties relevant for fluid modeling – porosity, permeability, capillary pressure functions, etc.
This process involves some simplification of the rock architecture, as we aim to group the rock elements into flow units with similar properties.
Upscaled reservoir properties are connected with the model length-scale.
Problems especially arise due to complex fault block geometries. The construction of 3D grids suitable for reservoir simulation requires significant manual editing. There are several reasons for this:
• The grid resolution in the geologic model and the simulation models are different, leading to missing cells or miss fitting cells in the simulation model. The consequences are overestimation of pore volumes, possibly wrong communication across faults, and difficult numerical calculations due to a number small or “artificial” grid cells.
• The handling faults is difficult. When using grids with stair-step faults special attention must be paid to estimation of fault seal and fault transmissibility.
• Regions with fault spacing smaller than the simulation grid spacing give problems for appropriate calculation of fault throw and zone to zone communication.
• Flow simulation accuracy depends on the grid quality, and the commonly used numerical discretization schemes in commercial simulators have acceptable accuracy only for ‘near’ orthogonal grids.
B) Upscaling of rock properties
Upscaling flow properties is to estimate large-scale flow behavior from smaller-scale measurements.
Upscaling involves some form of numerical or analytical method for estimating effective or equivalent flow properties at a larger scale given some set of finer scale rock properties.
Typically, we start with a few measurements of rock samples (length scale ~3 cm) and some records of flow rates and pressures in test wells (~100 m). Our challenge is to estimate how the whole reservoir will flow (~1 km).
Flow properties of rocks vary enormously over a wide range of length scales, and estimating upscaled flow properties can be quite a challenge.
The upscaled permeability is defined as the permeability of an homogeneous block, which under the same pressure boundary conditions will give the same average flows as the heterogeneous region the block is representing.
The upscaled block permeability could be estimated or it could be measured at the larger scale (e.g., in a well test or core analysis), in which case the fine scale permeabilities need not be known.
Due to its highly variable nature, some form of averaging of permeability is generally needed. For the general case, where an average permeability cannot be assumed, a numerical method must be used to calculate the block permeability.









Chapter 4
Practical part
(Static Modelling)

 
Chapter 4: Case Study
4.1 Introduction
In the study, the main data for the construction of the static model have been gathered and used to run the simulation process using Petrel software.
1- Start up Petrel:
File –new project.
Projection (setting project Coordinate & Unit systems):
File- project set up- project setting- coordinate reference system (CRS)- select- click on the place of study on map – filter by – filter – scroll to choose the area – ok. (The area highlights on the map). Unit system: it is only used for a limited number of activities in Petrel, depth conversion, volume calculations and simulations.

Figure (4.1) Settings for “New project”


4.2 Preparing Input Data:
Collection raw data and put in the specific folders in excel file of four sheets (in sequence):
1- The well heads: this sheet includes:
Well name X Y KB MD Symbol
1
2
3

2- Deviation survey (for each well)
MD inclination azimuth


3- Well (outcrops) logs : this sheet includes:
Well name H,m K, % U, % Thu, % Total GR Well name H, m K, % U, % Thu, % Total GR
1 2
2 2

4- Formation tops (surfaces)
Well name Surface, horizon MD
1
2
3
4
4.3 Creation of ASCII (Las) Files in Sequence:
A) The Well Heads:
Create (headers folder) Transform excel file to text file: highlight well heads – copy – open (headers folder) – right click – new text document (call it: heads) – open it – past – save.
Transform text file to las (ASCII): File – save as – save as type (choose All files) – file name (add. ASCII) to be (heads. ASCII)- save. So, we see two files: heads & heads. ASCII.


Figure (4.2) The Well Headers Data File Open in A Notepad Window
B) Deviation Survey:
Create (deviation folder)
Transform excel file to text file: highlight hole the table – copy it - open (deviation folder)–right click –new text doc (call: deviation) – open it – past – save.
Transform text file to las (ASCII): File – save as – save as type (choose All files) – file name (add. ASCII) to be (deviation. ASCII) - save. So we see two files: deviation & deviation. ASCII.
Make so for each well.


Figure (4.3) The Well (A10) Deviations Data File Open in A Notepad Window
Table (4.1) Input Data for Petrel Software


c) Well (outcrops) logs data :
Create (Well logs folder)
Transform excel file to text file: for each well highlight the logs (without first column: well name) - copy - open (well logs folder) - right click - new text document (call it: 1) – open it – past – save.
Transform text file to las (ASCII): File – save as – save as type (choose All files) – file name (add. ASCII) to be (1. ASCII) - save. So, we see two files: 1& 2. ascii.
Make so for each well.

d) Formation tops
Create (Tops folder)
Transform excel file to text file: highlight hole the table – copy it - open (Tops folder) – right click – new text document (call it: tops) – open it – past – save.
Transform text file to (ASCII): File -save as-save as type (choose All files)- file name (add.ASCII) to be (tops.ASCII)-save. So we see two files: deviation & deviation.ASCII.
4.4 Data Import (Uploading):
a) The well heads:
Insert – new well folder
Go to the Insert pull down menu and select New Well Folder.
Right-click on Wells Folder, then select Import (on Selection) ...
Select Well Heads (*. *) as files of type and click– match – ok for all.


Figure (4.3) The Import File form


Figure (4.4) The Import Well Heads form

The display of the wells in the 3D window may be toggled using the checkbox to the left of the Wells folder. The wells will appear in the 3D window as vertical sticks once you check the Wells folder, as seen in figure 4.5.

Figure (4.5) The Wells Displayed In A 3D Window
By selecting Settings from the context menu when you right-click on the wells folder, you may modify the settings for the wells.
The settings for the well form are depicted in Fig. 4.6. Make sure the Style tab is turned on. Change the Pipe Width setting on the Path tab to a value other than the default, such 100.


Figure (4.6) The Settings for 'Wells' form on Path tab



Figure (4.7) The Wells Displayed in A 3D Window After Changing the Settings
How to Insert a New well header a new well may also be established by selecting Create New Well from the pull-down menu when the right mouse button is clicked on the well folder or a subfolder inside the well folder. You may also select New well from the Insert option.


Figure (4.8): New Well Creation Form
 
If the input figures differ from the project units, they may be converted. Enter the required data and then click OK. Later, these characteristics can be modified using the Well Manager program or the Settings window of the well.

Figure (4.9) The Import File form

Figure (4.10) The Match Filename and Well form
When the Import Well Path/Deviation window pops up, click the Input.
4.9 3D Grid Construction and Meshing Technique
A 3D grid construction is the first step to build the 3D model and is a network of horizontal and vertical lines used to describe a three-dimensional geological model. Each box is called a grid cell and will have a single rock type, one value of porosity, one value of water saturation.

Figure (4.11) the boundary of the study area
4.10 Fault Modeling
Because they provide the geological and drilling engineers with extra information that helps them better comprehend the position of trapped hydrocarbon and analyze the optimal drilling scenario during the operation, faults are regarded as one of the most significant structural geology characteristics.
The most important phase in a structural model is fault modeling. There have been vertical, inverse, and curved faults produced. Truncated faults (Y faults), series faults, conjugate faults, crossover faults, stair case faults, etc. are all included in the fault system. The fault plane is between the intersecting lines' base surface and base surface, respectively. To create a 3D flaw in the structural model, these lines are shown in three dimensions.

Figure (4.12) Meshing Process with Reservoir Boundary and Exciting Faults in The Study Area


Figure (4.13) Fault Sticks In 3D View in The Study Area
There are eleven faults which made geometrically and structurally in this study which represent with layers as three dimensional in Figure (4.14)




Figure (4.14) Complete Fault Model With 3D Planes

4.11 The Pillars
The process of creating the grid, which serves as the framework for all modeling, is known as pillar gridding. The top, middle, and base skeleton grids make up the skeleton grid [3]. The three-dimensional grid systems of (100) grid along the X-axis and (100) grid along the Y-axis were utilized to depict the grid that was employed in the reservoir. The size of the grid was determined by the field's size and used to describe how the petrophysical parameters varied. The primary skeleton in the top, mid, and base skeletons is the outcome of the pillar gridding, as seen in figure (4.15) A 3D grid or three skeletons of the reservoir model are seen in this illustration.

Figure (4.15) The Skeletons of The Reservoir.
4.12 Horizon modeling
Inserting the stratigraphic horizons into the pillar grid while respecting the grid increment and the faults is the last stage in structural modeling. The vertical stacking of the 3D grid in Petrel was defined by the Make Horizons process stage. This method generates 2D surfaces in a real three-dimensional manner while taking connections between the surfaces into consideration. The primary components of the reservoir are represented by the horizons in the figure (4.16)


Figure (4.16) Shows the Horizons of The Main Units of The Reservoir
4.13 Making Layers
The thickness and direction of the layers between the horizons of the 3D Grid must be determined as the last stage in creating the structural framework. The cells of the 3D Grid to which characteristics are allocated during property modeling are defined by these layers and the pillars.
Accurate modeling of layered volumes is necessary in modern geology. The most effective way to constrain geology at depth is progressively three-dimensional (3-D) geologic models. Depending on their petrophysical characteristics, each unit in the field has been separated into many strata.
Figure (4.17) show 3D well modeling that takes into consideration the reservoir model from various angles.


Figure (4.17) Over View Reservoir Model of Horizon with The Consideration of Faults, Wells and the layering in the Formation.

4.14 well logs
The 3D grid cells that the wells penetrate are averaged using the Scale up well logs method. Per up scaled log, one value is given to each cell. These cells serve as the foundation for property modeling later on. A 3D grid cell layout is utilized to represent the volume of the zone when modeling petrophysical parameters. For well logs, the cell thickness will often be greater than the sample density. As a result, before any modeling based on well logs can be done, the well logs must be scaled up to the resolution of the 3D grid. The blockage of well logs is another name for this procedure.
There are several statistical techniques for scaling up, including (arithmetic, geometric and harmonic methods). The present model's porosity and water saturation parameters have been scaled up using the (arithmetic average). Figure 4.18 depicts the scaling of water saturation and porosity for the Formation model.

Figure (4.18) Scale Up of Porosity and Water Saturation for A10.
4.15 Petrophysics
The process of assigning petrophysical property values (such as porosity and water saturation) to each cell of the 3D grid is known as petrophysical property modeling. For simulating the distribution of petrophysical attributes in a reservoir model, Petrel provides a number of methods. Geostatistical techniques were used to build the petrophysics model. On the basis of the findings of porosity and water saturation values that have been corrected and interpreted in the IP software, porosity and water saturation models have been constructed. The amount of available data was taken into consideration while choosing a statistical approach, and the sequential Gaussian Simulation procedure was utilized.
Figure (4.19) below depicts the reservoir's petrophysical model, which was created and constructed in accordance with petrophysical parameters (porosity, permeability, water saturation, etc.). The purple hue represents the zones.

Figure (4.19) Petrophysical Model for The Study Reservoir
The results of the petrophysical model including porosity model and the permeability model.
4.16 Porosity Model
Porosity logs (density, neutron, and sonic) logs have been adjusted to 3D grid cells, upon which a porosity model has been built. One well-known geostatistical technique that is employed as a statistical technique to create a porosity model is (SGS). Histogram windows have been used to identify the original log data's petrophysical characteristics and to scale up the log data in order to verify the correctness of the final 3D porosity model. Figure (4.20) displays a 3D depiction of the tertiary reservoir's porosity model.

Figure (4.20) 3D Porosity Model for The Study Field
4.17 Permeability Model
By studying well log data, the FZI approach has been utilized to calculate the permeability of cored wells. The optimal permeability distribution in a geological model was obtained by using a geostatistical methodology, which is employed as a statistical method (SGS).
In order to verify the quality of the final 3D porosity model, the histogram window has been used to identify the physical characteristics of the original log data and to scale up the log data. The 3D depiction of the permeability model for the tertiary reservoir is shown in Figure (4.21).
Figure (4.21) Shows Permeability Model of The Interest Zone
4.18 Water Saturation Model
For tertiary units in the oil field, a water saturation model has been built after upscaling well logs for water saturation. The geostatistical methodology employed in the Sw model and the SGS porosity model is the same. Histogram windows have been used to identify the original log data's petrophysical characteristics and to scale the log data up in order to assess the water saturation model's correctness. Figure (4.22) depicts a 3D depiction of the field's water saturation.


Figure (4.22) 3D Water Saturation Model for the Field
4.19 Net to Gross Modeling
Due to the clarification of the penetrated geologic section's high hydrocarbon content and optimal reservoir quality to use for producing intervals in the reservoir, net pay is a crucial metric in the reservoir's features. Because non-reservoir rocks are not taken into account, Net Pay shows facilities reservoir simulation. Utilizing cutoff techniques on Petrophysical well records, the net pay zone may be calculated. Cutoff has a specified value for the characteristics of the formation, and generating zones are not taken into account.
4.20 Volumetric calculations
Finding the correct original oil in place (OIIP) value using the volumetric approach is the primary goal of the reservoir volume estimation process.
The primary reservoir volume estimation utilizing software calculations for oil and gas zones is shown in Table (4.4).


Table (4.2) Summary of The Reservoir Volume and The Value Of OOIP
Properties in oil interval
Water saturation (Sw) 0.46
Gas Saturation (Sg) 0.15
Oil Saturation (So) 0.39
Bo (formation vol. factor)
[rm3/sm3] 1.229
Recovery factor oil 0.36
Gas cap volume m^3 2362
Aquifer volume m^3 12436
OOIP m^3 456987
Recoverable oil m^3 164515.32
Total bulk volume m^3 986425

Some geological maps have been prepared as result of the petrophysical model for the distribution of the porosity and the thickness of the study area as showed in figure (4.23 and 4.24) below.



Figure (4.24) Porosity Distribution Map for The Study Area


Figure (4.25) Thickness Distribution Map for The Study Area
4.21 The Seismic Model
By numerically calculating the displacement detected by a collection of seismic receivers as a result of seismic waves traveling through a geological model, forward seismic modeling is a geoscience method for creating synthetic seismograms. Geoscientists can improve the interpretation of seismic data, particularly in geologically complicated places, comprehend seismic wave propagation, and test seismic processing and inversion methods by creating synthetic seismic sections.


Figure (4.26) 3D Seismic Model
4.22 Fluid Model and Rock Physics Function
Black oil fluid models may be produced using a fluid model technique. For each of the fluid phases, these models are specified by a number of parameters, including viscosity, density, and volume formation variables. In most cases, these characteristics are recorded as tables that solely depend on pressure. Using fluid samples from bottom holes or reconstituted reservoirs, reservoir fluid research is the most trustworthy method for obtaining this data. Correlations may be used to determine the characteristics of oil, water, and gas as well as the relationships between gas and oil in the absence of such data.
In the Make fluid model step, information about the starting reservoir conditions must also be input. This enables the estimation of the initial fluid distribution in the reservoir together with the fluid characteristics and saturation functions. Compressibility of rocks.


Table (4.3) Fluid Properties

reservoir Oil properties Gas properties
properties Reservoir pressure Reservoir temperature Density S.g GOR Density S.g Gas FVF
Values 4600 220 54.15 0.86 1400 2.6 lbm/Ft3 0.00423
Unit psi F lbm/Ft3 - Scf/stb lbm/Ft3 0.76 SCF / FT3





Figure (4.27) Shows the Relationship Between Oil Viscosity and Pressure

Figure (4.28) Shows the Relationship Between oil viscosity and pressure

Numerous saturation and pressure functions are employed in simulation to reflect the physics of the fluids, the rock, or the interaction between the two. The functions that represent the fluids' physics are created using the Make fluid model method. Users can produce saturation functions and rock compaction functions by using the Make rock
physics functions technique to construct functions that capture the physics of the rock and the interaction between rock and fluids. The variety of rock physics features made available inside this procedure will be expanded in next Petrel versions. The Make saturation functions procedure from earlier iterations of Petrel is included into and replaced by the Make rock physics functions process.


Figure (4.29) Shows the Relationship Between Gas Relative Permeabilities and Water Saturation
Figure (4.30) Shows the Relationship Between Oil Relative Permeabilities and Water Saturation

 
Conclusions
The following conclusions might be drawn from the study of reservoir dynamics and examination of petrophysical properties:
1. A structural model of the reservoir has been created using the petrel program. According to this hypothesis, the field is a cylindrical anticlinal fold with some domes at its northern, which are separated by depressions and random faults.
2. horizons that have been made to determine the extend of the geological layers trough the reservoir to determine the thickness of the pay zone.
3. Porosity, permeability and water saturation models have been used for this project to determine the best location in which hydrocarbon are accumulated.
















Recommendations
The main recommendations are the following:
1: to apply a wide rage of reservoir simulations in the future to take into account the well of interest and to choose the best locations where the best future wells should br placed.
2: to maximize the oil and gas production in a cost affective manner by determination of the best locations where the hydrocarbons are exict.
3: one of the most important recommendations is to consider the dynamic model in a future work.
4: to validate the results that obtained after the simulation of the model. 
References