Building of a Static Model for Jeribe Formation in Jambour Oil Field

Abstract


Geological Model
The definition of a static geological model of the reservoir rock is an essential part of reservoir studies because the proper building of 3-D static models for dependable reservoir evaluation is necessary for effective management (Bruhn et al., 2003). A geological model will be used to obtain the most accurate characterization of reservoir properties. Geological modeling uses the best description for reservoir features and subsurface quantities based on reservoir characteristics. Through reservoir properties, recognizing and covering most geological features relating to porosity, permeability, water saturation, types of rocks and barrels (faults as well as folds), and would then knowledge about some restrictions of subsurface data needed to calculate the distribution of these properties are necessary (Doyle and Sweet, 1995). The technique of creating a mathematical representation of any threedimensional surface of an object using specialized software is known as 3D modeling. A 3D model is a name given to the product (Branets et al., 2008). In general, a model is a representation of a real-world element or process. A model is good if it correctly characterizes the real-world property or properties relevant to the investigation.
For example, a 3-dimensional geological model of a region is useful if it yields real-world values in reservoir simulations and modeling.
Based on the definition above, different models will produce the greatest results for several reasons. A geological model is a spatial visualization of the subsurface distribution of sediments and rocks. The model is often exhibited as a 2D cross-section, whereas it is increasingly presented as a digital 3D model (Schlumberger, 2017).

Study Area
Jambour field is a major area of interest in northern Iraq, southeast of Kirkuk, located on the same axis as the Kirkuk, Bai Hassan, and Khabbaz structures. It extends from the northwest to the southeast (Baban et al., 2018). Fig. 1 represents the field's location. This field's longitude coordinates are between 44° and 45°, and its latitude coordinates are between 34° and 35°. The field is a narrow asymmetric fold (anticline) about thirty-five kilometers long and 4.5 kilometers wide. The carbonate tertiary Jeribe reservoir within Kirkuk is one of northern Iraq's most major productive reservoirs, with massive oil and gas resources (Alatroshe et al., 2018). Jeribe Formation has two main faults at its boundary, parallel to the fault axis with nine mini faults. The Jeribe Formation is composed of marly limestones that are crystalline and partially dolomitized. Anhydrite with blue marl streaks can be found locally (Sahib and Al-Dulaimi, 2022). Zaidky (2021) studied the porosity of the Jeribe reservoir, and he discovered that the porosity of the Jeribe formation varies between 13.0 and 24.3 percent, with an average of 17.3 percent. Qader and Ali (2022) studied the reservoir characteristics of the Jeribe reservoir in Kor Mor Gasfield and realized that the thickness of the Jeribe Formation varies from 30 to 50m according to the studied wells, and the porosity of the Jeribe reservoir varies between 0.01 and 0.23 with an average of 0.10 (Qader and Ali, 2022).
This research aims to establish a static geological model for four wells from the Jeribe reservoir in the Jambour oil field (W-37, W-48, W-49, and W-50). A 3-D static model has been created with Petrel 2020.3 software and contains structural models (structural maps), fault modeling, facies modeling and petrophysical property distributions (effective porosity, permeability, net to gross, and water saturation).

Materials and Methods
Petrel 2020.3 software generated a three-dimensional (3d) static model of the tertiary Jeribe reservoir. The well heads that locate the wells from Kelly bushing (KB), the well tops that indicate the reservoir zone division, the structural contour map with faults that were digitized by Neuramap 2015 software, and the well logs that include the reservoir characteristics (facies, effective porosity, permeability, water saturation, and Net to Gross) that were received from IP (Interactive Petrophysic V.2018) program were imported into Petrel as the important data of a geological model building.

Structural Contour Maps Modeling
Structure contour maps relate reservoir structure characteristics like differing rock layers and elements on well tops, well paths, and zones log data (Engheim, 2018). Because of the disorganized dataset and additional geometrical restrictions, the proper structure model for a reservoir has gotten more complicated for petroleum engineers (Schlumberger, 2017).
Petrel was used to construct structural contour maps depending on the contour map of the top of the Jeribe reservoir and well tops. As illustrated in Fig. 2, a structural contour map of the top of Jeribe has been created.

Fault Modeling
This method attempts to build a fault model based on the fault data. Fault contours interpreted seismic segments, imported structural maps, or fault sticks can all be used to develop a fault model. The length and geometry of the fault planes are defined by Key Pillars. The framework of the 3D model is built by the Key Pillars. A key Pillar is a vertical, linear, listric, or curved line representing two, three, or five shape points (Ahmed and Hamd-Allah, 2021). The Jeribe Formation in the Jambour oil field has two main faults parallel to the fold axis and nine minor faults (Sahib and Al-Dulaimi, 2022). According to the fault data from the Jeribe structural contour map, a fault model with listric key pillars was constructed, as demonstrated in Fig. 3.

Pillar Gridding
Pillar Gridding is the process of forming a spatial framework and skeletons based on key pillars, with the key pillars being turned into fault surfaces described by Pillars (Schlumberger, 2017). The first stage in creating a three-dimensional model is establishing a 3-D grid model, a system of horizontal and vertical lines used to define a three-dimensional geological model. A 3D grid, in simple terms, splits a model into boxes. Each box is termed a grid cell, and it has a single type of rock, a single value of porosity, one value of the net gross ratio, and so on. Those are all defined as cell characteristics (Al-Mozan and Al-Jawad, 2020). A 3-D Pillar Gridding model for the Jeribe reservoir has been generated with 100 * 100 m on the X-Y axes. Fig. 4. represents the 3-D skeletons of the Jeribe reservoir.

Make Horizons and Layering
Horizon is a border that separates two beds or surfaces of top zones. Generate horizon is a method for adding strata in the vertical plane inside a 3D grid (Bendiksen, 2013).
The last step when building the structural framework is to start defining the thickness and direction of the layers between the 3D grid's horizons. These layers, combined with the pillars, establish the 3D Grid cells that are provided attributes throughout property modeling (Schlumberger, 2017). Based on the porosity values gathered from the well logs, four horizons (3 zones) have already been constructed for the Jeribe reservoir. Depending on the variance in porosity, the zones were subdivided into layers, as shown in Table 1.

Scale up Well Logs
Scale-up of the well log is a procedure that uses statistical approaches to average well log readings in grid cells. Many wells penetrate each cell of the 3D grid. Each cell has a value of one for each of the Petrophysical characteristics. The final 3D grid's outputs are just specified value grid cells throughout its penetration. After this process is scaled up, the well log can be utilized in Petrophysical modeling whenever reservoir properties are described by splitting the studied area into three-dimensional grids. Grid cells are often substantially larger than the density of samples extracted from logs. Before every modeling process depends on well logs, the well log must be upscaled to provide defined 3D grids, referred to as the blocking of well logs (Al-Husseini and Hamd-Allah, 2022).
There are many statistical approaches for scaling up, including (arithmetic, harmonic, and geometric method). The present model's porosity, water saturation, and net gross ratio values were upscaled using (an arithmetic average) because they are well-behaved variables since they typically have small variability and averages according to a simple arithmetic average. Meanwhile, permeability was scaled up by using a geometrical approach since permeability is a variable not intrinsic rock properties; it depends on boundary conditions outside of the volume of measurement or specification. These variables can vary over several orders of magnitude (Pyrsz and Deutsch, 2014).

Facies modeling
Facies modeling is the process of lithology distribution based on the data received from well logging interpretation. Facies distribution of Jeribe reservoir in Jambour oilfield was set up using object modeling-adding general objects method. The facies distribution of the Jeribe Formation shows that this formation is composed mainly of limestone with some dolomitic-limestone and a few anhydrite, as shown in Fig. 5.

Petrophysical Modeling
Petrophysical modeling is a static distribution of reservoir parameters in 3D grid cells. The Petrophysical Model was designed using the Sequential Gaussian Simulation Algorithm (SGS) as a statistical method to agree with available magnitude data (Schlumberger, 2017). These properties are as follows:

Porosity Modeling
Petrel generated this model based on the porosity data collected from the well logs (density, neutron, and sonic) via Interactive Petrophysics software. As a statistical technique, the Statistical Sequential Gaussian simulation algorithm was used (Schlumberger, 2017). Fig. 6. represents a threedimensional representation of the porosity model for the Jeribe reservoir.
Because a relationship between porosity and permeability is a proper technique for designing the model, the permeability concept is highly dependent on the porosity feature. FZI represents similar or close values for some rock properties (surface area and flow path) based on the generalized Flow zone indicator FZI technique used to predict permeability (Ezekwe, 2011). Fig. 7. illustrates the threedimensional permeability variation in the Jeribe reservoir.

Permeability Modeling
Because a relationship between porosity and permeability is a proper technique for designing the model, the permeability concept is highly dependent on the porosity feature. FZI represents similar or close values for some rock properties (surface area and flow path) based on the generalized Flow zone indicator FZI technique used to predict permeability (Ezekwe, 2011). Fig. 7. illustrates the threedimensional permeability variation in the Jeribe reservoir.

Water Saturation Modeling
The estimation of water saturation is very important because it is key in hydrocarbon saturation determination. The water saturation model was constructed based on the Sequential Gaussian Simulation Algorithm in Petrel 2020.3. The 3D water saturation model is clarified in Fig. 8.

Net to Gross Modeling
Due to the clarity of the drilled geologic section, which contains a rich hydrocarbon content and optimal reservoir quality, the net pay is a critically valuable variable in determining the reservoir characteristics. Because non-reservoir rocks are not considered, the net pay reflects the reservoir modeling (Worthington, 2009). Fig. 9 demonstrates the net-to-gross ratio in the Jeribe reservoir.

Results and Discussion
The Jambour oil field is a narrow, asymmetrical subsurface anticline. The main tertiary reservoir is called the Jeribe reservoir. This reservoir is about 39 m thick on average. The Jeribe reservoir was separated into three units also composed of three rock types (limestone, Anhydrite, and dolomitic limestone); their ratios are tabulated in Table 2, while Table 3 shows the average values for the reservoir properties (porosity, permeability, the thickness of zone, water saturation, and net to gross ratio).

Discussion
Based on the results, the Jeribe reservoir has good petrophysical properties. The best averages for porosity, net to gross, and permeability in zone two are approximately 9.1%, 50%, and 194 md. In comparison, water saturation is about 72 % because some of the Anhydrite is included in this zone. Zone three is about 22m thick, and zone one cannot be considered a good unit with average properties, as shown in the Table. 2, with an average thickness of about 11m, and its permeability is low. Even though zone two is more than 42 m thick, the porosity and permeability results show that zones two and three are good units in the reservoir. The porosity and permeability values of the Jeribe reservoir are particularly good.

Conclusions
The Jeribe Formation is divided into three zones. According to this study, the porosity modeled by Petrel 2020.3 varies from 5 to 25 percent, with no observable variations in average porosity values for any zone. The permeability distribution predicted in this study showed that zones 2 and 3 are good in contrast to zone one, which has a lower permeability value. Permeability results ranged from 0.1 to 1277 md. According to the Net to Gross model, zone three had 62 % net values, whereas the Jeribe Net to Gross values ranging had been 36-85 %. Consequently, zones 2 and 3 account for 85% of the Jeribe. As a result, nearly 85 % of the Jeribe reservoir could be classified acceptable reservoir units.