Static reservoir modeling of the Bahariya reservoirs for the oilfields development in South Umbarka area, Western Desert, Egypt

Accepted Manuscript Static reservoir modeling of the Bahariya reservoirs for the Oilfields development in South Umbarka area, Western Desert, Egypt Mohamed I. Abdel-Fattah, Farouk I. Metwalli, El Sayed I. Mesilhi PII:

S1464-343X(17)30418-1

DOI:

10.1016/j.jafrearsci.2017.11.002

Reference:

AES 3043

To appear in:

Journal of African Earth Sciences

Please cite this article as: Mohamed I. Abdel-Fattah, Farouk I. Metwalli, El Sayed I. Mesilhi, Static reservoir modeling of the Bahariya reservoirs for the Oilfields development in South Umbarka area, Western Desert, Egypt, Journal of African Earth Sciences (2017), doi: 10.1016/j.jafrearsci.2017.11.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Static Reservoir Modeling of the Bahariya reservoirs for the Oilfields Development in South Umbarka Area, Western Desert,

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Egypt

Mohamed I. Abdel-Fattah1*, Farouk I. Metwalli2, El Sayed I. Mesilhi3 1

Geology Department, Faculty of Science, Suez Canal University, Ismailia, Egypt

2

Geology Department, Helwan University, Cairo, Egypt

3

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Halliburton, Cairo, Egypt

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*Corresponding author E-mail: [email protected]

Abstract

3D static reservoir modeling of the Bahariya reservoirs using seismic and wells data

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can be a relevant part of an overall strategy for the oilfields development in South Umbarka area (Western Desert, Egypt). The seismic data is used to build the 3D grid, including fault sticks for the fault modeling, and horizon interpretations and surfaces for horizon modeling. The 3D grid is the digital representation of the

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structural geology of Bahariya Formation. When we got a reasonably accurate representation, we fill the 3D grid with facies and petrophysical properties to simulate

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it, to gain a more precise understanding of the reservoir properties behavior. Sequential Indicator Simulation (SIS) and Sequential Gaussian Simulation (SGS) techniques are the stochastic algorithms used to spatially distribute discrete reservoir properties (facies) and continuous reservoir properties (shale volume, porosity, and water saturation) respectively within the created 3D grid throughout property modeling. The structural model of Bahariya Formation exhibits the trapping mechanism which is a fault assisted anticlinal closure trending NW-SE. This major fault breaks the reservoirs into two major fault blocks (North Block and South Block). Petrophysical models classified Lower Bahariya reservoir as a moderate to good reservoir rather than Upper Bahariya reservoir in terms of facies, with good porosity and permeability, low water saturation, and moderate net to gross. The Original Oil In 1

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(OOIP)

values

of

modeled

Bahariya

reservoirs

show

hydrocarbon

accumulation in economic quantity, considering the high structural dips at the central part of South Umbarka area. The powerful of 3D static modeling technique has provided a considerable insight into the future prediction of Bahariya reservoirs performance and production behavior.

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Keywords: Seismic Interpretation, Structural Modeling, Facies and Petrophysical Modeling, Volumetric Calculation, Bahariya Formation, South Umbarka 1. Introduction

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In the oil and gas industry, static reservoir modeling incorporates the construction of a computer model of a petroleum reservoir (Doyen, 2007), for the reasons for enhancing estimation of reserves and settling on decisions in regards to the

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development of the field, placing additional wells, predicting future production, and assessing alternative reservoir management circumstances (Tyson, 2007). Reservoir models are built incrementally utilizing the accessible knowledge about the reservoirs including geological, geophysical, reservoir and production engineering data (Cosentino, 2001; Fanchi, 2002; Love and Purday, 2008; Abdel-Fattah, 2010). A

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static reservoir model is a grid-based mathematical representation of a reservoir, which integrates various information from different sources such as well logs, core and seismic data (Viste, 2008). In the model, the reservoir can be represented in three dimensions for better volumetric calculation, well planning, uncertainty analysis

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and flow simulations (McLean et al., 2012). Reservoir models are built for a wide range of purposes, however common to all of them is the desire to construct a

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representation of the subsurface. Depending on the purposes, different aspects of the model may be significant. Recently, the enormous explorations distributed to the search for Western Desert (Egypt) petroleum resources have brought about the successful discovery of new oil and gas fields. The focus of activity is currently changing to some degree from exploration to development of these resources (Dolson et al., 2001; Abdel-Fattah, 2010). The expanding interest for petroleum products has represented a change to the search of oil and gas. This search for hydrocarbon has created increment with advances in more noteworthy computational innovation to assess the probability of hydrocarbon proneness of the basin subsequently limiting the risk factor associated 2

ACCEPTED MANUSCRIPT with hydrocarbon. South Umbarka area is located in the central western part of the Western Desert (Egypt), between latitudes 30 43’06”, 30 38’27” and longitudes 26 15’36”, 26 23’32”, approximately 115 kilometers southwest of the town of Matruh, and about 200 kilometers west of Alamien field (Fig. 1). South Umbarka oilfields are found in a development lease to the north of the Khalda Concession.

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There are three producing oilfields on the acreage: Khepri, Sethos, and Selkit. The main target of this study is Bahariya Formation which is one of the most important reservoirs in the Western Desert in Shushan Basin (Tanner & Khalifa, 2010). Bahariya Formation is overlain and underlain by Abu Roash-G Member and Kharita

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Formation, respectively (El Shazly, 1977; Dolson et al., 2001).

Oil deposits in the porous sandstones of Bahariya Formation (Khalifa & Catuneanu,

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2008). Under normal conditions, the reservoir occurs at locations where the Bahariya Formation is at a higher elevation (horsts) than the surrounding region. The primary job in this work is to discover such events. The strategy for doing this is the most ideal interpretation of seismic data recorded for area of interest. Essentially as with any physical technique of this nature, it ends up being exceedingly appealing to simulate the data collection process and to get learning by the examination of known

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circumstances (Adegoke, 2000). Realistic 3D reservoir model is then required in order to estimate the volume of hydrocarbon accumulations (Abdel-Fattah and Tawfik, 2015). This model comprises principally of a network of horizons and faults, providing the basis for subsequent model refinement and integration with other well

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data and applications. An actual reservoir can only be developed and produced once and errors can be disastrous and inefficient. It is important to model the reservoir as

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precisely as could be acceptable with a specific end objective to calculate the reserves and to decide the best technique for recovery (Haldorsen and Damsleth, 1993), however much of the petroleum economically as could reasonably be expected. Therefore, it allows for 3D visualization of the subsurface, which enhances understanding of reservoir heterogeneities and aids to improve recovery rates, as low recovery rates stem from inefficient sweep caused by poor knowledge of inter well-scale heterogeneities (Abdel-Fattah et al., 2010). For achieving optimal reservoir management of Bahariya Formation in South Umbarka area, an insight view towards good understanding of the reservoir behavior should be attained. Then, an efficient design of integrated static reservoir modeling is 3

ACCEPTED MANUSCRIPT a crucial requirement for planning of an optimal future development in South Umbarka area, and can be upgraded as more oilfields information becomes available. 2. Geological Setting In the northern part of the Western Desert, the stratigraphic column includes the full

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sequence of sedimentary succession from Cambrian rocks to recent deposits (Fig. 2). Overlying the basement rocks the deposits sequence thickens northwards in the Abu Gharadig Basin (reaching more than 10,700 m) before thinning over the Ras Qattara ridge (to about 2,990 m), which marks the northern edge of the basin

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(Sestini, 1984; Froidevaux, 1987; Saied et al., 2002). Several Western Desert basins have extremely deformed sequences of sedimentary rock which made as a result of

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extensive cycles of marine transgression combined with at least three orogenic (mountain-building) phases (Said, 1962; Metwalli and Bakr, 2007). The earliest of these phases was the Caledonian Orogeny which happened during the Middle Palaeozoic. The second phase, the Hercynian Orogeny, took place at the end of the Palaeozoic, while the Alpine Orogeny was a Jurassic-Tertiary phase (Sultan and Halim, 1988).

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The first Mesozoic deposit in the Western Desert was the early Jurassic Bahrein Formation, a continental sequence (El Shazly, 1977). This was followed by shallow marine sediments during the Middle Jurassic. Lower Cretaceous clastic sequences

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record a marine transgressive cycle - a relative sea level rise, and subsequent fall. This cycle begins with fluvio-continental sediments at the base (Neocomian) followed by transitional, near shore-deltaic sediments during the Lower Aptian and Albian. The

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transgression got its maximum during the Middle and Upper Aptian with deposition of the Alamein carbonates in a constrained marine/lagoonal environment. A return to continental deposits at the end of the Lower Cretaceous (Upper Albian to Lower Cenomanian) finished the cycle. Hydrocarbon production is concentrated in Cretaceous sequence of the Western Desert, mostly Aptian and CenomanianTuronian carbonate and clastic reservoirs (Metwalli and El Maadawy, 2005). The upper part of the old clastic sequence has a mixed lithology of limestones, sandstones and shales and has been producing from both the Abu Roash and Bahariya sandstones.

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ACCEPTED MANUSCRIPT The Cenomanian Bahariya Formation consists mainly of fine- to medium- grained quartizitic sandstone, colorless to pink, medium to coarse grained with thin streaks of shales, interbeds and carbonate inclusions (Soliman and El Badry, 1980). It represents a gradational phase of finning upwards to the overlying marine carbonates and shales of the Turonian-Coniacian Abu Roash Formation. Bahariya

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Formation is of early to mid-Cenomanian age, and was deposited first under fluviatile conditions that later changed to estuarine as flooding continued. The unit unconformably overlies the Kharita Formation and conformably underlies the Abu Roash Formation (Sestini, 1984; Dolson et al., 2001). Sands are variable, being

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made up of coarse-grained, cross-bedded sandstones that are seemingly nonfossiliferous to fine-grained, well-bedded, ferruginous clastics.

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The structural framework of Egypt can be explained in terms of plate tectonics. These tectonic generally E to NE trending highs, crossed by rejuvenated NNW-SSE Paleozoic faults (Meshref, 1990; Hussein and Abd-Allah, 2001; Abdel-Fattah and Alrefaee, 2014). As a consequence of these block tilting tectonics, a complex pattern of graben shaped depocenters developed over the unstable shelf area. The most prominent of them are the SW Qattara-Abu Gharadiq troughs. Periodically

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reactivated tilting of basement fault blocks led to remarkable erosions over the highs and originated alternating sequences of shales and limestone all around (Youssef, 1968). With local unconformities, onlapping wedges and significant thickening of the series towards the adjacent subsiding areas. The most intensive deformation

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occurred during Late Senonian-Early Tertiary. It is giving rise to the bulk of the structures drilled so far, and which consist mainly of faulted anticlines (EGPC, 1992).

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South Umbarka area experienced many tectonics that vary from deeper to shallow horizons (Meshref, 1990). Left and right lateral shears with minor compressional movements were encountered. Locations for drilling both shallow and deeper targets are difficult to find due to the complicated tectonic setting. Both shallow and deep Bahariya targets are sand reservoirs. The deeper sand is composed of transitional sand intercalated with shale, siltstone and limestone streaks (Metwalli and Bakr, 2007). South Umbarka area produces only oil and is located in the unstable shelf area (Said, 1962). 3. Database and Methods

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ACCEPTED MANUSCRIPT Our dataset available from South Umbarka area for reservoir modeling includes Bahariya reservoirs-specific data (Fig. 1), is comprised of both geological and geophysical information provided by Khalda Petroleum Company (KPC), and Egyptian General Petroleum Corporation (EGPC). The geological data are represented by eight borehole petrophysical logs (gamma ray, density, sonic,

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neutron, and resistivity logs) with interpreted stratigraphic tops, in addition core petrophysical reports. The geophysical data includes approximately 360 km of forty 2D seismic lines.

In the South Umbarka area, the static model of Bahariya reservoirs is used to

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represent the shape of the structures, and achieve accurate volume calculations. The workflow used to model the Bahariya reservoirs in South Umbarka area

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consisted of three types of modeling based on the seismic and well logs interpretation: structural modeling, facies modeling, and petrophysical modeling (Fig. 3).

Seismic interpretation represents the backbone of structural modeling (Fig. 3). The primary step in seismic interpretation is to establish the connection between seismic reflections and stratigraphy (Avseth et al., 2005). All wells have formation density

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logs and sonic (i.e. formation velocity); from these it is possible to make a synthetic seismogram showing the predictable seismic response for comparison with the real seismic data (Brown, 2004). Tying well data (in depth) to seismic (in time) helps to

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find events (seismic reflections) that corresponds to geological formations. One of the important process before facies modeling is the 3D grid depth conversion (domain conversion) of structural model because seismic data are interpreted in

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time, however the wells data are presented in depth (Abdel-Fattah and Tawfik, 2015). Depth conversion allows to take 3D grid from time domain and convert it to depth domain, to correlate it with well data and perform volume calculations. Spatial data analysis is the next step in the reservoir modeling workflow (Fig. 3). Spatial data analysis is a process of data quality control (QC), understanding the data and preparing inputs for facies and petrophysical modeling (Tyson, 2007; Pyrcz and Deutsch, 2014). This process of applying transformations on input data (normally upscaled well logs), identifying trends and defining variograms describing the data, is used to characterize the spatial continuity in the variation of discrete

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ACCEPTED MANUSCRIPT reservoir properties (facies) and continuous reservoir properties (shale volume, porosity, and water saturation). Facies modeling is a method for distributing discrete facies throughout the model grid to acquire a superior information and comprehension of the stratigraphic framework and facies distribution and architecture of the reservoir (Fig.3). Sequential

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indicator simulation (SIS), a krig-based stochastic method, was used to create facies values in all model cells using vertical proportion curves generated with the data analysis tool, and conditioned on the upscaled facies values in the node well cells to create a 3D facies model (Deutsch and Journel, 1998). This technique is often used

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in conditions where sparse data is present. A realistic facies distribution that matches the well data provides a realistic 3D static model (Abdel-Fattah, 2010). Such types of

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models represent a good approach to enhance the understanding of reservoir heterogeneity.

3D modeling of petrophysical log data is a key process for comprehension spatially distribution of the petrophysical reservoir parameters (Fanchi, 2002; Abdel-Fattah et al., 2010). The purpose of a petrophysical model is to provide an entire arrangement of continuous reservoir parameters for each cell of the 3D grid. Commonly it is

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utilized in combination with facies modeling to capture geological heterogeneity at different scales (Dubois et al., 2003). Interpreted petrophysical logs (porosity, permeability, and saturation) from the well logs analysis for Bahariya reservoirs were scaled up to the resolution of the cells in the 3D grid. The product of the interpolated

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cells is a 3D grid with petrophysical values for every cell. Sequential Gaussian Simulation (SGS), a stochastic method of interpolation based on Kriging, was used

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to create petrophysical models. Sequential Gaussian simulation is a stochastic simulation that is used for sparse continuous data (Pyrcz and Deutsch, 2014). It can honor input data, input distributions, variograms and trends. Many realizations can be run based on the same input data. However, each realization will provide a different but equal-probable result. Volume calculation including the volumes of a 3D grid (bulk, pore and fluid) is often left to the last phases of a reservoir investigation (Viste, 2008) when detailed property (facies and petrophysical) modeling has been completed (Fig. 3). These volumes represent a first indication of the economic possibility of the field, and can determine where the promising site should be drilled. Any development project on a 7

ACCEPTED MANUSCRIPT field obviously depends on the oil and/or gas in the reservoir rocks (Cosse, 1993). Calculation of the Original Oil In Place (OOIP) for Bahariya reservoirs in South Umbarka fields is the last step in the evaluation of the reservoir (Fig. 3). The goal of this calculation is quantitatively estimation of the oil amount in the reservoir. There are a lot of methods to estimate the OOIP, but the volumetric method is the most

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commonly used to estimating the OOIP (Rasheed and Kulkarni, 2016). 4. Results and Discussions 4.1. 3D Structure Model

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Structural modeling based on the seismic interpretations is the primary step in constructing 3D reservoir models. The target of 3D structural modeling is principally to get a 3D analysis and comprehension of the geological structure. The interpreted

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fault pattern and structural horizons of the hydrocarbons accumulation are basically the main components to construct a structural reservoir model (Cosentino, 2001; Avseth et al., 2005). This framework of horizon and fault surfaces defines the structural model of Bahariya reservoirs and forms the geometrical input for 3D grid building.

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Bahariya Formation consists mainly of sandstone, siltstone with limestone and shale streaks (Khalifa & Catuneanu, 2008); therefore problem is expected in starting picking from Bahariya Formation. Synthetic seismogram has been generated to overcome this problem (Brown, 2004) and determine the time of the marker bed by

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which we started picking (Fig. 4). We focused on picking horizons both above and below the target level in order to prevent mistakes and make a consistent framework

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during the interpretation. Therefore, Top Abu Roash (F2) Member, Top Upper Bahariya, and Top Lower Bahariya have been picked to act as a structural framework to construct the Bahariya reservoirs geometry (Fig. 5). The time maps of Upper and Lower Bahariya Formation are used together with the velocity model to convert the reflection times to depths, keeping in mind the end goal to build the structure depth maps (Figs. 6 and 7). These maps demonstrate the structural elements portraying the studied horizon tops in terms of two-way times (TWT) and depths. The comparison of these maps shows that, the tops of the picked horizons are structurally similar. The two-way-time of the Upper and Lower Bahariya Formation varies between 1282 to 1402 ms and 1323 to 1456 ms respectively, while 8

ACCEPTED MANUSCRIPT the depth values vary between 6055 to 6467 ft and 6291 to 6930 ft, respectively, and achieve their maximum value towards the northern part of the study area. The 3D structural model of both Upper and Lower Bahariya units (Fig. 8) based on the seismic interpretations shows that Selkit filed is located on a 4-way dip closure (anticlinal dome) trending E-W, while Khepri field is located on a 3-way dip closure

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(faulted anticline) trending WNW-ESE, whereas Sethos field to south located on a 4way dip closure (anticlinal dome) is slightly faulted by NW-SE trending fault. This major fault divides the area into two segments (North Block and South Block). Most folds owe their origin to compressional movements which affected the area during

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the Late Cretaceous - Early Tetiary tectonic event (Dolson et al., 2001; Abdel-Fattah et al., 2015). The structural cross sections made from the 3D structural model can be

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created in many direction of the study sector (Fig. 8); showing the lateral extension of reservoir zones and their thickness variation including faults. The structure for both Upper and Lower Bahariya units are nearly identical, due to the fact that the vertical thickness is no more than 150-200 ft, and the whole Bahariya Formation was affected by the Upper Cretaceous (Cenomanian) tectonic movements (Meshref, 1990; Hussein and Abd-Allah, 2001). The anticlinal domal structure provides a good

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trap for oil in both Selkit and Sethos fields, while the fault plane in Khepri field provides a good sealing in the area where Khepri-3 and Khepri--5 wells were drilled in order to catch the attic oil present under fault plane. These evidences are

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supported by N-S seismic line (Fig. 5). 4.2. 3D Facies Model

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The initial step to facies modeling is to build the facies logs for the available wells (Fig. 9). Facies logs need to be generated and upscaled to the geological grid before facies modeling (Tyson, 2007). The way of subdividing the stratigraphic section of Bahariya Formation into facies depends on the purpose of the study. The Bahariya Formation in South Umbarka area is a sequence of shallow marine sands, tidal deposits, marine shales and carbonates (Fig. 9). The sequence was laid down during the Middle Albian to Lower Cenomanian interval (Khalifa & Catuneanu, 2008). The overall depositional model for the Bahariya Formation is that of a tidal flat dissected by channels and passing seaward into tidal shelf sands and muds. The percentage of shale and carbonates increases in Upper (U-) Bahariya rather than

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ACCEPTED MANUSCRIPT Lower (L-) Bahariya (Fig. 9). The second step in facies modeling is to scale up the facies logs utilized in the reservoir modeling process (Fig. 9). For modeling process, various types of well logs or log data can be upscaled. The goal of upscaling well logs is to assign well log values to the cells in the 3D grid that are penetrated by the wells (Deutsch and

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Journel, 1998). Every cell in the model can hold only one value, thusly the log data is averaged, or upscaled, to distribute the facies data between the wells where data is not present.

After the well logs have been up-scaled, Quality Control (QC) by using histogram

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analysis should be done keeping in mind the end goal to check whether the flow units and the barriers have been captured (Fig. 9). If the layering is too thick then too

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much information has been lost, and the thickness of the layers must be adjusted in the model (Pyrcz and Deutsch, 2014). The shape of the histogram distribution of the input data and the simulated result should be similar (Fig. 9). The upscaled facies logs and their patterns identified in each well were then used to forecast or interpolate the facies vertical and lateral distribution between wells. The data analysis results can, together with the conceptual sedimentological model, be used

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in the facies modeling process to build a more realistic facies model (Doyen, 2007; Pyrcz and Deutsch, 2014). This analysis is the first step in facies modeling and can be performed once an upscaled facies property is available.

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Facies classification and their precise representation in a 3D cellular geologic model is basic because permeability and fluid saturations for a given porosity distribute substantially among facies (Dubois et al., 2003). An accurate facies model depends

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on the good delineation of the depositional environments of the study area (Fig. 10). The best source of facies information is the core samples of reservoir rock from boreholes, however, the reservoir interval are not usually cored due to the expense. Therefore, a well logging interpretation for estimating facies in wells without cores is required because of the availability of core information is limited (Fig. 9). Upper Bahariya reservoir consists of heterogeneous rock sequences which are often ignored in the search for potential reservoirs (Fig. 10). One of the most common heterogeneous sequences are thin-bedded sandstones and shales (Fig. 9). These reservoir sequences are not normally identified as potential reservoir zones in drill

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ACCEPTED MANUSCRIPT cuttings or through analysis of standard well logs (Asquith et al., 2004). However, once a heterogeneous reservoir has been identified in an area the appropriate technology can be applied to discover and evaluate these sequences in other wells. However, there is a real possibility that such zones will be over-looked in new exploration areas. Thin-bedded sandstone/shale reservoir rocks are found in South

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Umbarka area and were almost missed in some early wells. The resolution of the conventional well logs can’t detect the presence of thin reservoir beds which are interbedded with shales. The presence of conductive shale beds can cause high water content readings even for oil zones (Bilodeau et al., 2002). High resolution

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logs were adopted as standard for evaluating the Bahariya sandstones of the Western Desert. The complexity of the Bahariya Formation is well documented. A

observed in a number of fields. 4.3. 3D Petrophysical Model

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number of depositional models have been proposed for the sand/shale sequence

Facies and petrophysical reservoir models are constructed based on the interpretation of wire-line well logs (Fig. 11) and the correlation (Fig. 12) of geological units between wells because well logs can provide a lot of information about the

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properties of the reservoirs encountered in a well. Well log interpretation involves the synthesis and summary of large amounts of quantitative and qualitative information (Asquith et al., 2004). The important information required are the areal extent and thickness of the reservoir. Other essential parameters are the facies, shale volume,

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porosity, saturation values, and net to gross ratio. These parameters are important because they assist as significant inputs for facies and petrophysical reservoir

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modeling, and volumetric analysis, consequently estimation of the volume of hydrocarbon in place (Abdel-Fattah, 2010). The petrophysical model of the Bahariya reservoir, defined in the log quantitative interpretation phase, can be extended to the whole reservoir by means of the stochastic modeling approach. This can be done by attributing average petrophysical values or probability distribution functions to each facies within the reservoir (Cosentino, 2001). The previous methodology was applied to the zones of interest which are the Upper and Lower Bahariya units defined in each well. Tables 1 and 2 summarize the results from the log analyses of all the eight wells in the area. The reservoir parameters of the Upper and the Lower Bahariya reservoirs extracted from 11

ACCEPTED MANUSCRIPT the well logging data are averaged and modeled to reflect the petrophysical lateral distribution throughout the Upper and the Lower Bahariya reservoirs (Fig. 13). Such well interpretation would make a basic building block for 3D petrophysical model of the field-wide (Bilodeau et al., 2002; Abdel-Fattah et al. 2010). While the average values of hydrocarbon saturation in the Bahariya reservoir zones are high, the

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influence of shale reduces the average values of porosity and permeability (McLean et al., 2012). Therefore, the net pay thickness shows low values around well Selk-7 through the Upper Bahariya reservoir and high values around well Khep-5 and well Sethos-22 through the Lower Bahariya reservoir (Tables 1 & 2).

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The upscale well logs process was utilized to create upscaled shale volume (Vsh), porosity (PHIE), and water saturation (Sw) logs. The petrophysical modeling process

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was used in conjunction with the upscaled logs to assign values to the 3D grid cells (Fig. 13). These values over the entire model were generated using Sequential Gaussian Simulation (SGS). The specification of a variogram model are required for the stochastic simulation techniques for each lithofacies portraying the spatial distribution of the simulated property (Deutsch and Journel, 1998). The porosity and water saturation are determined stochastically within each lithological facies

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(backbone for calculating petrophysical parameters) (Fig. 13). The distribution of shale volume (Vsh) property gives key-control factor to the petrophysical potential of South Umbarka fields. The shale volume model of the Upper Bahariya reservoir reveals that shale volume attains its lowest value (13 %) at

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Sethos-7 and Sethos-16 wells and attains the highest value (32 %) at Selk-6 well (Fig. 13). The shale volume content increases in the east and west directions, while

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it decreases in the north, and south directions of the Upper Bahariya reservoir area. The shale volume model of the Lower Bahariya reservoir reveals that shale volume attains its lowest value (8 %) at Selk-7 well and attains the highest value (40 %) at Selk-6 well. The sequence stratigraphic framework of the Bahariya Formation is based on the lateral and vertical changes between shelf, subtidal, coastal and fluvial facies (Catuneanu et al., 2006). Therefore, the main factor controlling differences in shale volume for the Bahariya reservoirs is the type of sandstone facies. In porosity model, high effective porosity cells are located in the central part of South Umbarka area (around wells Sethos-16 and Sethos-22) (Fig. 13), which represented by sand facies. In Bahayria Formation, the high effective porosity cells are generally 12

ACCEPTED MANUSCRIPT located in the Lower Bahariya rather than Upper Bahariya which characterized by thin-bedded sand/shale facies (Catuneanu et al., 2006; Khalifa & Catuneanu, 2008). The final petrophysical property to be modeled was the water saturation. Modeling water saturation was carried out in a similar way as the porosity model. The water saturation model (Fig. 13) in turn gives a guide to the hydrocarbon-producing

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capacity of the Bahariya reservoirs. Each cell in the 3D grid represents a value of the water saturation in the South Umbarka area. The water saturation of Upper Bahariya reservoir attains the highest value (50 %) at sethos-22 well, while the lowest values (27 %) at sethos-7 well and (38 %) at selk-6 well. Water saturation model of the

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Upper Bahariya reservoir shows that water saturation decreases towards southeast direction and increases towards north and central part of the study area, while the water saturation model of the Lower Bahariya reservoir shows that the water

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saturation reaches the lowest values (43 %) at khep-10 well, while the highest value (52%) at selk-7. Water saturation of the Lower Bahariya reservoir increases towards south and north directions and decreases in the central part and some parts in the east and south directions of the South Umbarka area.

The overall lateral and vertical changes of the petrophysical parameters are mainly

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related to changes in facies (Haldorsen and Damsleth, 1993) as the overall depositional model for the Bahariya Formation is that of a tidal flat dissected by channels trending South-North direction (Said, 1962; Metwalli and Bakr, 2007; Catuneanu et al., 2006) and passing seaward into tidal shelf sands and muds.

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4.4. Reservoir Volumetric

Analysis of reservoir volumes acts as a guide for field exploration and development

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in South Umbarka area. This is the final significant stage because it is quantitatively estimating the amount of oil (Dubois et al., 2003) and determining its distribution in the Bahariya reservoirs. A static model of Bahariya reservoirs was used to calculate the reserves in terms of stock tank of original oil in place (OOIP), and predict comprehensively Bahariya reservoirs quality. All input parameters of the OOIP calculation came from the 3D reservoir model, expect the formation volume factor (Bo) is fixed at each reservoir interval (Rasheed and Kulkarni, 2016). The net to gross ratio (reservoir versus non-reservoir rocks) was determined from the facies model indicating to the distribution of sandstone, siltstone, shale and limestone throughout Bahariya reservoirs. 13

ACCEPTED MANUSCRIPT In South Umbarka oilfields, the total OOIP was calculated as 433 MMSTB. The Upper Bahariya reservoir has 138 MMSTB and 295 MMSTB for the Lower Bahariya reservoir. Much of the oil occurrences are confirmed by the eight wells. Seismic data suggests that major fault breaks the blocks into two segments (North block and South block) so the OOIP is calculated for each segment as shown in (Table 3).

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Most of the oil occurrences reside on the central and northern part of the study area as a result of the structure pattern and distribution of sand facies in the area. 5. Conclusions

In South Umbarka area, the geological structure of the subsurface is an important

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parameter for both exploration and development purposes. Once an area has been explored and estimated reserves be assured, the next step would be locating new

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wells for further development. Locating a new well depends on the extension of the trap and the petrophysical evaluation of the previously drilled wells. The structure of Bahariya Formation was studied through mapping Upper and Lower Bahariya units of South Umbarka area. Maps and cross sections from seismic and well data were used to explain the impact of geological structure in developing the area. 3D structural model of the Bahariya reservoirs based on the seismic and well data

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revealed that Sethos field having higher porosity and hydrocarbon saturation values due to the fault assisted anticlinal closure trending NW-SE. Faulting and folding plays an important role in the definition of the structural setting on the top of the Bahariya Formation in South Umbarka area. The oilfields were identified as a 4-way

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dip closure (Selkit and Sethos fields) and 3-way dip closure (Khepri field). 3D static reservoir modeling of the Bahariya reservoirs at South Umbarka area was

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required dramatically to deal with the complex problems associated with reservoir heterogeneity. The 3D property (facies and petrophysical) models based on the well data called for detailed geological and petrophysical spatially distribution in the area. The lithologic identification of the Bahariya reservoirs reveals the majority of sands bodies and the remainder (shale and limestone streaks) were barriers to vertical movement. The reservoir parameters of Upper and Lower Bahariya reservoirs are illustrated in the form of 3D petrophysical models which include shale volume (Vsh), effective porosity (Φe), and water saturation (Sw). In the Upper Bahariya reservoir the hydrocarbon saturation increases towards southeast direction and decreases towards north and central part, while the hydrocarbon saturation of the Lower 14

ACCEPTED MANUSCRIPT Bahariya reservoir increases towards south and north directions and decreases in the central part and some parts in the east and south directions. The central part over the study area is generally of perfect reservoir volumetric (OOIP), considering the high structural dips in the North Block and South Block. Static reservoir modeling coupled with proper uncertainty analysis can be applied to optimize a new

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development strategy of Bahariya reservoirs in South Umbarka area and in other analogous areas. Acknowledgements

We wish to express our most sincere gratitude and appreciation to the Egyptian

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General Petroleum Corporation (EGPC) and Khalda Petroleum Company (KPC) for

References

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Figures Captions

Fig. 1. Location map of the available data (seismic lines and wells) in the South Umbarka area (Western Desert, Egypt).

Fig. 2. Western Desert’s geological sequence is summarized in this lithologic column chart

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for different oil fields (EGPC, 1992).

Fig. 3. Simplified workflow for 3D static reservoir modeling of Bahariya reservoirs in South

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Umbarka area (Western Desert, Egypt). Fig. 4. Synthetic seismogram for (Sethos-22) well and its seismic tie. Fig. 5. Seismic line showing the asymmetrical anticlinal fold in N-S direction. Fig. 6. Time (A) and Depth (B) structure map for top Upper Bahariya. Fig. 7. Time (a) and Depth (b) structure map for top Lower Bahariya. Fig. 8. A structural model of Bahariya reservoirs build based on fault sticks from seismic, integrated with depth converted horizon picks to create a conceptually suitable structural framework which honors all inputs.

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ACCEPTED MANUSCRIPT Fig. 9. Quality control (QC) of upscaled facies logs (track 4) and facies distribution through U-Bahariya (upper histogram) and L-Bahariya (lower histogram). These histograms show the interpolated facies together with upscaled log data and raw well logs. Red column: raw well logs; Green column: upscale cells; Blue column: modeled facies. Fig. 10. 3D facies model of Bahariya Formation to better define vertical and lateral facies

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distribution between wells through L-Bahariya and U-Bahariya reservoirs. Fig. 11. Petrophysical log of L-Bahariya and U-Bahariya reservoirs in SETHOS-22 well. Porosity and Permeability Relationship of L-Bahariya and U-Bahariya reservoirs for well (SETHOS-22) from core analysis.

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Fig. 12. Correlation profile (A-A') using the information inferred from GR logs of different wells in W-E direction of the study area.

Fig. 13. 3D petrophysical models of the Bahariya reservoirs (Upper and Lower Bahariya)

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from well data analysis, showing lateral variations in petrophysical characteristics: (a) shale

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volume (Vsh), (b) effective porosity (PHIE), and (c) water saturation (Sw).

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Highlights • Static reservoir modeling is becoming more complex, dealing with larger datasets. • Reservoir modeling aims at better representation of Bahariya reservoirs behavior.

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• A realistic model enhances the understanding of reservoir heterogeneity.

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• Development strategy of oilfields is linked to the overall reservoir model.

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Table 1. Petrophysical out-put data of Upper Bahariya reservoir. Net Pay

Vsh

фe

Sw

Sh

K

(ft)

(%)

(%)

(%)

(%)

(mD)

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Wells

53.5

23

15

46

54

150

KHEP-5

13.5

18

9

45

55

89

KHEP-3

28.5

20

19

46

54

70

SETHOS-16

19

13

19

SETHOS-22

13.5

22

17

SETHOS-7

10

SELK-6

24.5

SELK-7

2.5

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KHEP-10

56

130

50

50

50

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44

18

27

73

100

32

16

38

62

77

26

11

49

51

13

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Table 2. Petrophysical out-put data of Lower Bahariya reservoir. Net Pay

Vsh

фe

Sw

Sh

K

(ft)

(%)

(%)

(%)

(%)

(mD)

Wells

59.25

15

13

43

57

130

KHEP-5

92.5

13

15

46

54

100

KHEP-3

62.5

16

14

48

52

180

SETHOS-16

49

18

17

49

51

160

SETHOS-22

90

18

16

47

53

100

SETHOS-7

51.5

10

10

48

52

120

SELK-6

17.5

40

11

45

55

130

SELK-7

6

8

13

52

48

160

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Table 3. Volume and OOIP calculations for Bahariya reservoir

Upper Bahariya

Block

Volume

OOIP

(MCM)

(MMSTB)

BULK

NET

PORE

North

251

147

22

South

477

267

39

North

782

469

61

South

875

469

61

2385

1352

Total

183

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MCM (Million Cubic Meter); MMSTB (Million Stock Tank Barrel)

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49 89

146 149

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Lower

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Reservoir

433

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