Integrating Rock Typing and Petrophysical Evaluations to Enhance Reservoir Characterization for Mishrif Formation in Garraf Oil Field

Abstract


Introduction
Reservoir characterization is crucial for the efficient extraction of hydrocarbon resources, especially in complex fields ( Al-Baldawi, 2022;Gibrata et al., 2023).To achieve successful reservoir characterization, it is necessary to interpret key properties such as porosity, volume of shale, net pay, and water saturation (Gupta and Gairola, 2020;Al-Heeti and Al-Fatlawi, 2022;Al-Heeti et al., 2023).These properties help to gain comprehensive insights into subsurface geology, understand reservoir heterogeneity, and optimize oil production (Ma and Ma, 2019;Sen et al., 2021).Petrophysical workflows and rock physics modeling integrate log, core/cuttings, and production data to obtain meaningful physical properties (Skalinski and Kenter, 2015;Al Jenaibi et al., 2018;Sabea et al., 2022).

Geological Setting
The Garraf Oil Field is located in Dhi Qar Governorate, approximately 265 km southeast of Baghdad and 85 km north of Nasiriyah city (Fig. 1).The Garraf oil field is a northwest-southeast trending anticline with 24 km length and 5 km width.Many wells were drilled in Gharraf oil field since 1984.Garraf oil field represents forms of a series of anticlinal structures developed on the southern flank of the Zagros Mountain front flexure, the trend of the anticline is parallel to the main Zagros trend.The primary oil accumulation in the Garraf structure is located in the Mishrif reservoir, which is located between approximately 2270 and 2450 m total vertical depth (TVD).It is the uppermost oil accumulation in the Gharraf structure (Didanloo et al., 2015).In well Ga-3, the thickness of the Formation measures around 209 m.The Mishrif Formation (Cenomanian to the Early Turonian) has a gradual contact with the underlying Rumelia Formation, whereas the overlying Khasib Formation is in unconformable contact (Fig. 2).It primarily consists of shallow-water, shelf carbonates, such as bioclastic limestones, as well as algal, coral, and rudist bioherms (Al-Dabbas et al., 2010).This formation holds considerable importance as a significant oil reservoir and serves as a productive hydrocarbon reservoir in numerous oilfields in Iraq.

Materials and Methods
The current study is based on data from two wells supplied by Dhi Qar Oil Company, Ga-92p deviated well and Ga-3 vertical well.
To achieve precise characterization of the Mishrif reservoir and identifing the optimal oil-rich zones the following aspects are investigated, lithological identification, porosity assessment (by employing a multi-method approach that includes density, neutron, and sonic porosity measurements), the volume of shale determination (by employing gamma-ray logs and resistivity measurements), water saturation estimation(through Pickitt plot analysis and using the Archie equation to derive critical parameters a, m, n), Sensitivity analysis conducting (to establish cutoff values for these parameters for water saturation assessments), and advanced rock typing techniques analysis (including hydraulic flow unit assessment and Rock fabric number cross-plots).(Neamah et al., 2022) Formation evaluation is a critical aspect of reservoir characterization, and it relies on a variety of well logging tools to gather essential data (Mahmood and Al-Fatlawi, 2021;Gao et al., 2023;Szabó et al., 2023).The available data includes caliper logs, gamma-ray logs, resistivity logs, sonic logs, density logs, and neutron logs.These logs provide crucial information about the formation's lithology, porosity, fluid content, and rock properties.
Caliper logs offer insights into borehole diameter variations, helping us identify irregularities and potential issues in the wellbore (Renteria et al., 2022).Gamma-ray logs are employed to determine shale content within the formation, aiding in the evaluation of reservoir composition (Inanc and Vogt, 2018).Resistivity logs measure the formation's electrical resistivity, which can be indicative of fluid types and their saturations (Merletti et al., 2023).
Sonic logs provide valuable information on the rock's acoustic properties.They are used for porosity calculations, fracture identification, seismic attributes inversion, and determining the dynamic mechanical properties of the rock (Carvalho and Carrasquilla, 2019;Chakraborty et al., 2021).Density logs are used to measure bulk density, which is an important parameter for estimating porosity and lithology in rocks (Ahmed et al., 2022).By analyzing the bulk density, it is possible to estimate the porosity of the rock and provide insights into the lithology of the rock, (Ijasan et al., 2013).Neutron logs are important for evaluating porosity and identifying fluid types based on hydrogen content measurements (Horsfall et al., 2013).These logs provide information about the hydrogen index (HI) and are commonly expressed in apparent water-filled porosity units assuming a constant matrix lithology (Niculescu and Negut, 2015).
The integration of these diverse data sets enhances our understanding of the geological and petrophysical properties critical for successful hydrocarbon extraction in this complex field (Figs. 3 and  4).

Results
The results indicate the determination of porosity, the calculation of shale volume, the assessment of water resistivity, water salinity, Archie's parameters, water saturation, and the categorization of rock types using hydraulic flow units (HFU) and rock fabric number (RFN).These analyses were performed utilizing the TECHLOG and IP software tools (Liang et al., 2022;Alzubaidi et al., 2021).This process relies on various data sources and techniques:

Lithology by neutron-density
The identification of lithology through neutron-density cross-plotting is a long-established technique within the realm of petrophysical analysis.This method holds significant importance in the fields of geology and reservoir characterization.It revolves around the application of measurements obtained from well logging tools, specifically those related to neutron and density data, to differentiate between different rock types and their respective porosity characteristics (Serra and Serra, 2004).Neutron and density logs furnish insights into the hydrogen and electron densities of the subsurface formation, respectively.Through the creation of a cross plot that juxtaposes these two measurements, discernible clusters associated with various lithologies become apparent, facilitating their accurate identification.The result reflected that the mean lithology of the Mishrif reservoir is limestone (Fig. 5).

Lithology by M-N plot
In essence, the M-N cross plot method leverages calculated values of M and N, derived from specific mathematical formulas, to construct a visual representation.This graphical depiction serves as a valuable tool for categorizing lithological characteristics and aids in the discernment of different mineral compositions within the Mishrif Formation.Within this cross-plot, three prominent lithological lines emerge, representing sandstone (silica), limestone, and dolostone (dolomite).These lithology lines are typically associated with specific porosity values, expressed as percentages.The M-N cross plot plays a pivotal role in categorizing the density and neutron mineral mixture, providing valuable insights into lithology-dependent quantities represented by M and N.
Additionally, the cross plot M-N technique is instrumental in the classification of major lithological and mineralogical constituents within the Mishrif Formation (Santos et al., 2003).Notably, the predominant mineral identified in this context is calcite, which serves as the primary constituent of limestone rock formations (Fig. 6).The formulae for calculating the lithology-dependent quantities M and N are as follows: Where: -△   represents the interval transit time for fresh water, taking the value of 189 m/s, and 185 m/s for salt mud.
-△  corresponds to the sonic log reading.
-  stands for the density log reading.
-  signifies the density of fresh water, which is either 1 g/cm³ or 1.1 g/cm³ for salt mud.
-∅  denotes the porosity of neutrons for the fluid, assumed to be 1.

Volume of Shale
The determination of the volume of shale is a fundamental undertaking in the field of petrophysics and reservoir characterization.This volume calculation holds pivotal importance as it provides critical insights into the composition and heterogeneity of subsurface formations (Tiab and Donaldson, 2015).Shale volume has been calculated by several methods, as follows:

Shale volume by Gamma ray log
The gamma-ray log is a measure of a formation's natural radioactivity.The most abundant radioactive elements in sedimentary rocks are potassium, thorium, and uranium.Each of these elements continuously emits gamma rays, which can be detected by a scintillation counter placed within the borehole.As a result of certain clay minerals possessing radioactive elements, the capability to distinguish between clay free and clay-rich sandstones are possible using the gamma-ray log provided that the clays carry the above radioactive elements, as follows: Where: ℎ : Volume of Shale.  : gamma ray reading in interested zone.  : minimum gamma ray reading (clean zone).  : maximum gamma ray reading (Shale zone).

Shale volume by resistivity log
The resistivity log gauges the opposition encountered by an electrical current as it traverses through a geological formation.These measurements, expressed in ohms per meter, are acquired through two methods: one involves directly introducing an electrical current into the formation, while the other induces a current and assesses the ensuing response at the detector.The resistivity of rock, being an insulator, relies on various factors, including the type and quantity of fluids present in the pore spaces or absorbed by clay minerals, the geometry of the pores, the thickness of the rock beds, the depth of penetration, and the conditions within the borehole itself.
To estimate the volume of clay in a formation, the resistivity method draws a connection between the resistivity of clay minerals (Rcl), typically extracted from a nearby shale deposit, and the true resistivity of shaly sands (Szabó et al. 2023).This estimation process is governed by equation 4, as follows: Where: ℎ : resistivity of shale (Adjacent Shale Bed)   : interested zones resistivity

Shale volume by Neutron-Sonic method
The neutron-density method of calculation clay volume relates the variation in the neutron and density porosities) between a shaly sand and an adjacent shale bed.Because clay minerals contain high amounts of hydrogen, neutron porosity will record higher than the density porosity.Thus, a linear relationship between clay volume and the amount of difference between the neutron and density porosities is formulated.As the amount of clay increases, the difference between neutron and density porosities increases.The neutron-density for calculating volume of clay by applying equation 5, as follows: ℎ : shale volume.∅  : neutron porosity.∅  : density porosity.∅ ℎ : neutron porosity at shale zone.∅ ℎ : density porosity at shale zone.

Shale volume by Neutron-Density method
The neutron-sonic method calculates clay volume using a combination of neutron and sonic porosities between a shaly sand and a certain shale bed.The neutron-sonic equation for calculating volume of clay by applying equation 6, as follows: Where  ℎ : Clay volume.
All previous shale volume calculation methods has been calculated for the two wells Ga-92p and Ga 3 (Figs.7 and 8).The method of estimating the volume of clay using deep resistivity logs has a notable limitation.When water saturation in a formation increases, it leads to an increase in the true resistivity, which, in turn, causes an overestimation of the volume of clay.Additionally, clays have a significant impact on both neutron and density porosities, which can further affect the accuracy of clay volume estimates.
Considering these factors, it's worth noting that estimating clay volumes using gamma ray logs is often regarded as the superior method for calculation.Gamma ray logs provide a more reliable and less affected measurement when it comes to clay content in formations.This is because gamma ray logs primarily detect the natural radioactivity of the formation and are less influenced by changes in water saturation or clay content, making them a preferred choice for accurate clay volume assessments.

Porosity Calculation
The porosity of a rock is defined as the ratio of the pore volume to the bulk volume of the reservoir rock on percentage basis the measurement of porosity is important to the petroleum engineer since the porosity determines the storage capacity of the reservoir for oil.All methods for calculating the effective porosity are used in order to compare them with the porosity of core and choosing the suitable state.The porosity can be calculated by according to the following equations 7, 8, 9 and 10 ( Silva et al., 2019;Dasgupta et al., 2019;Ojo et al., 2018) After calculation porosity by the prevouis methods,it should be compared with porosity measured by core.The best method is porosity by density ehich is close to core porosity (Fig. 9).

Formation Water Resistivity
Formation water is the reservoir water that is not contaminated with drilling mud.The resistivity of the formation water (Rw) is an important parameter since it is required for the saturation's calculation.The value of (Rw) can vary widely from well to well in some reservoirs because of the variation parameters that include salinity, temperature, freshwater invasion, and changing depositional environments.However, for determining the reservoir water resistivity have been developed, including chemical analysis of produced water sample, resistivity porosity logs, and various empirical methods (Archie, 1942).

Archie's Parameters
The Archie equation's parameters a, m, an n play a crucial role in accurately estimating water saturation in subsurface reservoirs, with each parameter representing specific geological and petrophysical characteristics.Where, a is the tortuosity factor, m is the cementation exponent, and n is the saturation exponent.These parameters provide insights into the complexity of the rock formation and the behavior of fluids within it The tortuosity factor (Gibrata et al. 2023) accounts for the convoluted path that electrical currents must take as they traverse through the porous medium of the rock.It reflects the degree of tortuosity or meandering of the fluid pathways within the reservoir.In carbonate formations, where pore structures can be intricate and interconnected, the tortuosity factor helps capture the electrical resistance caused by the indirect path that current takes, affecting the overall resistivity measurement.Deviations from the typical value of 1 can occur due to the varying degree of pore complexity and fluid movement within the reservoir rock.
The cementation exponent (Gibrata et al. 2023) characterizes the resistivity contrast between the rock matrix and the pore fluids.It indicates the degree of cementation or bonding between the mineral grains in the rock.In carbonate reservoirs, the value of m can vary widely due to factors such as the mineralogy, diagenesis, and compaction history of the rock.Higher m values indicate stronger cementation and tighter pore spaces, leading to lower effective porosity and potentially lower water saturation.Lower m values correspond to more open pore structures with increased potential for fluid movement (Archie, 1942).
The saturation exponent (Gibrata et al. 2023) represents the relationship between the water saturation and the effective porosity of the rock.While it is commonly assumed to be 2 in many applications, it can deviate from this value in carbonate reservoirs due to complex pore geometries and fluid behavior.n affects the sensitivity of water saturation to changes in porosity.In some cases, n values less than 2 indicate an increased sensitivity, implying that relatively small changes in porosity can result in significant variations in water saturation.Pickett's plot are used to estimate Rw, and Archie's parameters it will be described as the following.
Pickett's plot Pickett's (1966) proposed a method which depends on a cross plot between resistivity at water zone vs. porosity to estimate cementation factor (a,n,m) from well logs (Archie, 1942), this method is depends on Archie equation.The results from two wells are a=1.1,m=2.1,n=3.7 (Fig. 10).

Water Saturation
Water saturation is a critical parameter in the assessment of hydrocarbon reservoirs, particularly in carbonate formations.It represents the proportion of pore space within a reservoir rock that is filled with water, as opposed to hydrocarbons.Accurate determination of water saturation is essential for estimating reservoir reserves, predicting production behavior, and designing effective production strategies.The Archie equation, developed by Gus Archie in the 1940s, has been a fundamental tool for calculating water saturation in carbonate reservoirs.This empirical equation takes into account the porosity of the rock, the resistivity of the formation water, and the resistivity of the rock itself to estimate the water saturation.The Archie equation 11 application as follows: Where  : the water saturation (fraction).  : the water resistivity (ohm-m).  : formation resistivity (ohm-m).Ø: porosity a. n. and m: Archie's parameters The results of water saturation calculation in Ga-3 and Ga-92p are shown in Figs.11 and 12.

Cut-Offs by Sensitivity Analysis
Net pay represents a portion of the reservoir characterized by favorable petrophysical properties and economically viable hydrocarbon reserves.This parameter is pivotal in the estimation of initial hydrocarbon quantities , making it a critical aspect of reservoir assessment.The associated net-to-gross ratio (NGR) quantifies the ratio between the thickness of net pay within the reservoir and its overall (gross) thickness.A significant step in this process involves establishing a link between conventional core measurements and a reference parameter that can distinguish between reservoir rock and nonreservoir rock.In the oil industry, cut-off values are commonly referred to as limiting thresholds (Baker et al., 2015;Jiang et al., 2002;Al-Fatlawi, 2018).These thresholds serve as decisive points above or below which values are accepted or rejected.They play a vital role in determining net pay and net-to-gross intervals within the reservoir.Additionally, it's essential to differentiate between the terms "net reservoir" and "net pay."Net reservoir denotes intervals within the formation containing any fluid type with the capacity to flow.In contrast, net pay shares the same characteristics as net reservoir but specifically excludes water, thereby identifying hydrocarbon intervals.theestimated cutoff values of Mishrif Formation are shown in Figs.13, 14 and15, which volume of shale cutoff=0.22,porositycutoff=0.11and water saturation cutoff=0.56.

Rock Typing by Rock Fabric
Rock typing by rock fabric, as proposed by Lucia, is a methodology used to categorize carbonate reservoir rocks based on their depositional and diagenetic attributes.This approach aims to capture the variability in petrophysical and fluid flow properties within carbonate formations, which can be highly heterogeneous due to the complex interplay of factors like sedimentary history, mineralogy, and diagenesis.The rock fabric number, introduced by Lucia, is a key parameter in this categorization (Lucia, 2007).
The (RFN) value characterizes the degree of porosity and permeability development in a carbonate rock.Different (RFN) ranges correspond to distinct rock types, each with its own petrophysical characteristics.The rock types are associated with various depositional and diagenetic environments, including mud-dominated facies, grain-dominated facies, and intermediate facies (Lucia, 1995).These categories help in understanding fluid flow behavior, reservoir connectivity, and well performance.fromthis method packstone to mudstone (Fig. 16).

Rock Typing Based on Hydraulic Flow Units and Lorenz Plots
Rock typing based on hydraulic flow units and Lorenz plots is a methodology used to classify reservoir rocks within carbonate formations according to their fluid flow characteristics.This approach is especially valuable for understanding and predicting fluid flow behavior, permeability distribution, and connectivity in heterogeneous carbonate reservoirs.It involves the identification of distinct hydraulic flow units (HFUs) and the use of Lorenz plots to visualize and analyze the distribution of permeability within these units.Hydraulic flow units are rock intervals with similar petrophysical and flow properties, often characterized by common depositional and diagenetic features.These units represent zones with comparable fluid flow behavior, making them crucial for reservoir modeling and simulation.HFUs are defined by integrating various rock attributes, such as porosity, permeability, pore size distribution, and mineralogy, to capture the complex relationships that influence fluid flow.
Lorenz plots are graphical representations of cumulative pore volume versus cumulative permeability for different rock samples within a given HFU.These plots help visualize the variability in permeability distribution within a unit and provide insights into the reservoir's heterogeneity.Lorenz plots enable the quantification of reservoir connectivity, the identification of high-permeability streaks or barriers, and the determination of flow unit boundaries (Riazi, 2018;Shah et al., 2022).
By combining the identification of HFUs with Lorenz plots.There are five sedimentary facies within Mishrif Formation varied from packstone, packstone to wackstone,wackstone, wackstone to mudstone and mudstone (Al-Dabbas et al., 2010).The results are shown in Figs.17, 18 and 19.Lorenz plot can analyze and characterize the storage and flow capacity of different rock types within a hydrocarbon reservoir.It is a valuable tool for understanding the heterogeneity and connectivity of reservoir rocks, which is crucial for optimizing oil and gas production.The Lorenz plot typically involves plotting two key parameters against each other: porosity (storage capacity) and permeability (flow capacity).Porosity represents the ability of a rock to store fluids, such as oil or gas, while permeability quantifies the ease with which fluids can flow through the rock.These parameters are essential for determining how easily hydrocarbons can be stored within the rock and how effectively they can be produced.According to Li et al., 2021, the porosity represents the volume of pore space available for storing hydrocarbons.High porosity indicates that a rock has a greater storage capacity, as it can hold more hydrocarbons, while low porosity means limited storage capacity.While, the flow Capacity or permeability is a measure of the ability of a rock to transmit fluids through its pore network.Rocks with high permeability allow fluids to flow easily, making them favorable for production, while rocks with low permeability impede fluid flow.In a Lorenz plot, data points are typically taken from core samples or well-log data representing different rock types within the reservoir.The plot consists of a scatter of data points, each representing a specific rock sample (Li et al., 2021).
The hydraulic flow unit histogram is a graphical representation of the distribution of hydraulic flow units within a reservoir.Hydraulic flow units are defined as representative elementary volumes of reservoir rock with similar geological and petrophysical properties.These properties, such as porosity and permeability, play a crucial role in fluid flow within the reservoir.The histogram provides a visual representation of the frequency or occurrence of different hydraulic flow units within the reservoir.It helps in understanding the variability and distribution of flow units, which can be useful for reservoir characterization and predicting permeability (Figs. 18 and 19).
The histogram typically displays the hydraulic flow units on the x-axis and the frequency or percentage of occurrence on the y-axis.Each bar in the histogram represents a specific flow unit, and the height of the bar indicates the frequency or percentage of occurrence of that flow unit within the reservoir (Fig. 18).After integration of all the used methods, five sedimentary facies are distingwished and are varied from packstone facies (high permeabilty) to mudstone facies (very low permeability) (Fig. 19).

Conclusions
A comprehensive reservoir characterization investigation is accomplished for the Mishrif reservoir in Garraf Oil Field.Appiying various method such as the assessment of porosity through density, neutron, and sonic porosity measurements, providing a thorough examination of the reservoir's porosity distribution.The volume of shale is accurately determined through the use of gamma-ray logs and resistivity measurements, adding precision to our understanding of the reservoir's composition.The study employs the Archie equation to derive essential parameters (a, m, n) for estimating water saturation, with cutoff values meticulously determined through sensitivity analysis.Furthermore, advanced rock typing techniques, such as hydraulic flow unit assessment and rock fabric number crossplots from the cores from depth 2320 m to 2395 m , are analysed, facilitating the categorization of reservoir rocks into distinct flow units, depending on the Storage Capacity (core porosity) and flow Capacity (core permeability).Among these methodologies, gamma-ray logs prove to be the most reliable for determining shale volume, while density logs closely approximate core porosity measurements.Water resistivity is estimated at 0.016, and Archie parameters (a, m, n) are determined as 1.1, 2.1, and 3.7, respectively.The established cutoff values are 0.22 for shale volume, 0.11 for porosity, and 0.56 for water saturation.This comprehensive approach results in the identification and classification of five distinct sedimentary facies ranging from packstone, packstone to wackstone, wackstone, wackstone to mudstone, and mudstone.