Introduction

Reservoir geomechanics plays a vital role in well drilling, horizontal well placement and completion optimization (Zoback 2007; Ramdhan and Goulty 2011; Baouche et al. 2020a, b). An accurate assessment of the magnitudes of pore pressure (PP) and principal stresses [vertical stress (Sv) and two horizontal stress tensors—minimum (Shmin) and maximum (Shmax) horizontal stresses] is an important aspect of comprehensive geomechanical modeling (Ganguli 2017; Ganguli et al. 2018; Radwan et al. 2019a, 2020a; Ganguli and Sen 2020), as it helps in understanding the potential risk of production-induced faulting, wellbore instability and solid production (Zoback and Zinke 2002; Haug et al. 2018). Stress state changes in subsurface reservoirs are controlled by changes in PP due to fluid drawdown or fluid injection that affected the horizontal stresses magnitudes during the production or gas storage (Ruistuen et al. 1996; Addis 1997a, b; Hillis 2001; Altmann et al. 2010; Dahm et al. 2015; Mortazavi and Atapour 2018; Li et al. 2019a, b; Candela et al. 2019). Advancing and evolving production from mature basins leads to critical changes in the stress state and causing reservoir depletion that resulted from fluid drawdown (Hol et al. 2018). In addition, fluid injection is usually used in the secondary recovery to increase hydrocarbon production and maintains the reservoir pressure, as well as gas storage (Paslay 1994; Radwan et al. 2019b, c, d). The previously mentioned activities in sedimentary systems can be called human-induced mechanical effects or anthropogenic activities that need to be paid attention to during oil and gas extraction and gas storage processes globally (Hol et al. 2018). Hence, the consequences of stress state change (or PP–stress coupling), which has been recorded in several basins worldwide, cause surface subsidence, field-scale deformation (e.g., Sand production and casing collapse) and fault reactivation in many basins like Brunei (Breckels and van Eekelen 1982), Texas, USA (e.g., Salz 1977; Whitehead et al. 1987), Canada (Ervine and Bell 1987), North Sea (e.g., Teufel et al. 1991; Wirput and Zoback 2000; Van Geuns and van Thienen-Visser 2017; Hol et al. 2018; Candela et al. 2019), France (Maury et al. 1990), Australia (Ruth et al. 2006) and Germany (Dahm et al. 2015).

This work focuses on reservoir geomechanical modeling in the offshore Badri field, a proven hydrocarbon accumulation in the Gulf of Suez, Egypt. The Middle Miocene Hammam Faraun and Kareem Formations are the two principal hydrocarbon-bearing units in the Badri field (Radwan et al. 2019a, b, 2020a; Radwan 2020c), which are presently in depleted condition due to prolonged production. In this field, there have been no prior published works on the present-day in situ stress distribution and the effect of depletion in stress magnitudes. We took this opportunity to address these geomechanical aspects, the primary goals of this study are to (a) assess the magnitudes of the three principal stress components, (b) interpret the prevailing tectonic stress regime, (c) quantify the drop in PP and Shmin in depleted reservoirs, (d) establish the depletion-induced reservoir stress path and (e) link the stress path value of the studied reservoirs with it is similar globally. Being an old producing oil field, data availability is a major issue that poses challenges in various steps of the geomechanical modeling. However, to overcome this common issue, we have employed the standard accepted workflows and modeling techniques to quantify the principal stress magnitudes.

Geological Settings

The African plate separated from the Arabian plate during Late Oligocene–Early Miocene (Bosworth and McClay 2001), and because of the continental rifting, the Gulf of Suez opened up (Schutz 1994; Moustafa 2002). It acts as a northwestern continuation of the well-documented Red Sea rifting system (Bosworth 1995; Bosworth and McClay 2001). It is characterized by a complex pattern of N/NNE–S/SSW- and E–W-trending faults (Abdel-Gawad 1970; Colletta et al. 1988; Evans 1988 ; Lyberis 1988; Patton et al. 1994; 1995; El-Naby et al. 2009; Youssef 2011; Attia et al. 2015; Abudeif et al. 2016a, b; Radwan et al. 2020b, c) (Fig. 1). The studied offshore Badri hydrocarbon field is situated in one of such structures. It covers ~ 12 km2, lying between 28°24′–28°26′ N and 33°22′–33°47′ E (Fig. 2). A steeply dipping N–S normal fault separates the studied field from its western neighbor El Morgan field (Abudeif et al. 2016b, 2018; Radwan et al. 2020a). Figure 3 represents the regional lithostratigraphic succession of the study area. During the mid-Miocene active rifting phase, clastic sediments were deposited in the Kareem and Hammam Faraun Formations, which are the principal hydrocarbon-bearing reservoirs in the studied field (Radwan et al. 2019a, 2020a). Reservoir facies is characterized by stacked sandstone units associated with thin shales deposited in a fan delta system (Rhine et al. 1988; EGPC 1996; Peijs et al. 2012; Radwan 2018). The structural contour map at the Belayim Formation top is shown in Figure 4. Late Miocene South Gharib anhydrite acted as seal to the underlying clastic reservoirs (Alsharhan and Salah 1995; Alsharhan 2003; Peijs et al. 2012; Radwan 2014, 2020a, b, c; Rohais et al. 2016).

Figure 1
figure 1

Structural setting of the Gulf of Suez and surrounding areas

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Figure 2
figure 2

Map of basement depth structure of the Gulf of Suez derived from high-resolution aeromagnetic data (modified from Peijs et al. 2012), showing the location of the studied Badri oil field

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Figure 3
figure 3

Oligocene recent lithostratigraphy of the Badri oil field (adapted from Radwan 2014, 2018)

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Figure 4
figure 4

Structural contour map at reservoir top. X1–X4 represent the studied wells in the Badri field. The projection coordinates of the presented map are related to Egypt Gulf of Suez S-650 TL/Red Belt (modified after Abudeif et al. 2018, Radwan et al. 2019a, 2020a, c)

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Materials and Methods

Four offshore wells were studied in the Badri field, which encountered roughly 7000 feetFootnote 1 of the mid-late Miocene Ras Malaab Group of sediments. Wireline geophysical logs were the primary inputs for all the analyses. Log data were processed for environmental and hole size corrections before utilizing in the analyses. Various downhole measurements, i.e., direct formation pressure and leak-off tests were also available and used for calibration and validation of the estimated parameters. Wireline logs along with the direct downhole measurements have been integrated to estimate the mentioned parameters and ascertain the normal faulting potential in the depleted units. The methods employed in this study are discussed below.

Estimation of Mechanical Properties

Rock strength and elastic properties are the basic inputs to geomechanical modeling. In the absence of core-based static rock property measurements, geophysical logs are employed to estimate these parameters. In this study, the dynamic geomechanical properties were estimated by the following equations (Lal 1999; Chang et al. 2006; Zhang 2013):

$$\upsilon d = \frac{{Vp^{2} - 2Vs^{2} }}{{2\left( {Vp^{2} - Vs^{2} } \right)}}$$
(1)
$$Ed = {\text{RHOB}} \times Vs^{2} \left[ {\frac{{3Vp^{2} - 4Vs^{2} }}{{Vp^{2} - Vs^{2} }}} \right]$$
(2)
$$\mu = \tan \left[ {\sin^{ - 1} \left( {\frac{Vp - 1000}{Vp + 1000}} \right)} \right]$$
(3)

where RHOB indicates log bulk density (gm/cc), Vp and Vs represent compressional and shear seismic velocities in m/s unit, d is log-derived Poisson’s ratio (unitless), Ed is dynamic Young’s modulus (in GPa) unit and μ stands for friction coefficient. Rock strength parameters, i.e., cohesive strength (c) and uniaxial compressive strength (UCS) were estimated against the reservoir intervals (Hammam Faraun and Kareem Formations) as (Zoback 2007; Khaksar et al. 2009; Zhang et al. 2010):

$${\text{UCS}}_{\text{Sandstone}} = 1200 e^{ - 0.036DT}$$
(4)
$${\text{UCS}}_{\text{Shale}} = 1.35 \left( {\frac{304.8}{DT}} \right)^{2.6}$$
(5)
$$c = \frac{{{\text{UCS}} \cos \phi }}{{2 \left( {1 - \sin \phi } \right)}}$$
(6)

where DT represents log sonic slowness (is μs/ft) and generated UCS values (in MPa) against sandstones [Eq. (4)] and shales [Eq. (5)], c is the cohesion (in MPa) of the rock and ϕ denotes the internal friction angle in degrees.

Calculation of S v Profile

Vertical stress (Sv) at a depth (H) represents the amount of the overburden pressure exerted by a rock column of height H. Sv is calculated using the log density (Plumb et al. 1991):

$$S_{\text{v}} = \mathop \smallint \limits_{0}^{H} {\text{RHOB}}\left( H \right) \times g {\text{d}}H$$
(7)

where RHOB is log bulk density at the depth (H) of investigation, g is gravitational acceleration (9.8 m/s2). A continuous density profile is required to estimate a vertical stress profile, but in a practical scenario, wireline density logs are never recorded right from the top of a drilled well leaving a data gap in shallow section (Sen et al. 2017; Ganguli and Sen 2018; Radwan et al. 2019a). Therefore, a synthetic density profile was extrapolated from the surface to a well’s drilled depth/target depth (TD) using the Amoco equation (Sen et al. 2019; Radwan et al. 2019a, 2020a).

Calculation of PP

PP is one of the primary input parameters in geomechanical models, as it affects the magnitudes of horizontal stress components. The most reliable PP estimates come from the direct measurements of downhole formation pressure, but these data are normally acquired against reservoir zones (Ganguli et al. 2017; Sen et al. 2020). There are indirect ways to calculating PP from wireline logs (Zoback 2007; Zhang 2011; Sen et al. 2018a, b; Sen and Ganguli 2019; Baouche et al. 2020c, d) by employing a normal compaction line (NCL). The degree of deviation of the mentioned log responses with respect to the NCLs translates to the magnitude of the overpressure (Radwan et al. 2019a, 2020a). However, this method is only applicable to the shale sections. In the studied Badri field, the majority of the lithostratigraphy consists of salt and anhydrites, belonging to Zeit, South Gharib, Feiran and Baba Formations. This possess a huge limitation in employing NCL-based PP estimation method. Therefore, the drilling mud pressure used against these formations was applied as a PP proxy. Usually, mud pressure is kept in such a way that it provides sufficient pressure overbalance (i.e., mud pressure ≥ formation PP) to avoid any formation fluid influx. Direct downhole pressure measurements were available against the primary reservoir units, i.e., Hammam Faraun and Kareem Formations, which guided the PP interpretations against these zones.

Calculation of S hmin

In a tectonically active basin, tectonic stresses and strains arise from tectonic plate movement. If tectonic strains are applied to rock formations, these strains add a stress component in an elastic rock. The poro-elastic horizontal strain model considers tectonic strains and therefore accommodates anisotropic horizontal stresses (Blanton and Olson 1999). Because Gulf of Suez is active tectonically, this study employed a poro-elastic horizontal strain model to ascertain Shmin magnitude. This model involves the tectonic strain components (Najibi et al. 2017) acting on two horizontal stress directions (Javani et al. 2017). The Shmin is estimated as:

$$S_{\text{hmin}} = \frac{\upsilon s}{1 - \upsilon s} \left( {Sv - \alpha PP} \right) + \alpha PP + \frac{\upsilon Es}{{1 - \upsilon s^{2} }} \left( {\varepsilon x + \upsilon s\varepsilon y} \right)$$
(8)

where α denotes the Biot coefficient; and υs and Es denote the static Poisson’s ratio and Young’s modulus. The Biot coefficient has a default value of 1 (Li et al. 2019a, b; Ganguli and Sen 2020). Laboratory-based rock compressibility and bulk modulus measurements can quantify the Biot coefficient. However, in the absence of core-based measurements, we used the default value for the calculation purpose, which is considered a standard modeling practice (Hofmann et al. 2005; Zoback 2007). Static elastic properties were available from core-based measurements. ‘εx’ and ‘εy’ indicate the two strain elements acting on a horizontal plane in mutually perpendicular directions (Kidambi and Kumar 2016; Amiri et al. 2019; Taghipour et al. 2019); thus,

$$\varepsilon x = Sv\frac{\upsilon s}{Es} \left( {\frac{1}{1 - \upsilon } - 1} \right)$$
(9)
$$\varepsilon y = Sv\frac{\upsilon s}{Es}\left( {1 - \frac{{\upsilon^{2} }}{1 - \upsilon }} \right)$$
(10)

In the absence of core-based elastic property data, empirical relationships by Wang (2000) were used to derive the static elastic properties; thus,

$$\upsilon s = \upsilon d$$
(11)
$$Es = 0.4142 \times Ed - 1.0593$$
(12)

where υs and Es are static Poisson’s ratio (unitless) and Young’s modulus (in GPa), respectively. Dynamic elastic properties (i.e., υd and Ed) are calculated using Eqs. (1) and (2), respectively. The estimated Shmin was calibrated with formation integrity test (FIT) and leak-off test (LOT) measurements, which are recorded during drilling, usually below the casing setting depths.

Estimation of S hmax

This is the most difficult parameter in geomechanical modeling because there are no direct measurement methods. Wellbore breakout widths (from image logs) are generally used to constrain the Shmax magnitude (Zoback 2007). Zhang (2011) discussed using an empirical tectonic factor (tf) to estimate Shmax, although the output can be vague and largely over-/underestimated if tectonic factor data for the studied area are not well established from a good volume of well data estimates. Since the image log was not run in the studied wells, we used the poro-elastic model to estimate this parameter. Unavailability of image logs is very common in old producing hydrocarbon fields globally, which poses challenges in Shmax magnitude calibration. However, the poro-elastic horizontal strain model is an established Shmax modeling technique in the absence of image log-based calibration data (Kidambi and Kumar 2016; Javani et al. 2017; Najibi et al. 2017; Abbas et al. 2018; Mohammed et al. 2018; Amiri et al. 2019; Taghipour et al. 2019). PP, calibrated with direct measurements, is one of the main input parameters and, hence, provided increased confidence in this method. The equation is:

$$S_{\text{hmax}} = \frac{\upsilon s}{1 - \upsilon s} \left( {Sv - \alpha PP} \right) + \alpha PP + \frac{Es}{{1 - \upsilon s^{2} }} \left( {\upsilon s \varepsilon x + \varepsilon y} \right)$$
(13)

Input parameters of the above equation are already discussed above.

Stress Path Analysis in a Depleted Reservoir

The stress state within a reservoir is coupled to changes in PP resulting from fluid injection and withdrawal. The most observed coupling is between the least horizontal principal stress and PP. The coupling coefficient, ΔSh/ΔPP, is commonly known as the stress path (Santarelli et al. 1998; Goulty 2003). For a reservoir characterized by vertical stresses larger than horizontal stresses, depletion can induce normal faulting when ΔSh exceeds a critical fraction of ΔPP (Chan and Zoback 2002). The stress path associated with depletion at reservoir scales ranges from 0.34 to 1.18 (Addis1997a; Hillis 2000; Altmann et al. 2010) and it is between 0.6 and 0.8 for overpressured compartments at basin scales (Engelder and Fischer 1994; Hillis 2001; Tingay et al. 2003). Coupling between maximum horizontal principal stress and PP is predicted, but has rarely been reported in the field due to difficulties in measuring the relationship (Zoback and Zinke 2002). Overburden and shear stresses are generally assumed to be decoupled from changes in PP. Based on the poro-elastic theory, the stress path (A) can be expressed mathematically as (Segall and Fitzgerald 1996):

$$A = \frac{{\Delta S_{h} }}{\Delta PP} = \alpha \left( {\frac{1 - 2\upsilon }{\upsilon - 1}} \right)$$
(14)

where α denotes the Biot coefficient. Deformation analysis in reservoir space indicates that, if a depleted reservoir’s stress path surpasses a critical value, ΔPP and ΔSh can induce normal faulting in the reservoir (Zoback and Zinke 2002). In case of a normal faulting scenario, this can be expressed as:

$$\frac{{S_{\text{v}} - \left( {PP - \Delta PP} \right)}}{{\left( {S_{\text{h}} - \Delta S_{\text{h}} } \right) - \left( {PP - \Delta PP} \right)}} = \left[ {\sqrt {\mu^{2} + 1} + \mu } \right]^{2}$$
(15)

In terms of stress path, the above equation can be reduced to:

$$A = 1 - \frac{1}{{\left[ {\sqrt {\mu^{2} + 1} + \mu } \right]^{2} }}$$
(16)

Considering μ = 0.6 (Townend and Zoback 2000; Chan and Zoback 2002), the value of A for normal faulting becomes 0.68. The evolution of stress and PP in a depleting producer unit with a steep stress path can induce normal faulting even if the initial stress state is not close to the shear failure (Zoback and Zinke 2002). In case a depleted reservoir attains this A value, the reservoir stress path intersects the normal faulting limit yielding an unstable stress path. Unstable stress path implies the potential for production-induced normal faulting in the depleted reservoir (Chan and Zoback 2002).

Results and Discussions

Downhole pressure measurements were taken by modular dynamic tester tool (MDT) against the two hydrocarbon-bearing formations, which yielded 0.30 and 0.27 PSI/feet PP gradients against the Hammam Faraun and Kareem Formations, respectively, in the present day. We also had access to the reservoir PP measurements taken in the virgin conditions (prior to production). At the in situ pre-production condition, the Hammam Faraun and Kareem Formations exhibited 0.48 and 0.45 PSI/feet gradients, which can be considered hydrostatic. Due to prolonged depletion, both the formations are presently in sub-hydrostatic PP regime. PP in most of the Zeit Formation and the three members (Feiran, Sidri and Baba) of the Belayim Formation have been interpreted as hydrostatic (0.44 PSI/feet). However, because of ductile nature and solubility, salt/halite is hard to drill with hydrostatic mud pressure. Using drilling fluid pressure as PP proxy, an average 0.57–0.60 PSI/feet PP gradient was finalized against the South Gharib Formation and the basal part of the Zeit Formation. The calibrated PP profiles at both present day and virgin conditions are presented in Figure 5. Results indicate that both the hydrocarbon producers (sandstones of the Hammam Faraun and Kareen Formations) are depleted (i.e., they have sub-hydrostatic condition) while hydrostatic pressure condition is retained between non-producer units of the Feiran, Sidri and Baba Formations, indicating an alternating pressure regression profile.

Figure 5
figure 5

Present-day PP and principal stress magnitudes in well X1, Badri field. ‘MDT’ represents direct measured pore pressure values by Modular Dynamic Tester tool. Green and red dots indicate MDT measurements in depleted and virgin reservoir conditions, respectively. Estimated Shmin is validated with FIT (pink squares) and LOT (black square)

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The studied wells have been drilled to average 165–170 feet2 of water depth. Extrapolated density profile with a shallowest sediment density of 2.15 g/cc provides the best fitting density trend with the wireline bulk density log (Radwan 2019a, 2020a). Vertical variation of Sv is controlled by lithology. For example, anhydrites possess a higher density (2.9 g/cc) than salt/halite (2.1 g/cc), resulting in higher Sv gradient in the Zeit Formation (0.98 PSI/feet) compared to South Gharib (0.94 PSI/feet). Based on the density log-based estimation, at 7000 feet2 TVD (true vertical depth), Sv magnitude is 6850 PSI, which translates to an average 0.978 PSI/feet vertical stress gradient (Fig. 5). Rock mechanical properties were estimated from geophysical logs to characterize formation strength and stiffness, as presented in Figure 6. Based on the log-derived Poisson’s ratio and Young’s modulus and static to dynamic property correlation [Eqs. (11) and (12)], Shmin and Shmax magnitudes were quantified according to the poro-elastic horizontal strain model (Fig. 5). Two FITs and one LOT were available from the lower part of the Zeit and South Gharib Formations, which have been instrumental in the Shmin calibration. Estimated magnitudes (PSI unit3) and gradients (PSI/feet) of PP and principal stress elements are presented in Table 1.

Figure 6
figure 6

Dynamic elastic parameters (Poisson’s ratio and Young’s modulus), friction coefficient and rock strength (cohesive strength and UCS) estimated from geophysical logs in well X1. The presented interval belongs to the Kareem reservoir unit

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Table 1 Estimated magnitudes and gradients of present-day PP and principal stress components
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The estimated stress magnitudes indicate that Sv is the highest principal stress and Shmin is the least stress component (Table 1). Based on the relative stress magnitude distributions (Sv ≥ Shmax > Shmin), a normal faulting stress state is inferred from the studied Badri field. Figure 7 presents a cross-plot between Sv normalized horizontal stress components. Similar stress field observation has been made by Hussein et al. (2013), who interpreted a pure extensional normal faulting regime in the northern (using 18 earthquake events) and southern Gulf of Suez zone (total 79 events) based on the inversion of earthquake focal mechanism (Hussein et al. 2013). Bosworth and Durocher (2017) studied 17 earthquake events (M ≥ 3) during the last 45 years from the central sub-basin of the Gulf of Suez and interpreted a NE–SW extension; the same is supported by wellbore breakout-based observations (Bosworth and Durocher 2017). Zaky (2017) studied the outcrops from the El Quseir region and observed that the Miocene–Pleistocene syn-rift rocks are dissected by a large number of NNW–WNW-oriented normal faults, some of which have oblique slickensides indicating reactivation of these normal faults during a recent extensional movement (Zaky 2017). Our poro-elastic horizontal strain model results are compatible with these previous stress field interpretations, referring to the present-day normal faulting tectonic regime in the studied field.

Figure 7
figure 7

Sv normalized horizontal stress cross-plot from the Badri oil field. Data points indicate a present-day normal fault tectonic stress state in the study area

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Based on MDT measurements in depleted and virgin reservoir conditions, PP drop (ΔPP) across the two reservoirs was quantified. Results indicate a 950–1000 PSIFootnote 2 PP reduction in the Hammam Faraun Formation, whereas PP dropped 1070–1200 PSI against the Kareem (Fig. 8). The vertically stacked Kareem reservoir sandstone units are separated by thin shales over a cumulative 1000 feet1 vertical thickness. A wide range of ΔPP in the Kareem Formation (as interpreted from MDT points) might be due to unequal drawdown from various permeability units across shale barriers. Based on the ΔPP, Shmin dropped in the range of 500–560 PSI in the Hammam Faraun Formation and 570–790 PSI in the Kareem Formation (Fig. 8). These yielded the average stress path (A) values of 0.54 and 0.59 in the upper and lower reservoirs, respectively. Both estimated A values are still far from the critical failure/faulting limit (A = 0.68) (Fig. 9). A higher depletion rate would have potentially increased the stress path values of the reservoirs to the critical level, resulting in depletion-induced reservoir instability. Therefore, it was concluded that the present rate of depletion in the mid-Miocene reservoirs of the Badri oil field will most probably not induce normal faulting.

Figure 8
figure 8

Changes in pore pressure (ΔPP) and minimum horizontal stress (ΔSh) due to depletion in Track 5. PP and Sh of virgin and depleted conditions are plotted on Track 6, along with the geophysical log signatures and downhole measurements (formation MDT pressure points before and after production, FIT and LOT)

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Figure 9
figure 9

PP–Sh cross-plot and stress path (A) of the Hammam Faraun (A = 0.54) and Kareem (A = 0.59) Formations along with normal faulting limit. Depletion, in this case, is unlikely to introduce normal faulting in the two reservoirs

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In this work, we have compared our data with other depleted reservoirs worldwide data to figure out how our findings in the Badri field are far or similar to those in other regions. From our review of the previously published data by many authors worldwide at reservoir scale (e.g., Salz 1977; Breckels and van Eekelen 1982; Whitehead et al. 1987; Woodland and Bell 1989; Teufel et al. 1991; Addis 1997a, b Hillis 2000; Altmann et al. 2010), we concluded that the least recorded stress path value was 0.34 in Canada, while the highest value was 1.18 and it was recorded in the North Sea (Table 2). Hence, by using a simple approach, we classified the A values into three categories (i.e., high, medium and low). To get the values for each category, we subtracted the lowest and highest values and then divided the result by three to define the boundaries of the proposed three categories. The stress path A values in the low category range from 0.340 to 0.620, in the medium category from 0.621 to 0.900 and in the high category from 0.901 to 1.18. The A values in both the middle Miocene reservoirs in this study belong to the low category, similar to Travis Peak Formation and Lake Maracaibo in the USA and Venezuela, respectively (Table 2).

Table 2 Stress path values in different depleted reservoirs worldwide including the studied reservoirs
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Conclusions

This study offers a reservoir geomechanical modeling of the Miocene formations in the Badri oil field, offshore Egypt. Pore pressure and three principal stress magnitudes were quantified from geophysical logs and calibrated/validated with various downhole measurements. A normal faulting tectonic regime was deciphered in the study area. Reductions in PP and Shmin were inferred in the primary reservoirs, i.e., Hammam Faraun and Kareem Formations, because of prolonged hydrocarbon production. Stress path analysis was performed in both mid-Miocene reservoirs to identify any potential depletion-induced normal faulting. This study will be helpful in future planning of infill well drilling, production rate optimization without inducing any reservoir damage or instability.