Introduction

In order to reduce the risks and costs related to the drilling operations, it is crucial to assess the stability of wellbores as part of a comprehensive field study (Ashena et al. 2020). In order to prevent mechanical failures in the wellbore, the process includes preventing chemical reactions between the drilling fluid and well fluid, ensuring a good drilling trajectory, and controlling the drilling parameter (Mondal and Chatterjee 2019). In most cases, drilling problems caused by unanticipated or unforeseen subsurface conditions cost time, money, and well destruction. The problem costs the oil business a lot of money every year (Awal et al. 2001; Bradley 1978). Many steps were taken to optimize such pricey issues as drilling fluid programs and casing programs (Kadkhodaie 2021). The advent of underbalanced drilling technology has led to concerns about how wells that enter horizontally and drill multiple laterals from one well can address these concerns (Awal et al. 2001; Kristiansen 2004; Martins et al. 1999; Tan et al. 2004). As long as drilling does not take place, the stress applied to the ground is less than the rock’s strength, maintaining its equilibrium (Bagheri et al. 2021). A part of the rock column was deflected by the drilling and moved out of the well, which was then replaced by drilling mud and pushed against the wall of the wellbore (Bagheri et al. 2021). As a result, the ground stress equilibrium was disrupted and stress was generated by drilling (Bagheri et al. 2021). It is essential to maintain wellbore stability and regulate these stresses by using the optimal mud weight (Darvishpour et al. 2019). Instability in the wellbore occurs for a number of reasons, including mechanical failures (such as high-stress concentrations, low rock strength, or improper drilling methods) and chemical interactions between the drilling fluid and the rock (most commonly clays) (Kadkhodaie 2021). Washout and wellbore wall failure are usually caused by a combination of mechanical and chemical factors (Kadkhodaie 2021). It is necessary to know the rock strength, select a suitable model, and define an appropriate rock failures criterion in order to determine wellbore stresses. Rock strength properties can be calculated from well logs and empirical equations (Peng et al. 2001; Rahimi 2014). A number of relationships are presented by Heller and Zoback that can be applied to unconfined compressible rocks (sandstones, carbonate rocks, and shale) (Heller and Zoback 2014). They depend on many variables, including sonic wave transit times, ultrasonic wave velocities, elastic moduli, and porosity (Hoseinpour and Riahi 2022). In recent years, several studies have investigated wellbore stability, geomechanical modeling, and associated parameters. Radwan discovered several different data sets and numerical methods to investigate pore pressure and fracture gradients (Radwan et al. 2019, 2020; Radwan 2021). They looked into the connection between in situ stress and reservoir characteristics related to wellbore stability, well decline, and flow rate in addition to reservoir geomechanical modeling (Radwan et al. 2021; Radwan and Sen 2021a, b). According to research by Radwan, there are many different ways to estimate in situ stresses (Radwan 2021; Radwan and Sen 2021a, b). Gholami’s study in the Canning Basin, Australia, focused on geomechanical parameters and stress levels for wellbore stability analysis (Gholami et al. 2017). In a study conducted by Han, the researchers described three steps for determining the poroelastic properties of fractured rocks: measurement, analysis, and interpretation (Han et al. 2019). Khatibi calculated the single-parameter parabolic failure criterion by applying triaxial strength, a geomechanical model of the Iranian oil field, and uniaxial compressive strength estimates (Khatibi et al. 2018). Haimson and Kovacich studied fracture-like breakouts as a mechanism to resolve the wellbore instability associated with porous Berea sandstones (Haimson and Kovacich 2003). Kassem determined the geomechanical parameters of a sandstone reservoir to determine the effect of fluid injection and depletion (Kassem et al. 2021). A shallow unsupported wellbore based on the Drucker–Prager failure criteria was analyzed by Hashemi to create geomechanical models (Hashemi et al. 2014). Carbonate reservoirs have a complex pore system and fractures as compared to clastic reservoirs. Therefore, it is very important to be able to predict pore pressure effectively due to the complexity of the pore system and fractures. Additionally, pore pressure has a direct impact on drilling parameters such as weight on bit, which has a direct effect on drilling rate. Moreover, geomechanical studies also take into account the pore pressure and rock strength parameters (Khoshnevis-zadeh et al. 2021). Khoshnevis-zadeh concentrated on the K4 member of the Dalan Formation by using drilling data such as weight on bit (WOB) to measure and compare the dynamic parameters calculated by well log instead of core data. Thus, the porosity and density of the rock in the wellbore are directly correlated; both of those variables are impacted by the strength of the wellbore and the depth of penetration (Khoshnevis-zadeh et al. 2019).

Studies on well stability and geomechanical parameters in carbonate formations have not been sufficiently extensive. This study specifically focuses on estimating mud weight by using geomechanical parameters to determine the accuracy of elastic moduli using laboratory tests and well log data from the Kangan and Dalan Formations. Additionally, safe drilling deviation trajectory standards are being developed using these findings. The outcome of this study can potentially be used to arrive at a good development plan, and the overall well development can be optimized through the use of the best engineering design in the carbonate formation.

Study area

The South Pars gas field is located in the Persian Gulf in Iran’s southern part. In the South Pars gas field, the well that was studied is located. Our study was conducted on the Kangan and Dalan Formations. The Dalan and Kangan Formations are of late Permian and early Triassic age, respectively, and both are composed primarily of carbonate rocks. The Dalan and Kangan are, respectively, late Permian and early Triassic, and carbonated lithology dominates both formations (Rahimpour-bonab 2007). Accordingly, the reservoir zone can be divided into four distinct sections, depending on the stratigraphic aspects, and these sections are known as K1, K2, K3, and K4 (Khoshnevis-zadeh et al. 2021). Accordingly, sections K1 and K2 belong to the Kangan Formation, and sections K3 and K4 belong to the Dalan Formation (Khoshnevis-zadeh et al. 2021). Specifically, the majority of hydrocarbons are found in the upper Dalan and Kangan Formations, which are equivalent to the upper Khuff Formation (Esrafili-dizaji and Rahimpour-bonab 2019). Figure 1 illustrates the location of the study area and the stratigraphic column of that area.

Fig. 1
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The map to the left shows the location of the studied area, while that to the right shows the general stratigraphy of the South Pars gas field (Rahimpour-bonab 2007)

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Materials and methods

The conventional well logs, which include density, sonic logs porosity together with formation micro-image (FMI), are used in this study. Additionally, data were obtained from rock mechanical tests including uniaxial compressive strength tests, unconfined modulus of elasticity, Poisson’s ratio, and pore pressure values gathered from modular dynamic tester instruments. This study presents geomechanical analyses derived from well log data and direct downhole measurements in one well. The standard well logs used in the geomechanical model are depicted in Fig. 2. From left to right, they are as described as follows: The first track shows the delta-t compressional log. Delta-t shear log is represented on the second track, neutron porosity log on the third, and formation density log on the fourth track.

Fig. 2
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Petrophysical logs were used for the geomechanical model in the Kangan and Dalan Formations

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The best drilling and completion methods can be estimated and predicted using a geomechanical model, either for drilling within a borehole or for developing a field (Ashena et al. 2022). Figure 3 shows the workflow and general steps used in this work.

Fig. 3
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Step-by-step process for building a geomechanical model

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Rock elastic parameters

An analysis of density and acoustic logs can determine a variety of rock strength parameters, including Young’s modulus, Poisson’s ratio, bulk modulus, and shear modulus (Khoshnevis-zadeh et al. 2019). Empirical equations were used to convert the calculated parameters into static modules (Khoshnevis-zadeh et al. 2019).

Rock strength

Rock resistance to in situ stresses around the wellbore is referred to as rock strength. Unconfined compressive strength (UCS) is a frequently used indicator of the power of rocks. For UCS calculations, measurements from records are typically utilized. Unconstrained compression tests or multistage triaxial compression tests on cores are frequently used to calibrate continuous logs at a single location. Equation 1 can be used to determine the UCS. Tensile strength (TSTR) is calculated by dividing UCS by a predetermined factor. It typically ranges from one-eighth to one-twelfth of UCS (Zoback 2010). Here, tensile strength is equivalent to one-tenth of uniaxial compression strength.

$${\text{UCS}} = \frac{{\frac{7682}{{\Delta t}}^{1.82} }}{145}$$
(1)

where \(\Delta {\text{t}}\) is the measured sonic transit time in µs/ft.

Static Young’s modulus and unconfined compressive strength

Laboratory test results were used to calibrate static data generated from dynamic data (static values). We determined the static Young’s modulus and unconfined compressive strength in the examined field using Eqs. 2 and 3, as well as earlier research and laboratory experiments.

$$E_{{{\text{sta}}}} = \left( { - 2.21 \times {\text{PIGN}} + 0.963} \right) \times E_{{{\text{dyn}}}}$$
(2)
$${\text{UCS}}_{{{\text{sta}}}} = \left( {3 + 4.1 \times E_{{{\text{sta}}}} } \right) \times 145$$
(3)

where \({{\text{E}}}_{{\text{sta}}}\) = static Young’s modulus; \({{\text{E}}}_{{\text{dyn}}}\) = dynamic Young’s modulus; \({\text{PIGN}}\) = porosity log.

Accordingly, in this study, ASTM D 3148–93 and ISRM suggested methods were considered. Briefly, a rock core specimen was cut to length and the ends were machined flat. The specimen was placed in a loading frame. In addition, axial and lateral deformation were monitored as a function of load on the specimen, as axial load continuously increased. The specimens were tested at room temperature under dry conditions. In order to preserve natural conditions, specimens from the reservoir were not washed. The specimens were tested at a stress rate of 0.75 to 1.0 MPa/s.

In Table 1, the results of the uniaxial compressive strength test, elasticity modulus, and Poisson’s ratio measurement are listed.

Table 1 The results of the uniaxial compression test plus unconfined modulus of elasticity and Poisson’s ratio determination
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Overburden pressure

Pressure profiles are created by combining the vertical column pressures of various rocks with the pore pressures in overburden stress (Almalikee and Al-najim 2018). In subsurface studies, the overburden pressure is affected by different factors, such as the weight of upper layers and porosity of the rock, density of fluids in the pores, diagenetic impact, and tectonic factors. Overburden pressure is determined by the thickness of the bed and density of the bed (Zoback 2010). According to Eq. 4, it was calculated (Zang and Stephansson 2009).

$$S_{v} = \mathop \int \limits_{0}^{z} \rho_{z} gd_{z} \cong \overline{\rho }gz$$
(4)

Rock density is \(\uprho\) and z is formation thickness, respectively.

For intervals without density logs, the vertical stress was calculated by averaging the densities of subsurface formations in relation to lithology (Kadkhodaie 2021).

Prediction of the pore pressure

Typically, pore pressure is measured using a repeat formation tester (RFT), a modular dynamics test (MDT), well test analysis, and logging while drilling sensors (Ashena et al. 2020). There are a large number of carbonate reservoirs worldwide, particularly in the Middle East (Khoshnevis-zadeh et al. 2021). Thus, the compaction trend in carbonate rocks is influenced both physically and chemically by a variety of parameters, including pore system complexity, diversity extent, and the determination of pore structure (Croizet et al. 2013). The pore pressure gradient was expressed by the Eaton sonic equation using Eq. 5 (Azadpour et al. 2015).

$${P}_{pg}={S}_{g}-\left({S}_{g}-0.86\right){\left(\frac{50+\left(185-50\right){e}^{-0.00137z}}{\Delta t}\right)}^{0.45}$$
(5)

where \({{\text{S}}}_{{\text{g}}}\) is the vertical stress gradient, \(\Delta {\text{t}}\) is the measured sonic transit time, and z is depth.

Discussion of results

In the current study, a complete set of subsurface data including conventional and advanced well logging data, core data, rock mechanical test results and formation test (MDT) data were integrated to make an optimal geomechanical model for the Permian–Triassic gas reservoirs of the world’s largest non-associated gas field, Persian Gulf. For this purpose, firstly dynamic geomechanical properties were calculated using density (RHOB), compressional sonic, compressional wave velocity (Vp), and shear wave velocity (Vs) logs (Radwan et al. 2021) (Fig. 4). As is seen, the following tracks from left to right are shown, respectively: P wave modulus (track 1), shear modulus (track 2); Bulk’s modulus (track 3); Poisson’s ratio (track 4), the compression to shear velocity ratio (track 5) and Young’s modulus (track 6). Afterwards, UCS and TSTR were estimated to plan the mud weight window, analyze wellbore stability and bit selection (Khoshnevis-zadeh et al. 2019) (Fig. 5). An optimal pore pressure prediction is essential to determining the magnitude of horizontal stresses (Radwan et al. 2021). As shown in Fig. 6, the pore pressure gradient calculated by Eaton sonic equation was compared with the modular formation dynamic tester (MDT) available for Kangan and Dalan Formations.

Fig. 4
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A dynamic elastic parameters calculation was performed on the Kangan and Dalan Formations

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Fig. 5
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The UCS and TSTR were calculated by empirical equations and laboratory test

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Fig. 6
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The pore pressure gradients were calculated from Eaton’s method and calibrated with MDT results

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Prediction of the minimum and maximum horizontal stresses

Prediction of minimum and maximum horizontal stresses is complicated and some approximations must be made. The poroelastic hypothesis was frequently used to determine the magnitude of horizontal stresses. According to the poroelastic theory, pressure-induced deformation of porous media results in volume changes. Applying this process to reservoir rocks with fluid-saturated pores within the elastic matrix is an especially effective strategy. Such a pore pressure changes caused by saturating pore fluids help to promote pore fluid flow in porous media as well as stiffen the material (Kadkhodaie 2021). Viscose fluids have a time-dependent reaction in porous media systems. Stresses and strains resulting from tectonic plate movement are encountered in tectonically active basins. When tectonic strains are applied to rock formations, they add stress to elastic rocks (Abdideh and Fathabadi 2013).

In order to calculate the minimum horizontal stress, the following equations based on poroelastic theory (Eqs. 6, and 7) were used.

$$\sigma_{h} = \frac{\upsilon }{1 - \nu }\left( {\sigma_{v} - \alpha P_{p} } \right) + \alpha P_{p} + \frac{{E\varepsilon_{y} }}{{1 - \upsilon^{2} }} + \frac{{E\varepsilon_{x} }}{{1 - \upsilon^{2} }}$$
(6)
$$\sigma_{H} = \frac{\upsilon }{1 - \nu }\left( {\sigma_{v} - \alpha P_{p} } \right) + \alpha P_{p} + \frac{{E\varepsilon_{x} }}{{1 - \upsilon^{2} }} + \frac{{E\varepsilon_{y} }}{{1 - \upsilon^{2} }}$$
(7)

\({\varepsilon }_{x}\) and \({\varepsilon }_{y}\) are the linear strain elongation components on the “x” and “y” axis directions, respectively, α represents Biot’s coefficient (was set to 1). In Fig. 7, the horizontal stresses, as well as the vertical stress, are illustrated (Eaton 1976). The minimum horizontal stress (\({\upsigma }_{{\text{h}}}\)) was recalculated based on drilling evidences such as mud loss and appearance of shear and tensile fractures in the wellbore as well as the strain coefficients of \({\varepsilon }_{x}\) and \({\varepsilon }_{y}\) were adjusted until they reached optimal values meeting the stress regime model (Talebi et al. 2018). This equation predicts that the borehole breakout will initiate along the azimuth of \({\upsigma }_{{\text{h}}}\) when the maximum effective circumferential stress at the borehole exceeds the compressive rock strength (Moos and Zoback 1990).

Fig. 7
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The horizontal stresses and vertical stress profiles in well A

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Field stress regime analysis

According to the world stress map and regional tectonic studies, strike-slip faults are the predominant stress regime (Bozorgi et al. 2016). Anderson’s fault classification for strike-slip fault systems predicted that the magnitude of the maximum horizontal stress would be greater than the magnitude of the vertical stress and minimum horizontal stress (Fig. 8), respectively (\({\upsigma }_{{\text{H}}}>{\upsigma }_{{\text{v}}}>{\upsigma }_{{\text{h}}})\) (Kadkhodaie 2021). Identifying the maximum horizontal stress orientation and minimum horizontal stress orientation can be established from the FMI log. By contrast, breakouts are generally directed along the path of least horizontal stress (\({\upsigma }_{{\text{h}}}\)), while induced fractures are generally directed along the path of maximum horizontal stress (\({\upsigma }_{{\text{H}}}\)) (Kadkhodaie 2021). Figure 9 indicates that maximum stress is located in azimuths of 30° and 240° (NE-SW direction) based on FMI logs interpretation. There were breakouts (Fig. 10) in azimuths 60◦ and 330◦ (NW–SE) illustrating the direction of the minimum horizontal stress.

Fig. 8
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An example of a borehole breakout and a drilling-induced fracture. There are equations related to the minimum and maximum hoop stresses (Fjær et al. 2008)

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Fig. 9
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Observed induced fractures on the FMI log (a) and rose diagram showing the induced fractures orientation (b) in well A

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Fig. 10
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Observed breakouts on the FMI log (a) and rose diagram showing the breakouts orientation (b) in well A

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Three types of borehole instability factors exist, including chemical, mechanical and combinations of both. A mechanical factor is mainly determined by the weight of the mud (too light or too heavy) and the drilling method (depth, vibration, and lifting of pipes), whereas a chemical factor is greatly influenced by the drilling mud, namely inappropriate mud and insufficient inhibitors. The cause of borehole instability is usually a combination of chemical and mechanical factors. There is always pressure on underground structures due to vertical and tectonic stresses. During the drilling of a borehole in a geologic structure, some materials (rock) are taken out of the borehole. Only fluid pressure maintains the side walls of a borehole. As a result of inconsistencies between fluid pressure and the principal in situ stresses, new stress redistributions occur around the borehole, causing rock fractures. To analyze borehole stability issues, it is essential to identify the stresses present around the borehole. Borehole fractures can be divided into two types: drilling-induced tension fractures (DITF) and breakout fractures (Abdideh and Fathabadi 2013). Accordingly, the proper mud weight should be greater than the pore pressure to avoid formation collapse. In other words, formation’s horizontal stress and failure pressure should not be less than its mud weight as shown in Fig. 11. To estimate the elastic properties of the rock, data from logs, including sonic (DT), bulk density (RHOB), and a formation micro-image (FMI), were taken from 8631 to 10,545.5 ft of well A in the study area. Overall, various types of data were used, including formation test, well logs and rock mechanical tests. In this study, it was conducted using the STABView software for building a geomechanical model. Data sets were imported into this software are shown in Table 2.

Fig. 11
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The safe mud window is defined as the pressure between pore pressure and minimum horizontal stress (Abdideh and Dastyaft 2022)

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Table 2 The data sets were used to build geomechanical model
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Figure 12 represents the drilling mud weight (equivalent to collapse pressure) in black. When drilling fluid pressure drops below the black line, causes the wellbore to collapse, increasing or decreasing the diameter of the wellbore. A shear failure may occur at low levels of least mud weight, referred to as collapse pressure. Shear failure occurs at greater depths, while tensile failure occurs near the surface at the shallower depths. Loss of circulation increases the probability of drilling fluid entering formations. Tensile failure ranges with a higher amount are known as fracture pressures (Gao et al. 2019a, b, c). Mohr–Coulomb failure criteria and two parameters are often used to determine shear strength. These are failure envelope cohesion and internal friction angle (Hoseinpour and Riahi 2022). According to Figs. 13, 14, 15, and 16, drilling direction and mud weight can provide an indication of the stability and instability of certain depths along the examined well. Using stereonets, the blue color represents locations where drilling orientations are safe (stable orientations), and the red color indicates unfavorable conditions (unstable orientations). A point on the figures (Figs. 13 through 16) indicates the depth at which mud is employed. It is necessary to specify the optimal deviation, azimuth, and mud weight in order to prevent tensile and shear failures. Polar plots in the STABView are shown in Figs. 13, 14, 15, and 16.

Fig. 12
figure 12

The drilling safe weight window in the study area is shown in yellow for the Kangan and Dalan Formations. Mohr–Coulomb failure criteria used to determine the wellbore trajectory, which is determined by fracture gradient, drilling pressure, and mechanical conditions

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Fig. 13
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Polar plot showing required mud weight at different azimuths, K1 member of Kangan Formation

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Fig. 14
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The wellbore inclination and azimuth affect the mud weight window in K2 member, Kangan Formation

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Fig. 15
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The maximum and minimum horizontal stresses in K3 member, Dalan Formation

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Fig. 16
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The inclination and azimuth of the wellbore affect the mud weight window in K4 member, Dalan Formation

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In the Kangan Formation, deviations greater than 50° may cause various problems, including wellbore breakdown. Therefore, the safe mud weight window for drilling in the K1 and K2 members of the Kangan Formation is from 9.66 to 11.55 ppg and 10.03 to 11.64 ppg, respectively, at an azimuth of 277° and inclination of 35° (Figs. 13 and 14).

In the Dalan Formation, the minimum horizontal stress (\({\upsigma }_{{\text{h}}}\)) direction is NE-SW, while the maximum horizontal stress (\({\upsigma }_{{\text{H}}}\)) direction is NW–SE. In the Dalan Formation, drilling at more than 60° inclinations can cause wellbore breakout problems. As a result, the equivalent mud weight window for the K3 and K4 members of the Dalan Formation is 11.29 to 12.65 ppg and 10.55 to 11.33 ppg, respectively, with 45° of drilling inclination and 277° of azimuth (Figs. 15 and 16).

The safe mud window is defined as the area between the collapse pressure and the minimum horizontal stress. Figures 17, 18, 19, and 20 show the effects of the well inclination (Equivalent Mud Weight, EMW) on collapse pressure for the Kangan and Dalan Formations. Results show that the maximum horizontal stress requires a higher mud window. Parallel to the \({\upsigma }_{{\text{H}}}\), Azimuths of 30° and 240° are likely to have higher mud weight. Similar to the issue of drilling inclination, a high mud weight will be required to overcome borehole collapse when drilling inclination increases borehole instability.

Fig. 17
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An analysis of collapse pressure (EMW) in terms of well inclination in K1 member, Kangan Formation

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Fig. 18
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Plot showing the effect of well inclination on collapse pressure in K2 member, Kangan Formation

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Fig. 19
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Plot showing the effect of well inclination on collapse pressure in K3 member, Dalan Formation

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Fig. 20
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Plot showing the effect of well inclination on collapse pressure in K4 member, Dalan Formation

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A significant challenge, when developing carbonate reservoirs, is the potential for abnormal pore pressures zones causing geological hazards (Khan et al. 2022). As abnormal (high) pore pressure enhances friction between formation and bit, it may contribute to wellbore instability issues, such as borehole collapse (Atashbari and Tingay 2012). Pore pressure variations in carbonates are caused by different types of porosity formed during diagenesis (fracturing and dissolution), depositional fabrics and deformation mechanisms (stress) (Khan et al. 2022). Pore pressure in carbonates is mostly caused by compaction disequilibrium regulated by the physical characteristics of the rock and pore fluids (Atashbari and Tingay 2012; Mohammed 2017). A variety of factors influence wellbore instability and the optimal mud weight in order to avoid sticking and borehole collapses, which cause borehole diameters to be smaller or larger and increase wellbore collapse in many formations such as shale and carbonate formations (Hoseinpour and Riahi 2022). This foundational information is crucial, particularly in the vicinity of faults and other geological complexities (Sanei et al. 2023). Identifying the magnitude and orientation of in situ stresses will aid drillers and reservoir engineers in determining the most appropriate safe trajectory for maximum productivity (Abdelghany et al. 2021; Bashmagh et al. 2022). Horizontal stresses calculations suffer from lack of leak-off test (LOT) or extended leak-off test (XLOT) data. In this regard, it was tried to optimize strain parameters (\({\varepsilon }_{x}\) and \({\varepsilon }_{y}\)) until the mechanical earth models of the study area are well matched with field stress regime. Furthermore, the direction of horizontal strains can be inferred or verified using the full arm caliper data when borehole image logs are not available in all wells (Abdelghany et al. 2023). It was found that in the studied field, horizontal wells drilled parallel to maximum horizontal stress (the most stable direction) where their stress anisotropy is the minimum (Peška and Zoback 1995). Khoshnevis-zadeh compared rock strength parameters with WOB instead of core data due to lack of availability (Khoshnevis-zadeh et al. 2019). Research on the investigated in situ stresses and wellbore stability analysis could provide valuable information for petroleum exploration and development and suggest possible areas for further exploration.

Conclusions

The current study showed a successful application of wellbore stability workflow on one of the gas wells of the Persian Gulf by using extensive data set of well logs, well test, and laboratory results. The following conclusions were reached.

  • Stress calculations based on FMI logs showed that the stress regime is commonly strike-slip. Wellbore stability was analyzed using the Mohr–Coulomb failure criterion for the Kangan and Dalan Formations.

  • According to the results, the mud weight applied in the Kangan and Dalan Formations was within a safe range. In addition, sensitivity analyses showed the optimal azimuth, deviation, and mud weight ranges. The optimal mud weights for the Kangan and Dalan Formations are 10–13 ppg and 11–14 ppg, respectively.

  • Well azimuths in the NE-SW direction provide the best drilling stability in the studied carbonate formations.

  • It was shown that the worst drilling direction that could lead to a high risk of wellbore instability is the azimuth of the minimum horizontal stress (\({\sigma }_{h}\)). This is because drilling in this direction causes the pore pressure to exceed the tensile failure stress or fracture pressure, causing the wellbore to be damaged.

  • It is recommended to develop a 3D/4D numerical model to improve the understanding of the relationships between field depletion and changes in stressed magnitude and orientation. Results of this research will help prevent borehole instability in the South Pars gas field and help drill similar wells in the future.

  • The use of core samples and core data is very important for delivering reliable results, so we utilized three core samples and core data for the purpose of this study. In addition, using Eaton’s method, pore pressure gradients were also calculated, that provided valuable information when calibrated with MDT results.