STUDY OF THE BACTERICIDAL-INHIBITOR PROPERTIES OF NEW REAGENTS

1. Introduction

Geomechanics plays a crucial role in minimizing hazards during drilling operations. It is used to analyze fault seal potential, predict pore pressure, assess wellbore stability, and evaluate various stimulation methods (Lanin, 2021). With current technological advancements, various methods have been applied in geomechanical data processing, one of which is machine learning-based approaches such as real-time LWD analysis. This technology enables the rapid and accurate estimation of geomechanical parameters, thereby supporting more effective, safe, and efficient decision-making during drilling operations and reservoir management (Liu et al., 2025). Neglecting geomechanical considerations has led to numerous incidents, including the Lapindo mudflow disaster. This disaster was triggered by subsurface overpressure, where the drilling mud weight was insufficient to counteract the formation pressure. As a result, the high-pressure mud erupted to the surface (Nuraini, 2023). This incident underscores the importance of geomechanical applications, particularly developing a Mechanical Earth Model (MEM), to mitigate subsurface risks.

The Mechanical Earth Model (MEM) is a comprehensive representation of rock mechanical properties, fracture behaviour, and subsurface stresses, pressures, and temperatures at depth. Geoscientists use MEM to predict rock deformation and failure during drilling. MEM integrates geological, geophysical, and field measurement data, including core tests, well logs, drilling records, and production data (Zain et al., 2020). Additionally, MEM helps estimate the maximum allowable fluid injection pressure, which must be carefully calibrated based on reservoir pore pressure and in-situ stress conditions. Excessive injection pressure can fracture cap rocks, leading to fluid leakage into unintended formations (Rozamuri et al., 2022). Mechanical Earth Models (MEMs) can be categorized into one-dimensional (1D) and three-dimensional (3D) models. The 1D MEMs demonstrate significant potential in addressing various challenges associated with reservoir planning and management. Furthermore, they also serve as a foundational step toward the development of 3D MEMs. This progression can enhance the understanding of reservoir behaviour and improve predictive capabilities in geologically complex environments (Verma et al., 2021).

Previous geomechanical studies on the Keutapang Formation were conducted by Aziz & Bolt (1984) and Yosandian et al. (2014). Aziz & Bolt (1984) attributed overpressure in the formation to high sedimentation rates and recommended a mud weight of 16.5 ppg for safe drilling. Similarly, Yosandian et al. (2014) confirmed overpressure in the Keutapang Formation, with density-sonic transit time cross-plots showing no evidence of mineral transformation. Another geomechanical study was conducted in southwest Iran, which used 1D MEMs to predict pressure and deformation changes, also assess the risk of fault reactivation and caprock stability. This approach can recognize the risk management of injection in old reservoirs. Although the processing is easier, if the data is incomplete, it can make the model less accurate and the risk of failure increases due to extreme conditions that cannot always be predicted (Saadatnia et al., 2023). This study aims to further investigate subsurface conditions through geomechanical analysis and determine the maximum safe injection pressure limit Keutapangan Formation, “ZA” Field, North Sumatra Basin.

2. Geological Setting

The North Sumatra Basin is one of Sumatra’s three back-arc basins, situated along the western margin of Sundaland and spanning approximately 60,000 km², covering both onshore and offshore areas (Tampubolon et al., 2017). It is bounded by (see Figure 1); The Barisan Mountains to the southwest (SW), the Asahan Arch to the southeast (SE), the Andaman Sea to the northwest (NW), and the Malacca Platform to the northeast (NE).

The basin’s dominant structural trends align in NW-SE and NE-SW directions. As shown in Figure 1, the North Sumatra Basin lies within a tectonically active zone on the western edge of the Sibumasu Block, where multiple tectonic events have significantly influenced its geological evolution. The basin’s complex structural framework results from rotational and translational movements of major tectonic plates (Wardhana et al., 2021). The basin’s tectonic model is primarily governed by a dextral strike-slip fault system (Wardhana et al., 2021).

Figure 1. North Sumatra Basin (Wardhana et al., 2021)

Figure 2. Basin stratigraphy (Syarifuddin et al., 2018)

Based on the stratigraphic column of the North Sumatra Basin (see Figure 2), nine formations have been identified:

3. Materials and Methods

This research involves 26 wells across the North Sumatra Basin. The data used includes wireline logging, pressure test, fracturing report, and well reports. The wireline logging data used comprises gamma ray logs, sonic logs, and density logs, all of which are available for every well. Pressure test data only available for one well (Well-23) and fracturing report data, includes step rate test data and fracture gradient, is available for only one well (Well-7). All geomechanical data calculations were performed in Microsoft Excel, where the processing flow can be seen in Figure 3.

Figure 3. Flowchart

The data limitations and applications are that the limited availability of pressure test data (only one well) limits direct comparisons across the field and prevents extrapolation to other wells, single-well step rate test is applied conservatively and locally without generalization to field-wide operations, and fracture gradient data serves as key validation for stress field modelling. The integration of these datasets enables robust pore pressure prediction through Eaton's method with empirical validation, accurate determination of injection pressure limits for reservoir management, and comprehensive stress field characterization for wellbore stability analysis

All data processing and interpretations follow standard petroleum engineering protocols, with quality control measures applied to ensure data reliability. Where data is limited (pressure tests, step rate tests), conservative engineering approaches are employed to mitigate uncertainty. While this study does not include direct validation using numerical simulations, core-based MEMs, or machine learning approaches, the mechanical parameters and equations adopted in the 1D MEM construction are consistent with widely accepted geomechanical modelling practices as demonstrated in numerous previous studies, such as Zain et al. (2020), Shen et al. (2024), and Larsen et al. (2000).

Overburden stress is caused by the weight of the overlying formation layers. Density logs are typically used to determine the overburden stress. The value of the overburden stress can be obtained using the equation (Knoll, 2016),

Hydrostatic pressure is caused by static or non-moving fluid at a certain depth (Wicaksono, 2021). Hydrostatic pressure is defined by

The Normal Compaction Trend (NCT) represents a linear relationship derived from optimally compacted intervals in low-permeability formations. This fundamental geomechanical parameter serves two critical functions, i.e. baseline reference for normal pressure conditions, and a diagnostic tool for identifying geopressure zones where pore pressure exceeds hydrostatic pressure (see Figure 4).

Figure 4. Pore pressure prediction based on NCT (Shaker, 2007)

Key characteristics of NCT exhibit an exponential decrease with depth, it requires Δt_mudline (sonic transit time at mud-line) as the input parameter, typical range of 185-200 μs/ft (Zhang, 2013), and formation-specific variations may occur based on lithology.

In pore pressure prediction, the primary reference for Eaton's method calculations and critical for identifying transition zones between pressure regimes, enables the detection of under-compaction anomalies (Shaker, 2007). The NCT curve's reliability depends on the quality of sonic log data, proper selection of reference shales, and accounting for regional geological variations. This methodology provides the foundation for accurate pore pressure prediction, essential for well planning and drilling hazard mitigation.

Pore pressure refers to the fluid pressure within rock pore spaces. Under normal hydrostatic conditions, it equals the pressure exerted by a vertical column of formation water. Direct measurement techniques include Repeat Formation Tester (RFT), and Modular Dynamic Tester (MDT) (Wicaksono, 2021). Wireline log-based pore pressure calculations achieve greater accuracy in shale intervals than in sandstone due to fundamental differences in compaction behaviour:

Especially in Shale Characteristics: extremely low permeability inhibits fluid escape, mechanical compaction under overburden stress leads to fluid retention and pressure buildup, and diagenetic processes can further enhance overpressure conditions. While for Sandstone behaviour, higher permeability enables fluid redistribution, and rapid pressure equalization occurs despite diagenetic effects (Osborne, 1997).

The Eaton method was developed for the Gulf of Mexico's undercompacted, overpressured shales. This widely used technique derives from the principle of effective stress. The method can be identified the specifically designed for shale formations; incorporates sonic, resistivity, or density log data; relies on normal compaction trend analysis; and is particularly effective in young, Tertiary basins.

This equation to approach remains fundamental in modern pore pressure prediction workflows, though it requires careful calibration with direct pressure measurements in each basin.

Terzaghi’s principle states that when a rock experiences vertical stress, that stress is countered by the pore fluid pressure within the rock, which consists of pore pressure and effective stress. Effective stress can be correlated with log data such as resistivity, wave velocity, or transit time to establish the Eaton relationship in (Knoll, 2016)

The elastic properties of rocks can be classified into static and dynamic elastic properties. Static elastic properties are obtained through core analysis to determine Young’s modulus and Poisson’s ratio, with depth-specific information limited to the locations where core samples are taken. In contrast, dynamic elastic properties are derived from sonic log measurements (Knoll, 2016). Based on laboratory testing, static and dynamic elastic properties are not identical due to differences in strain magnitude. The measured modulus is not purely elastic, as it includes additional irreversible deformation caused by friction. This indicates that static strain is always greater than dynamic strain, resulting in static elastic modulus values that are consistently lower than dynamic elastic modulus values (Knoll, 2016). The equation for static Young’s modulus is presented in (Amin et al, 2022);

Poisson’s ratio is defined as the (negative) ratio of lateral strain to axial strain under uniaxial stress conditions (Tandirerung, 2023). The equation for Poisson’s ratio is presented in Christaras et al. (1994);

Rock strength is the ability of a rock to resist external deformation when subjected to external stress (Gunawan et al., 2017). Rock strength can be represented by unconfined compressive strength (UCS), tensile strength, and friction angle. An empirical approach is commonly applied using various correlation methods. One such technique involves relating uniaxial compressive strength (UCS) to the compressional wave transit time interval, as shown in Equation 8 (Amin et al., 2022). Rock strength based on UCS is classified into four categories, as presented in Table 1 (Attewell & Farmer, 1976).

Table 1: Rock strength classification based on UCS (Attewell & Farmer, 1976)

Rock strength refers to a formation's capacity to withstand external stresses without permanent deformation (Gunawan et al., 2017). This fundamental geomechanical property is typically quantified through three key parameters;

This classification system provides essential guidance for Wellbore stability analysis, Hydraulic fracturing design, Sand production prediction, and Drilling parameter optimization

The empirical correlations remain particularly valuable when core data is unavailable, though they require regional calibration for optimal accuracy in different geological settings (Moos et al., 1999 in Knoll. 2016).

Tensile strength is defined as the maximum amount of tensile stress that a material can withstand before failure occurs. In the context of rock mechanics, tensile strength is crucial as it affects the stability and integrity of rock structures (Perras & Diederichs, 2014). According to Perras & Diederichs (2014), an empirical equation for tensile strength is presented in;

The tensile strength can be classified for various rock types based on Lockner (1995), as shown in Figure 5.

Figure 5. Classification of tensile strength (Lockner, 1995)

The friction angle is a measure of a rock’s ability to resist shear stress. It represents the ratio between the normal force and the resultant force during failure caused by shear stress. The friction angle, along with UCS, is essential for many commonly used failure criteria to estimate rock strength at depth. An empirical equation to calculate the friction angle is presented in Zain et al. (2020);

Based on Linh et al. (2021), the friction angle is categorized into three groups, as shown in Table 2.

Table 2. Classification of friction angle (Linh et al., 2021)

Minimum horizontal stress represents the threshold at which rock begins to fracture, indicating the rock's strength limit (Wicaksono, 2021). This stress can be measured using extended leak-off tests (XLOT) or mini-frac tests. In these methods, fluid is injected into the wellbore to pressurize the exposed rock interval until fracturing occurs. As pumping continues at a constant rate, the fracture propagates away from the wellbore. The fracture grows in a direction perpendicular to the least principal stress, as this orientation requires the least energy for propagation. Consequently, the pressure required to propagate the fracture is equal to or greater than the minimum horizontal stress (Knoll, 2016).

Fracture propagation will cease when the rate of fluid leakage from the fracture and wellbore into the formation exceeds the rate of fluid injection. If pumping is stopped, fluid continues to leak from the fracture walls until the fracture closes and becomes disconnected from the wellbore. The fracture closes once the pressure falls below the normal stress acting on it, which corresponds to the minimum horizontal stress, and the value of the minimum horizontal stress can be calculated using an equation (Knoll, 2016),

The injection pressure window refers to the pressure range used to inject fluid into the reservoir, where the pressure must be high enough to induce shear failure and enhance permeability, yet low enough to avoid damaging the formation. Elevated injection pressures can trigger shear failure, thereby improving permeability. An effective degree of shear failure can be achieved by maintaining injection pressures within this optimal window (Collins, 2002).

Determining the appropriate injection pressure window requires an understanding of both the pore pressure and the minimum horizontal stress. Pore pressure represents the fluid pressure within the formation, while the minimum horizontal stress defines the lower bound beyond which rock fracturing will occur.

Underpressure is defined as a pressure that is lower than hydrostatic pressure. It typically occurs when fluids are extracted from a reservoir, leading to a reduction in fluid volume and, consequently, a decrease in pore pressure. This phenomenon is commonly observed in depleted fields—reservoirs that have already produced oil and gas.

In addition to production-related causes, underpressure can also occur naturally. Naturally occurring underpressure can be classified into gravity-driven and geologically driven processes. Gravity-driven underpressure arises from changes in elevation, flow rates, or the selected reference for the hydrostatic gradient. In contrast, geologically induced underpressure results from geological changes that alter the intrinsic properties of rocks or the pressure–volume–temperature (PVT) conditions of fluids, preventing the fluid pressure from equilibrating with its surroundings (Birchall et al., 2022).

Overpressure can be categorized into two main mechanisms: loading and unloading. The loading mechanism (see Figure 6a) is typically caused by rapid sedimentation in a basin, during which the fluids within the rock pores are unable to escape— a process known as disequilibrium compaction. This mechanism generally produces consistent patterns in sonic and density logs, reflecting the imbalance between fluid retention and the rate of pore compaction. As sediment accumulates, vertical stress—or gravitational loading—increases, potentially generating excess pressure due to uneven compaction (Buntoro et al., 2022).

The unloading mechanism (see Figure 6b), on the other hand, contributes to overpressure through processes that transfer the load from grain-to-grain contact within the rock to the fluids occupying the pores (Buntoro et al, 2022). This can occur when pore fluids are introduced or when part of the solid matrix spontaneously transforms into fluid, increasing pore pressure—assuming the fluid cannot escape. In such cases, a portion of the stress previously supported by the rock framework is transferred to the pore fluid, resulting in elevated pore pressure due to load redistribution (Buntoro et al., 2022).

Figure 6. Overpressure mechanism a) Loading mechanism b) Unloading mechanism

b)

a)

4. Results and Discussion

4.1 Analysis of 1D Mechanical Earth Model

This study presents a one-dimensional geomechanical model analysis conducted on 26 wells within the Keutapang Formation of the 'ZA' Field, located in the North Sumatra Basin. The analysis encompasses pore pressure, elastic properties, rock strength, the injection pressure window, and injection pressure limits.

The pore pressure anomalies identified in this study are classified into three categories: underpressure, overpressure, and a combination of overpressure and underpressure. Each well exhibits distinct pore pressure behaviour at various depths, as summarized in Table 3.

Table 3. Depth of overpressure-underpressure in each well

Out of the 26 wells analyzed, three representative wells are selected to illustrate each type of pore pressure condition. Figure 7 presents an example of a one-dimensional Mechanical Earth Model (MEM) curve displaying an underpressure condition. Underpressure occurs when the pore pressure is lower than the hydrostatic pressure. As shown in Figure 7, the pore pressure (green curve) lies to the left of the hydrostatic pressure (red curve).

The underpressure mechanism observed in the study area is attributed to fluid extraction from the reservoir during past production activities. This condition commonly arises in depleted fields, where reduced fluid volume within the rock leads to a drop in pore pressure. As the amount of fluid in the pore spaces becomes insufficient to support the overburden pressure, underpressure conditions are developed.

Figure 8 presents a sample of one-dimensional Mechanical Earth Model (MEM) curves illustrating an overpressure condition. Overpressure occurs when the pore pressure exceeds the hydrostatic pressure. As shown in Figure 8, the pore pressure (green curve) is positioned to the right of the hydrostatic pressure (red curve).

The overpressure mechanism observed in the study area is attributed to a sedimentation rate that outpaces the rate at which fluids can escape from the rock pores, a process referred to as the loading mechanism. This typically occurs at relatively shallow formation depths. In such cases, fluids become trapped within the rock matrix rather than being expelled, resulting in a buildup of pore pressure and the development of overpressure.

Figure 9 presents a sample of one-dimensional Mechanical Earth Model (MEM) curves illustrating a combined overpressure–underpressure condition. Overpressure occurs when the pore pressure exceeds the hydrostatic pressure, while underpressure occurs when the pore pressure falls below the hydrostatic pressure. As shown in Figure 9, the pore pressure (green curve) lies both to the right and left of the hydrostatic pressure (red curve). The overpressure observed in the study area is attributed to a sedimentation rate that exceeds the rate of fluid expulsion from the rock pores, a process commonly referred to as the loading mechanism. In contrast, the underpressure condition is linked to fluid extraction from the reservoir during past production activities. The presence of both overpressure and underpressure within a single well may result from rapid sedimentation overlying an underpressured formation.

4.2 Analysis of Rock Elastic Properties and Rock Strengths

The wells in this field exhibit a relatively consistent range of elastic properties and rock strength. Young’s modulus values range from 0.5 to 2 GPa, while Poisson’s ratio is approximately 0.2. These values suggest that the rocks are relatively deformable and susceptible to fracturing.

In terms of rock strength, the wells display unconfined compressive strength (UCS) values in 42 MPa, tensile strength in 4 MPa, and friction angles ranging from 20° to 31°. Detailed rock strength values for each well are provided in Table 4.

Figure 7. 1D MEM of ZA-5 well

Figure 8. 1D MEM of ZA-1 well

Figure 9. 1D MEM of ZA-4 well

These data indicate that the formations are composed of weak sedimentary rocks with a shaly sand lithology. This lithology, characterized by fine grain size, tends to be more resistant to deformation if the rocks are well-compacted. However, in these wells, the rocks are not yet fully compacted, as evidenced by the relatively low friction angles. A low friction angle implies that the rock grains can be easily displaced under shear stress.

Table 4. Rock strength for each well

4.4 Water Injection Evaluation on Z-600 Layer

This study also includes an injection evaluation in the "ZA" Field, specifically targeting the Z-600 marker. The Z-600 marker refers to a stratigraphic horizon identified from well log responses, and its lateral continuity is limited to log-based correlation across the study wells. It is situated at a depth of approximately 600 meters, with a thickness ranging from 30 to 40 meters. Although the depth varies due to local geological structures, the correlation of this marker does not imply regional geological homogeneity. Wells marked with black circles indicate normal pressure, red circles indicate overpressure, and blue circles indicate underpressure.

Figure 10 shows that, among the production wells, only one well—ZA-1—experiences overpressure in the Z-600 layer. This overpressure condition is due to the continuous injection of fluid from injection well ZA-3, which causes the pore pressure of ZA-1 to exceed the hydrostatic pressure. This occurs because injection well ZA-3 not only supports production well ZA-1, but also injects fluid into wells ZA-10 and ZA-12 until their pressures return to normal.

Table 5. Injection window for each well

Figure 10. Pore pressure condition after water injection in Z-600 reservoir layer

Figure 10 also shows production wells that experience underpressure in the Z-600 layer, including wells ZA-24, ZA-23, and ZA-25. Well ZA-23 experiences underpressure despite its proximity to injection wells ZA-2 and ZA-4, which is attributed to its low permeability. The low permeability hinders the flow of injected fluid into the production well. Meanwhile, wells ZA-23 and ZA-25, despite having relatively high permeability, also experience underpressure. This is due to the distance between injection well ZA-22 and production well ZA-23, as well as the fact that injection well ZA-26 is located in a different structural contour than ZA-23. Additionally, well ZA-25 receives injection only from ZA-8, as an injection well ZA-9 has not yet been put into operation. It is assumed that injection well ZA-8 prioritizes injection into production wells within the same structural contour.

Based on this evaluation, it is concluded that the production wells in the "ZA" Field contribute to enhanced oil recovery, although three production wells are still experiencing under-pressure.

5. Conclusions

This study developed a one-dimensional Mechanical Earth Model (1D MEM) that includes elastic properties (Young’s modulus and Poisson’s ratio), rock strength parameters (UCS, tensile strength, and friction angle), overburden pressure, pore pressure, and minimum horizontal stress. Based on the 1D MEM for each well in the "ZA" Field, several pore pressure anomalies were identified, including over-pressure, under-pressure, and combined overpressure-under-pressure conditions. Underpressure is primarily attributed to fluid depletion from previous production activities, while overpressure results from loading mechanisms.

Each well in the field exhibits UCS values is 42 MPa, tensile strength is 4 MPa, and friction angles ranging from 20° to 31°. These values suggest that the rocks in this field are mechanically weak, likely due to their fine-grained texture. The wells are characterized by shaly sand lithology, which is associated with relatively low friction angles. This is likely due to the unconsolidated nature of the formation, making the rock grains more susceptible to shear under friction. This observation is further supported by reports of sand production events in the "ZA" Field. Additionally, the injection pressure window in each well is relatively wide, both in terms of overpressure (1–2 MPa) and underpressure (1.5–5 MPa). The maximum injection pressure value is currently available only for well ZA-7, recorded at approximately 1106 psi or 7.63 MPa. Therefore, step-rate tests are still required for the remaining wells to determine their actual maximum injection limits.

The results of the geomechanical analysis, particularly through the development of a Mechanical Earth Model (MEM), provide a strong technical basis for defining the safe upper limit of injection pressure. This information directly supports operational decision-making, including injection rate optimization, formation damage risk mitigation, and well planning—ultimately enhancing both safety and field operation efficiency.

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Author’s contribution

Zahra Adinda Billqish (B.Sc, Physics, expertise in geophysics, geomechanical analysis) performed the data processing, data analysis, interpretation of the results, and wrote the original draft and edited. Andika Wicaksono (M.T., Geology, seismic exploration, petroleum geology) provided the data, data collection, interpretation and supervision. Sismanto (Dr in Geophysics, expertise in seismic exploration and rock physics) performed the interpretation and supervision.

All authors have read and agreed to the published version of the manuscript.