OPTIMIZATION OF ACIDIZING IN CARBONATE RESERVOIRS WITH EXPLICIT ACCOUNT OF NEAR-WELLBORE GEOLOGICAL STRUCTURE AND HYDRODYNAMIC CONDITIONS

ABSTRACT

This article addresses the optimization of acidizing processes in carbonate reservoirs, considering the geological structure of the near-wellbore zone and hydrodynamic conditions. The reaction kinetics of acid solutions, acid flow distribution, and penetration through the fracture network are mathematically modeled. Based on simulation results, recommendations for optimizing technological parameters (acid concentration, injection rate, and flow rate) are developed.

АННОТАЦИЯ

В данной статье рассматриваются вопросы оптимизации процесса кислотной обработки в карбонатных коллекторах с учётом геологического строения призабойной зоны пласта и гидродинамических условий. Математически моделируются кинетика реакции кислотных растворов, распределение потока кислоты и процесс её проникновения по системе трещин. На основе результатов моделирования разработаны рекомендации по оптимизации технологических параметров (концентрация кислоты, расход, скорость закачки).

Keywords: carbonate reservoir, near-wellbore zone, acidizing, optimization, hydrodynamics, mathematical modeling.

Ключевые слова: карбонатный коллектор, призабойная зона, кислотная обработка, оптимизация, гидродинамика, математическое моделирование.

Introduction. Carbonate reservoirs play a pivotal role in the oil and gas industry due to their high porosity and permeability and their large hydrocarbon storage capacity. However, during production, permeability near the wellbore can deteriorate because of fines migration, scale and precipitate formation, which increases hydrodynamic resistance in the near-wellbore zone and sharply reduces well productivity. Acidizing is among the most effective techniques to restore and enhance conductivity in carbonate rocks: the acid reacts with formation minerals, enlarging the pore and fracture system. The efficiency of acidizing depends directly on the reservoir’s geological architecture, poro-permeability characteristics, the development of the fracture system, the acid type, and the placement schedule. Consequently, optimizing technological parameters with explicit consideration of in-situ conditions can significantly improve outcomes. Using mathematical modeling, this study identifies optimum conditions for acidizing in carbonate reservoirs and compares the modeling outputs with field observations..

Literature Review. Carbonate reservoirs constitute a substantial share of global oil and gas reserves. Despite their favorable porosity and permeability, near-wellbore permeability may plummet due to colmatation, precipitation, and accumulation of organic/inorganic deposits [1–3]. Acidizing is therefore widely applied as an efficient remediation technology. In acidizing, carbonate minerals (primarily calcite and dolomite) react with injected acid to form soluble products. This process generates new flow channels or enlarges existing ones, thus increasing well deliverability [4]. Hydrochloric acid (HCl) and its blends are most commonly used; their concentration, injection rate, and overall volume exert a first-order control on treatment effectiveness [5]. A large body of work has sought to improve carbonate matrix acidizing. For example, Chacón O. G. et al. (2022) proposed optimization strategies via mathematical modeling of acid flow distribution in fractured carbonates [6]. Keihani Kamal M. et al. (2024) demonstrated, using field data, how geological anisotropy impacts acidizing outcomes [7]. Hatamizadeh A. et al. (2023) evaluated hybrid acid systems and data-driven simulation approaches that can reduce acid volumes, especially in highly mineralized brines [8]. Contemporary studies increasingly employ digital hydrodynamic modeling to forecast and optimize acidizing by integrating reservoir geometry, poro-permeability maps, and fracture attributes [9, 10].

While prior research has accumulated extensive experience on general optimization, integrated modeling that simultaneously captures specific geological heterogeneity and hydrodynamic conditions in the near-wellbore region remains incomplete. This work is aimed at addressing that gap.

Materials and Methods

Object of Study. The study considers a fractured and porous carbonate reservoir characterized as follows:

• Lithology: predominantly calcite (CaCO₃) and dolomite (CaMg(CO₃)₂)
• Porosity: 8–18%
• Permeability: 10–350 mD
• Reservoir pressure: 12–18 MPa
• Reservoir temperature: 45–70 °C

Based on geophysical logs (gamma ray, saturation logs, acoustic logs), porosity-permeability maps were constructed, and the fracture system was delineated and used as input to the model.

Physicochemical Model. The principal reaction during acidizing of carbonate minerals with HCl is:

                                                 (1)

The reaction rate is described by the Arrhenius-type expression:

                                                                         (2)

where:

r – reaction rate (mol/(m²·s)),

k₀ – re-exponential factor;

E^a – activation energy (J/mol),

R – universal gas constant (8.314 J/(mol·K)),

T – absolute temperature (K),

C – acid concentration (mol/m³),

n – reaction order.

Transport of acid through the formation is modeled by an advection–diffusion–reaction equation:

                                                               (3)

where:

 – the Darcy velocity (m/s),

D – the molecular/dispersion coefficient (m²/s),

r_s – the dissolution rate of the solid phase (mol/(m³·s)),

ϕ – the porosity.

Hydrodynamic Model. Flow is governed by Darcy’s law:

                                                                         (4)

bu yerda:

k – permeability (m²),

μ – acid viscosity (Pa·s),

p – pressure (Pa).

Numerical Scheme

The governing equations were solved using a finite-difference scheme with the following workflow:

1. Import reservoir geometry and poro-permeability fields.

2. Impose initial and boundary conditions::

  (initial acid concentration),

  (initial pressure).

3. At each time step (Δt) compute advection, diffusion/dispersion, and reaction terms.

4. Update porosity and permeability fields and output porosity-change maps and the updated permeability.

Computations were performed in MATLAB and CMG.

Results and Discussion

Modeling Setup

Baseline parameters for the simulations:

• Acid: 15% HCl solution
• Initial injection rate: 2.5 m³/min
• Porosity: 12%
• Initial permeability: 150 mD
• Reservoir pressure: 16 MPa
• Reservoir temperature: 55 °C

Acid Concentration Distribution

Figure 1 (conceptual) shows the temporal evolution of acid concentration across a radial section. The bulk of the acid reacts intensively within 0–3 m from the wellbore, rapidly depleting concentration and constraining deeper penetration.

Figure 1. Temporal evolution of acid concentration in the formation (model result).

Porosity and Permeability Alteration

Table 1 compares permeability before and after acidizing.

Table 1.

Permeability before and after acidizing

The maximum increase occurs within 0.5–1 m from the well. Gains at 2–3 m are modest, indicating factors that limit deeper acid penetration.

Effect of Injection Rate and Volume

Injection rates of 1.5 m³/min, 2.5 m³/min and 4.0 m³/min were evaluated.

Figure 2 indicates that too low rates do not carry acid deep enough before reaction, whereas excessively high rates reduce residence time and reaction efficiency. An optimal value of approximately 2.5 m³/min maximizes permeability enhancement.

Figure 2. Influence of injection rate on permeability gain (model result)

Role of Fracturing

Fracture intensity was varied from 0% to 15%. Increasing fracture density promotes deeper acid penetration; however, preferential flow along fractures can reduce dissolution near the wellbore. For fractured reservoirs, staged acidizing or the use of viscous (thickened/gelled) acids is recommended to balance penetration and near-wellbore etching.

Key findings:

• Acidizing performance is strongly governed by the reservoir’s geological structure.
• Proper selection of injection rate and acid concentration can increase effective permeability by 40–60%.
• For fractured systems, staged acidizing and/or viscous acids are advisable.
• Numerical results are consistent with field observations, supporting model reliability.

Conclusions

For carbonate reservoirs, optimizing acidizing requires in-depth analysis of geological structure and hydrodynamic conditions in the near-wellbore zone. Using mathematical modeling, the following scientific and practical outcomes were obtained:

1. Optimal parameters—15% HCl and an injection rate around 2.5 m³/min —were identified; under these conditions, permeability gains of 40–60% are achievable.

2. Geological controls—porosity, fracture intensity, and mineralogy—exert significant influence on treatment response.

3. Recommendations for fractured reservoirs—staged acidizing and viscous acids improve performance.

4. Model credibility—agreement between simulations and field results indicates suitability for engineering application.

These findings can serve as a practical guide for engineers planning and optimizing acidizing treatments.

References:

1. Ermatov, N. Kh., Samatov, Sh. Sh., & Jorayeva, G. Ch. (2025). Karbonat kollektorlarida kislotali ishlov berish jarayonining geologik xususiyatlarni hisobga olgan holda modellashtirilishi [Modeling of acidizing in carbonate reservoirs with account of geological features]. In: Raqamli sanoat va avtomatlashtirish: texnologik jarayonlar va ishlab chiqarishni boshqarish tizimlarida innovatsion yechimlar [Digital Industry and Automation: Innovative Solutions in Process Engineering and Production Control Systems], Republican Scientific-Practical Conference, Karshi, Uzbekistan, 18–19 April 2025. – PP. 165–170. [in Uzbek]
2. Garrouch, A. A. (2017). A contemporary approach to carbonate matrix acidizing. Journal of Petroleum Science and Engineering, 158. – PP. 623–640. [Electronic resource] URL: https://doi.org/10.1016/j.petrol.2017.08.056
3. Samatov, Sh. Sh., Ermatov, N. Kh., & Hayitov, L. K. (2025). Karbonat kollektorlarida kislotali ishlov berishni modellashtirish va samaradorligini oshirish yo‘llari [Modeling acidizing and approaches to improve its efficiency in carbonate reservoirs]. In: Geologiya fanlari, innovatsion rivojlanish, mutaxassislar tayyorlashning dolzarb muammolari va istiqbollari [Geological Sciences, Innovative Development, and Training of Specialists: Current Problems and Prospects], International Scientific-Practical Conference, Tashkent, Uzbekistan, 7 May 2025. - PP. 272–274. [in Uzbek]
4. Sahu, Q., Alawani, N., Karpyn, Z. T., et al. (2022). Optimization and uncertainty quantification method for matrix acidizing in carbonate reservoirs. ACS Omega, 7(3). – PP. 2583–2598. [Electronic resource] URL: https://doi.org/10.1021/acsomega.2c05564
5. Samatov, Sh. Sh. (2025). Bir jinsli bo‘lmagan geologik sharoitda neft oqimini modellashtirish: karbonat qatlamlar misolida [Modeling oil flow under heterogeneous geological conditions: The case of carbonate formations]. In: Uglevodorod qazib chiqarishni ko‘paytirish, ularni qayta ishlash samaradorligini oshirish bo‘yicha innovatsion texnologik yechimlar va neft-gaz sohasi uchun kadrlar tayyorlashda klasterlarning roli [Innovative Technological Solutions for Enhanced Hydrocarbon Production, Increased Refining Efficiency, and the Role of Clusters in Training Oil-and-Gas Personnel], International Scientific-Technical Conference, Tashkent, Uzbekistan, 14 May 2025. – PP. 366–371. [in Uzbek]
6. Chacón, O. G., Cardona, J. C., Mendoza, A. D., et al. (2022). Matrix acidizing in carbonate formations: A review. Processes, 10(1), 174. [Electronic resource] URL:  https://doi.org/10.3390/pr10010174
7. Keihani Kamal, M., Moradi, B., et al. (2024). A comprehensive analysis of carbonate matrix acidizing in gas wells: A three-stage approach using HCl and VDA. Scientific Reports, 14, 29449. [Electronic resource] URL: https://doi.org/10.1038/s41598-024-52104-5
8. Hatamizadeh, A., Arabloo, M., et al. (2023). Simulation of carbonate reservoirs acidizing using machine learning and meta-learning. Energy Reports, 9. – PP. 10646–10665. [Electronic resource] URL:  https://doi.org/10.1016/j.egyr.2023.11.160
9. Kalfayan, L. (2008). Production Enhancement with Acid Stimulation (2^nd ed.). Tulsa, OK: PennWell. – PP. 252.
10. Economides, M. J., & Nolte, K. G. (2000). Reservoir Stimulation (3^rd ed.). New York, NY: John Wiley & Sons. – PP. 856.