Iraqi Journal of Chemical and Petroleum Engineering

Geological CO₂ sequestration is a widely accepted technique that consists of safely and securely hiding carbon emissions from human activities by storing them underground. It is considered the main method of carbon dioxide capturing and sequestration because of its large-scale application. Use of Downhole Water Sink (DWS) systems and other advanced reservoir control technologies is driven by the necessity to manage effectively the reservoir pressure change, the CO₂ plume migration, and the leakage pathways. The effectiveness of the DWS technology in providing better CO₂ storage performance in a depleted reservoir surrounded by a saline aquifer has been the focus of this research. The study analyzed three operational scenarios to evaluate how the different distances between the DWS wells and the CO₂ injectors would affect the storage efficiency, the leakage behavior, and the management of the reservoir pressure. Scenario 1 had DWS wells located right under the CO₂ injectors which caused negative hydrodynamic interactions, decreased the total trapped CO₂ and increased the leakage indices. Scenario 2 had a tiny lateral offset which led to small improvements in trapping and tiny increases in leakage, implying partial but still insufficient mitigation of injector-sink interference. In Scenario 3, with a 10-grid offset between the injection and extraction wells, achieved the maximum CO₂ storage efficiency, where trapping gained 2.79% (431,235 tons) at a CO₂ injection rate of 20 MMSCF/day and a DWS flow rate of 12,500 bbl/day. Leakage did not change, maintaining a Leakage Index of +0.06898 which indicated only a slight increase that was barely noticeable when compared to the more varying responses in Scenarios 1 and 2. In addition, the operation of DWS in Scenario 3 led to a peak pressure of the reservoir that was lower than that of the case without DWS, as the pressure dropped from about 6150 psi to 5100–5794 psi, thus relieving some of the stress on the formation. Although no specific fracture pressures or geomechanical analyses were performed, the pressure drop is likely to create better injection conditions and also enhance the integrity of the storage area. The overall results show that the DWS wells, when placed and managed properly and strategically, can not only increase the CO₂ trapping efficiency but also stabilize the leakage behavior and provide pressure control, thus making the geological CO₂ sequestration more reliable.

This work aims to study the kinetics of heterogeneous electro-Fenton (HEF) process used to treat petroleum refinery wastewater by a catalyst made of an activated carbon loaded with iron and cerium. Effects of HEF operating variables such as current density, dose of catalyst, and pH on the removal of COD and reaction rate constant (kapp) were examined. Results showed that decline of COD with time at different operating conditions obeys a pseudo-first-order kinetics with a regression fitting (R2) not less than 0.97. Furthermore, increasing the current density was found to give higher rate of COD removal and kapp to a limit beyond which no longer enhancement can be achieved. Similar behaviour regarding to the dose of catalyst while increasing pH has an adverse effect. The best operating conditions that give high rate of COD removal and kapp were a current density of 10 mA/cm2, a dose of catalyst equal to 1.0 g/L, and a pH of 3, in which 86.97% of COD was removed with kapp value equal to 0.0188538 min-1 which required only13.06 KWh/kg COD as an electrical energy consumption. The present system approved to have high reaction rate and could be applied to treat various types of wastewaters.

CO₂-based acidizing provides a corrosion-resistant alternative to potent mineral acids in carbonate reservoirs by producing carbonic acid in situ. This method synchronizes stimulation procedures with carbon-management objectives while minimizing the necessity for comprehensive corrosion-inhibitor systems. This review assesses six categories of chemical additives—amines, inorganic salts, inorganic bases, metal-oxide nanoparticles, biological macromolecules, and natural biopolymers—emphasizing their effects on CO₂ absorption, pH buffering, and wormhole morphology at reservoir-relevant temperatures and salinities. Experimental findings indicate that specific formulations can achieve CO₂ absorption levels reaching 2612 mg/L while maintaining pH levels between 4.5 and 5.2. Furthermore, computed CT imaging confirmed consistent wormhole development, indicating effective acid transfer and diminished corrosion risk. According to the reviewed literature, natural biopolymers and biological macromolecules provide the most advantageous equilibrium of reactivity and environmental compatibility; however, the heterogeneity of experimental data, diversity of reservoirs, and scalability are significant considerations. This review methodologically synthesizes peer-reviewed research and field reports published till October 2025, emphasizing mechanistic insights and identifying shortcomings in converting laboratory performance to field application. The proposed approach connects additive selection to carbonate reactivity, transport mechanisms, and operational limitations, thus informing the development of multifunctional fluids for sustainable stimulation and carbon-conscious reservoir management.

It is challenging to remove antibiotics from water bodies, as they are among the most widespread contaminants in the environment, particularly with conventional wastewater treatment methods, due to their persistence and low biodegradability. The current study examined the use of nanoscale zero-valent iron to remove antibiotics. Conocarpus leaf extract, which is primarily composed of polyphenols like flavonoids and tannins, was employed as a green source to prepare Fe/Cu nanoparticles, which can act as an eco-friendly reducing agent. The nanoparticles were immobilized onto silica sand (SS), forming SS-Fe/Cu nanocomposite to remove tetracycline (TC) and ciprofloxacin (CIP) from aqueous solutions. The structural characteristics of the produced nanocomposite were examined using a number of analytical techniques, including XRD, FTIR, FE-SEM, EDS, TEM, BET, TGA, and DSC. The structural and morphological analyses confirmed the successful synthesis of the SS–Fe/Cu nanocomposite, with well-dispersed nanoparticles (FE-SEM/TEM), characteristic functional groups (FTIR), crystalline structure (XRD), surface area enhancement (BET), elemental composition (EDS), and good thermal stability (TGA/DSC). Studies were conducted through batch experiments, and the influence of various variables (contact time, pH, agitation speed, nanocomposite dosage, and initial pollutant concentration (Co)) was investigated. The maximum removal efficiencies of 93% and 85% were achieved for TC and CIP, respectively, under ideal conditions: time = 90 min, pH of 11 for TC and 7 for CIP, agitation speed = 200 rpm, Co = 10 mg/L, and nanocomposite dosage of 1 g/50 mL. Isotherm, kinetic, and thermodynamic studies showed that adsorption of both TC and CIP followed the Freundlich model and pseudo-second-order kinetics, indicating chemisorption as the main mechanism. Thermodynamic results revealed that TC adsorption was endothermic and non-spontaneous (positive ΔS°), while CIP adsorption was exothermic and spontaneous (negative ΔS°), confirming the efficiency of the SS–Fe/Cu nanocomposite. The results revealed that the green-fabricated SS-Fe/Cu nanocomposite could be used as an efficient agent for extracting TC and CIP from aqueous solutions.

Carbon dioxide-enhanced oil recovery (CO₂-EOR) is one of the best commercial carbon capture, utilization, and storage (CCUS) technologies, enhancing oil recovery and achieving safe geological storage. However, data density and computational costs hinder conventional reservoir simulations. This research presents a multi-output machine learning model to simultaneously predict both incremental oil production and CO₂ storage volume. A selected global dataset of 173 projects was compiled, preprocessed, and optimized. Among the evaluated algorithms (random forest, gradient boosting, and random forest chain), gradient boosting achieved the best predictive performance, with a weighted coefficient of determination (R²) of 0.722. Model analysis showed that permeability, depth, and miscibility strongly influence oil recovery (R² for gradient boosting = 0.87), while the predictability of storage volume is low (R² for gradient e boosting = 0.5), which is significantly affected by reservoir depth and porosity. This study highlights the potential of machine learning as a rapid screening tool for evaluating carbon capture, utilization, and storage projects, identifying the performance of key factors, and emphasizing the need for standardized storage reporting to improve prediction accuracy.

The current study objective is to synthesize activated carbon (AC) from compressed wood using the ZnCl2 activating agent and to assess the ciprofloxacin (CIP) elimination efficiency in simulated wastewater. The produced AC was characterized using multiple techniques, including SEM, BET, FTIR, AFM, and XRD. The adsorbent demonstrates high adsorption performance, achieving 91% removal of CIP within 5 hours at an initial pollutant concentration of 100 mg/L with an AC dose of 2 g/L. Experimental data correspond to the Freundlich isotherm model (R² = 0.995) as well as the Langmuir competitive fitting (R² = 0.99), while the root mean square error (RMSE) equation best fits the Langmuir model. Moreover, the pseudo-second-order model (R² = 0.999) was used to describe the kinetic data. The adsorption thermodynamics indicate spontaneous adsorption with exothermic behavior (ΔG◦<0, ΔH◦<0, ΔS◦<0). A combination of mechanisms contributed to the CIP adsorption process (π-π interaction, hydrophobic interaction, bulk diffusion, hydrogen bonds, in addition to physical and chemical adsorption mechanisms). Pyrolysis recoverability shows a good result after three cycles (qe = 101.08 mg/g, compared to 170.13 mg/g in the first cycle). In conclusion, compressed wood AC offers a sustainable, low-cost adsorbent for treating wastewater and presents a prospect for addressing ecosystem contamination challenges.

The Sadi reservoir is one of the largest and most important unconventional tight oil reservoirs in southern Iraq. However, it suffers from low production rates, necessitating many development strategies that require a correct and reliable characterization of reservoir fluid properties. Whilst these properties are originally obtained from laboratory experiments, measurement errors often occur despite rigorous workflows, which negatively affect the calculation of reservoir fluid properties. This study utilizes the fluid thermodynamics characterization program (PVTp) to generate a reliable model for determining the oil properties of Sadi reservoir. A methodology was developed to simulate fluid thermodynamic tests, including Differential Liberation (DL) and Constant Composition Expansion (CCE) tests. This was achieved through adjusting the Peng-Robinson Equation of State (PR-EOS) by splitting and lumping the C7+ plus fractions and calibrating the Ω parameters to match model results with experimental data using regression. Using the absolute average error (AAE) as a model performance benchmark, the model achieved an AAE of 1% for estimating the relative volume, 0% for estimating oil density, 0.41% for estimating oil formation volume factor, 4% for oil viscosity, and 5% for the gas oil ratio. These values represent the highest AAE recorded for this dataset and show good agreements within the acceptable limits. The PR-EOS model proves to be an accurate fluid characterization alternative that can be applied to similar field data, providing a valuable tool for reservoir management, improving performance, and reducing costs in the future.

A scalable method of oxalate co-precipitation was used to prepare magnesium oxide (MgO) nanoparticles. The precipitant used was oxalic acid and the source of soluble magnesium was magnesium sulfate. Highly crystalline MgO nanoparticles prepared by calcining at 700℃ were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and Atomic Force Microcopy Analysis (AFM). The (MgO) nanoparticles were employed to investigate the adsorption performance of Methyl Violet (MV) dye from aqueous solutions under different conditions, i.e., contact time, pH, dye concentration and temperature. Maximum adsorption (96%) was obtained at time (60 min.), pH 6, at (20℃ ) and adsorbent dosage (0.05 g/L). It was found that the effect of temperature is inversely proportional to the percentage of dye removal in the studied range (20-50℃). Pseudo-second-order (R2=0.9968) and Langmuir models (R2=0.9931) described excellent fits in kinetic and isotherm experiments, respectively, indicating monolayer coverage and chemisorption. The greater adsorption capability of MgO was verified in comparison to equivalent adsorbents (qmax=83.1 mg/g).

The article investigated the effectiveness of removal (R%) of a batch study in the removal of lead (II) ions from synthetic aqueous water. A number of experiments were performed to determine the optimal parameters for the maximum removal procedure by using commercial activated carbon. The primary process variables studied included initial lead (II) ions concentration (pb (II)), pH, adsorbent particle size, dosage of adsorbent, and adsorption time.
The maximum removal occurred at an initial pb (II) ions concentration of 10 ppm, a pH of 5, an adsorbent dosage of 0.3 g with a particle size of 75 µm, and an adsorption time of 75 min. This research employed isotherm models to determine how the system reached a stable state. Langmuir model confirmed the largest accuracy, as evidenced by the results with a correlation coefficient (R²) of about 97.91%. The kinetic data was the most accurately described by pseudo-second-order model (PSO), suggesting that chemisorption is the limiting factor in the reaction rate, which was supported by a correlation coefficient of 99.5%. Four different adsorbent materials were investigated to evaluate their lead ions (II) removal %: commercial activated carbon (CAC), tea waste-derived biochar (TWDB), and CAC and TWDB modified with citric acid (CAC-CA and TWDB-CA). The study demonstrated that these four adsorbents were very effective and inexpensive agricultural waste may serve as an efficient method to remove pb (II) ions from contaminant water.

Accurate prediction of dew-point pressure (DPP) is essential for the development and production management of gas condensate reservoirs; however, existing empirical correlations and standalone machine learning models often suffer from limited generalization and sensitivity to data variability. This study addresses this gap by developing and comparing a nonlinear multiple regression (NLMR) correlation and a hybrid particle swarm optimized neural network (PSONN) model using a large and diverse dataset of 880 experimental samples collected from published literature and Middle East reservoirs. The PSONN model was selected due to its capability to overcome neural network limitations such as slow convergence by optimizing network weights through global swarm intelligence. The hybrid PSONN model achieved superior predictive accuracy with APRE of 2.45% and CC of 0.997, outperforming both the proposed NLMR correlation and previously published models. The results demonstrate that the developed hybrid framework can significantly improve dew-point pressure estimation, thereby supporting reliable reservoir characterization, surface facility design, and production planning in gas condensate fields.

Use of electrodes that provide a high surface area for reaction, such as Nickel foam and Carbon Fiber Felt, has proven highly efficient in treating wastewater. In this study, a mixture of dyes (Eosin Y, Methylene Blue, and Methylene Violet) was treated using Ni foam as a cathode and carbon fiber felt as an anode in the Electro-Fenton process, relying on iron waste, such as iron filings, as the catalyst source. The analysis characterization of electrodes and iron filings was determined by Energy dispersive X-Ray (EDX) and Scanning electron microscopy (SEM) tests. The results showed high efficiency in decomposing the dye mixture. The highest Re % 96.4591 which attained after accomplishing the experiments based on Response Surface Method (RSM) was recorded at 75 mA of current, 0.25 g/L of iron filings concentration, and 0.015 M of electrolyte concentration. The highest specific energy expenditure for this process was 6.892939 kWh/kg of dyes. In addition to the process's energy efficiency, using iron scrap as a cheap alternative to expensive iron salts is a cost-effective and environmentally friendly approach.