Search

Abstract Effective water management in oil reservoirs is crucial for maximizing hydrocarbon recovery while minimizing environmental degradation. This paper investigates the potential of innovative chemical techniques to control water production in oil reservoirs and compares these methods with traditional mechanical strategies. By reviewing over 70 case studies extensively, this research provides a detailed evaluation of different approaches to managing water cut. The study reveals that chemical methods, particularly those utilizing advanced polymer-based gels, are generally more effective than mechanical techniques. These methods are especially advantageous in settings with high water cuts and geologically complex reservoirs. Chemical treatments provide greater adaptability and cost-efficiency, significantly reducing the environmental impact compared to mechanical approaches. The primary aim of this research is to analyze the sources of water cut and evaluate common water shutoff operations to enhance reservoir management based on specific challenges, reservoir characteristics, and economic considerations. Our findings suggest using a two-step strategy: starting with mechanical control methods and then applying chemical treatments specifically designed for the reservoir’s unique physical properties. This not only improves oil recovery rates but also enhances economic efficiency by extending the reservoirs’ lifespan. Future research should focus on developing cost-effective, environmentally friendly chemical solutions suitable for various geological settings. Such advancements could significantly refine water management practices in oil fields, leading to better economic and environmental outcomes.

Semiconductor is the main part of the photoanode in dye-sensitized solar cells (DSSCs). In this study, an evaluation was conducted on two different morphological shapes of TiO2/ZnO semiconductors, namely nanoparticles (NPs) and nanofibers (NFs), to enhance the efficiency of DSSCs. The nanofibrous semiconductors were fabricated using the electrospinning technique. Furthermore, a natural sensitizer, anthocyanin dye, was used to promote the environmental friendliness of the fabricated cells. These cells were then compared to those that used a synthetic dye known as N719 to determine their efficiencies. The UV–vis results confirmed the extraction of anthocyanin from black plum fruits (Syzygium cumini). The structural characteristics of the composites were examined using XRD, FE-SEM, and BET experiments. The XRD results revealed that the anatase phase of TiO2 was dominant in the crystal structure of the semiconductors, while the crystallite sizes of the composites were 25.04 nm and 12.24 nm in NPs and NFs forms, respectively. The FE-SEM analysis revealed the formation of quasi-spherical NPs with an average diameter of 48.26 ± 17.75 nm and NFs with an average diameter of 153.99 ± 26.18 nm. The BET results showed that the surface characteristics of the nanocomposite were enhanced by changing the shape from NPs to NFs. Photovoltaic studies showed that utilizing NFs semiconductors in the photoanodes of DSSCs resulted in increased conversion efficiency of light to electricity. The results of the sun simulator studies confirmed that the use of natural dye led to a decrease in the effectiveness of the DSSCs. This phenomenon can be attributed to the weaker binding of the natural dye to the photoanodes. Furthermore, the EIS investigations illustrated that the electron lifetime in the natural dye is more than twice that of N719, leading to a reduced rate of electron recombination in the Syzygium cumini extract. The TiO2/ZnO NFs photoanodes exhibited superior performance compared to their nanoparticle counterparts in DSSCs. Their high surface area promoted superior dye adsorption and light absorption. Moreover, the interconnected nanofiber network facilitated efficient electron transport, minimizing recombination losses. Increasing the binding strength of the chosen natural dye to the photoanode will also make the cells more environmentally friendly and improve their ability to produce electricity.

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR.

In the last two decades, researchers have attempted to use environmentally friendly pigments and porous nanostructures as sensitizers and semiconductors in dye-sensitized solar cells (DSSCs), respectively. In this study, the pigments were extracted from black plums (Syzygium cumini) to increase the biocompatibility of the DSSCs. The UV–vis spectroscopy of the extracted solution revealed the existence of anthocyanin pigments. The cyclic voltammetry measurement showed that the pigment energy levels were suitable for electron transfer to the TiO2 semiconductor, and the pigment electrochemical properties were also comparable with the commercial synthetic pigments. The dynamic light scattering experiment and zeta potential measurement illustrated that the size and electrostatic potential of the pigments were 0.3 nm and ±5 mV, respectively. These data confirmed the potential of the pigments for the diffusion into the porous structure of the TiO2 semiconductor and attraction as a shell layer on its surfaces. Besides, the TiO2 pastes were produced with seven different methods and characterized using XRD, SEM, and BET experiments. The results showed that the morphology, particle size, porosity, and surface area of the produced nanostructures were different in each method. Finally, the pastes were coated on the glass, sensitized with the extracted natural pigments, and used in the fabricated DSSCs. The efficiency of the cells was evaluated in the range of 0.027%–0.256% using the sun simulator technique. The results showed that the optimum particle size of the TiO2 semiconductor was around 25.6 nm which had been formed in the sixth method of the paste preparation.

Biological treatment, due to the formation of hazardous chemicals to remove organic compounds such as dimethyl sulfoxide (DMSO) and N, N-dimethylacetamide (DMAC), has limited potential. Advanced oxidation processes (AOPs) are regarded as a viable alternative for treating molecules containing carbon-hydrogen bonds that cannot be broken down by traditional physico-chemical methods. In this investigation, various AOPs such as Photo-Fenton, Electro-Fenton, and Photo-Electro-Fenton processes were studied to treat wastewaters containing DMSO and DMAC. The effects of the operating parameters, including various initial concentrations of DMSO and DMAC, initial pH, reaction time, different concentrations of Fenton’s reagent, power of UV lamp, different concentrations of electrolytes, the distance between electrodes and current intensity, were investigated. The findings of the experiments revealed that a pH of 3 and a reaction time of 120 min were optimal. At 2000 mg L−1 of DMSO, maximum degradation and the final concentration of TOC were 98.64 % and 256.8 mg L−1, respectively, by the Electro-Fenton process under the optimal conditions. The Electro-Fenton process was successful in determining the maximum degradation of DMAC (96.31 %) and the final TOC concentration (10.03 mg L−1) at 250 mg L−1 of DMAC under optimal conditions. Finally, it can be concluded that the Electro-Fenton process was the best process for the efficient removal of DMSO and DMAC. The second step of the kinetic model follows a pseudo-first-order reaction for 250 and 500 mg L−1 of pollutants and obeyed a pseudo-second-order kinetic model for concentrations of 1000, 2000 mg L−1.

The present study reports on a green method for the fabrication of wound dressing loaded with Pistacia atlantica extract. The oil was extracted from P. atlantica plant using a solvent extraction process. The UV–vis spectroscopy showed that the phenolic compounds existed in the P. atlantica oil (PAO). The result of the antioxidant activity with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay illustrated an IC50 value of 7.26 mg/ml, which indicated that the PAO was a significant source of natural antioxidants. Then, the PAO-loaded polyvinyl alcohol (PVA)-sodium alginate (SA) nanofibres were successfully produced using the electrospinning process. The results of field emission scanning electron microscopy showed that the addition of the PAO to the polymer solution increased the diameter of the fabricated nanofibres, while the optimum blending concentration of the PAO for obtaining the maximum scaffold stiffness was almost 1.5% (w/v). The physical properties of the fabricated nanofibres were evaluated using Fourier transform infrared spectroscopy, X-ray diffraction technique, and thermogravimetric-derivative and thermogravimetric analysis. The results showed that the PAO was bonded to the structure of the PVA/SA nanofibres, and the crystallinity, as well as the thermal stability of the fabricated nanofibres increased due to this attachment. In addition, the experiments showed that the PAO concentration in the structure of the fabricated nanofibres played a major role in the drug release rate, and increasing the PAO concentration from the optimum value decreased the controlled release of the drug. Last, based on the two-film theory, a mass transfer model was developed to predict the PAO released from the fabricated wound dressing.

One of the most advantageous methods for lowering water hardness is the use of low-cost adsorbents. In this research, the effectiveness of natural zeolite (clinoptilolite type), activated carbon, and activated alumina was evaluated. These adsorbents were sequentially modified by NaCl, HCl, and NaCl-HCL to improve their ability to adsorb. The contact time and the amount of adsorbent used in the adsorption process were investigated experimentally to determine their effects. The results indicated that the best contact time for hardness reduction was 90 min, and the best concentrations of adsorbents in drinking water for zeolite, activated carbon, and activated alumina were 40, 60, and 60 g/L, respectively. In addition, for groundwater, these figures were 60, 40, and 40 g/L, respectively. The greatest possible decreases in total hardness under the best conditions by natural zeolite, activated carbon, and activated alumina adsorbents were 93.07%, 30.76%, and 56.92%, respectively, for drinking water and 59.23%, 15.67 %, and 39.72% for groundwater. According to the results obtained from experiments, NaCl-modified zeolite, natural zeolite, and NaCl-HCl-modified activated carbon performed better in terms of parameter reduction. The equilibrium data were well fitted by the Langmuir isotherm model, whereas the kinetic data for the adsorption process were consistent with the pseudo-second-order model. The equilibrium study of the adsorption process by the Morris–Weber model revealed that both chemical and physical adsorption are involved.

Abstract Compositional gradient can be described as changes in the composition of components both vertically and horizontally in a hydrocarbon reservoir. In the present work, two-dimensional compositional gradient in multi-component gas and oil mixtures is modeled. A thermodynamic model is developed based on molecular diffusion coefficients in mass diffusive flux. A combination of Sigmund and Bird correlations is considered to estimate molecular diffusion coefficients for gas mixtures. Implementing this comprehensive developed model on a gas condensate sample shows interesting differences not only in trends of component compositions but also in gradient magnitude. A real set of data from a supergiant gas condensate field is used to validate the model. It is perceived that the developed model reduces relative absolute errors to about 50%. In the next step, a comprehensive study was conducted to understand the cross effects of molecular diffusion and natural convection in gas condensate and volatile oil samples. Gas and oil samples are selected to investigate if natural convection has the same effects in different samples. It is observed that increase in natural convection causes to reduce horizontal and vertical composition gradients. This effect is more significant in gas reservoir, as methane composition varies by more than 5.2 mol% diagonally in gas condensate sample, whereas this value is about 0.45 mol% in volatile oil sample. Lower density and higher bulk velocity in gas sample cause more disturbances in flow streams of gas mixture. Evaluation of the developed model shows that the model is reliable for reservoir studies and management programs.

Abstract Wax deposition inside pipeline and process equipment is a major problem in oil industry. In this study, a multi-solid thermodynamic model was developed to predict wax disappearance temperature (WDT). Paraffinic–naphthenic–aromatic (PNA) analysis was performed and two correlations were introduced for fusion properties of these species. In addition, WDT of 12 Iranian oil and condensate samples were measured using viscometry and differential scanning calorimetry (DSC) techniques. Experimental data of multi-component and ternary systems were utilized for validation of the model. It was observed that measured WDT by viscometry method is higher compared to DSC. Statistics analysis shows that DSC technique has lower average absolute relative error (AARE) and standard uncertainty compared to viscometry. Results show that the AARE of the model for ternary systems is 0.52% which is much lower among the previous developed thermodynamic models. In addition, AARE of the new model for 68 data was calculated about 0.23%, and R-square of model prediction was calculated about 0.97. The cumulative distribution function also indicates that P50 values are almost the same for model and experimental data. These results show that the model has a good accuracy. In addition, the accuracy of model increases as the average carbon number of oil mixtures increases. Finally, it was found that PNA analysis and distribution of each component in its sub-fractions have a considerable effect on the model accuracy.

Abstract In this study, a novel and integrated strategy is proposed to investigate the problem of low production rate of gas well in a supergiant gas condensate reservoir. In this strategy, the nodal analysis approach is applied for production optimization and performance assessment of a real inclined well. A multi-layered gas condensate reservoir model was constructed and simulated using actual reservoir rock and fluid properties. Effects of reservoir rock and fluid model simplification on inflow performance relationship (IPR) curves were investigated. Also, five different tubing pressure drop models were evaluated using extracted pseudo spontaneous potential (PSP) data from reservoir model to select the most accurate one for computing tubing performance relationship (TPR) data. Then, accuracy of nodal analysis in prediction of well operating point was investigated through comparing with reservoir simulator results. Results of nodal analysis for this well indicated that a significant discrepancy exists between calculated and actual production rate. Sensitivity analysis on uncertainty parameters, skin factor and drainage radius, shows that skin factor of the investigated well varies between 11 and 12.9 for drainage radius in the range of 3000–20000 ft. Therefore, the problem of low well production rate was attributed to high skin factor as a result of formation damage. Also, results demonstrated that reduction of skin can lead to maximum 73% enhancement in daily volumetric gas production rate of well.

The use of dimethyl sulfoxide (DMSO) as a solvent for organic and inorganic compounds in various industries such as pharmaceutical, electronic and acrylic fiber manufacturing process has been increased in recent years. According to the growth of these industries in recent decades, treatment of DMSO wastewaters has become a serious concern. Although DMSO itself has low toxicity, the biological treatment of wastewater containing DMSO, is known to be difficult. According to limitations of biological treatment due to production of toxic compounds, advanced oxidation processes (AOPs) is considered as a most efficient method and the best one for purifying organic compounds which are resistant to conventional physical and chemical processes. In this study, some important AOPs such as extended and hybrid Fenton processes (photo- Fenton, electro- Fenton and photo-electro Fenton), ozone-based processes (O3, O3/H2O2, O3/UV), UV/H2O2 and TiO2/UV have been studied in the treatment of wastewaters containing DMSO. The efficiencies of these processes were also evaluated and discussed. Overall, it can be concluded that hybrid Fenton processes and ozone-based processes are more effective for removal of DMSO from wastewater.

Gas condensate reservoirs are characterized by a distinctive retrograde behavior and potential for condensate drop out during production and sampling. Efficient modeling of gas condensate reservoir requires careful phase behavior studies of samples collected prior to and during the production life of reservoir. In this work, an integrated characterization and tuning algorithm is proposed to analyze the pressure-volume-temperature (PVT) behavior of gas condensate samples. Each characterization and tuning scenario is described by a “path” which specifies the class of fluid, splitting and lumping (if any), the type of correlation, and grouping strategy (static or dynamic). Different characterization approaches were tested for the effective description of heavy end. Meanwhile, dynamic and static strategies were implemented to tune the equation of state (EOS) through non-linear regression. The optimum combination of characterization and tuning approach was explored for each sample by a rigorous analysis of the results. It was found out that the exponential distribution function gives the best performance for heavy end characterization in a dynamic tuning strategy. Also, analyses indicate that using higher single carbon number may not necessarily make EOS tuning more accurate. In addition, the optimum step is reached in either the third or fourth step for most cases in a dynamic tuning approach, and is sensitive neither to the characterization path nor to the selected end carbon number.

Gas-condensate reservoirs contain hydrocarbon fluids with characteristics between oil and gas reservoirs and a high gas-liquid ratio. Due to the large gas-liquid ratio, wellhead choke calculations using the empirical equations such as Gilbert may contain considerable error. In this study, using drill stem test (DST) data of a gas-condensate reservoir, coefficients of Gilbert equation was modified; 26.7% of DST data has uncertainty. In these data, due to a problem of flow transmitter, the water flow rate is recorded equal to zero. This makes the mean absolute error of 5% in the measuring of total liquid phase flow rate. Because of uncertainty in the water flow rate in some DST data, the coefficients were optimized for two sets of data to investigate the effect of water flow rate on the calculations. The first dataset was the complete set of DST data, and, in the second, data were filtered with the elimination of uncertain data. The regression results showed that the whole data have a mean absolute error of 5.1%. For this regression, the uncertain data had a mean absolute error of 8.6%, while the error of the remaining data was 3.9%. In this case, for 38% of uncertain data, the mean absolute error was more than 10% indicating that these data are the major factor of the error. Mean absolute error for the filtered dataset was 3.0%. Error reduction was due to the elimination of data with uncertainty. In this case, 3% of the total data had a mean absolute error of more than 10%. In other words, 5% error of the liquid phase flow measurement that includes 26.7% of data caused an increase of 2.1% in the error of calculations. This showed that the elimination of uncertain data causes a remarkable reduction in error. To study the effect of temperature on choke calculations, wellhead temperature was considered as a variable in the Gilbert equation form. The regression results showed that the mean absolute error of 3.0% does not change, and the wellhead temperature has no considerable effect on the choke calculation accuracy.

The significance of gas compressibility factor in petroleum engineering encourages the researchers to employ the most accurate and precise methods for estimation of this factor. Commonly, empirical correlations due to their simplicity have been referred more than other approaches for prediction of Z-factor. There is no clear and reliable report to address an appropriate combination of correlation and mixing rule for each type of gas. In the present study, combination of several empirical correlations and mixing rules is examined and a decision tree is constructed to suggest best combination for each gas system. For this reason, 2329 experimental data were used for analysis. According to the results, Leland–Mueller mixing rule/Sanjari and Lay correlation is the best combination for sour and natural gas. Also, Van Ness–Abbot mixing rule/Hall–Yarborough correlation, Stewart–Burkhardt–Voo mixing rule/Heidarian correlation and Satter–Campbell mixing rule/Papay correlation are the most appropriate combination for gas condensate, binary and ternary mixtures respectively. For binary mixtures, a robust and novel empirical correlation was developed based on Kay mixing rule to estimate Z-factor. The results employed how good the new correlation is in agreement with the experimental data with significant R-squared 0.9843.

Production data analysis techniques have been widely used for estimating reservoir properties such as gas in place and average pressure. Knowledge of this information is crucial for proper reservoir management. The present study discusses the roadmap, challenges and uncertainties for analyzing production data in an Iranian gas-condensate offshore field. This work is an integrated study involving the quality check of production data, platform process simulation, orifice simulation, modeling choke performance, well modeling, and Rate Transient Analysis (RTA). The study is an inverse analysis which starts from production platform and continues down to the reservoir. To perform data analysis, we propose five general steps which are: Data gathering/extraction/quality check, well rate determination, well bottom hole pressure estimation, layer rate allocation, and reservoir property estimation. In this study, these steps are discussed elaborately. Furthermore, challenges of each step are presented and discussed. In addition, the required input and also missing data for each step is mentioned. Also, to cope with lack and/or uncertainties of data, feasible solutions are proposed for the current field situation as well as future developments. This paper can help petroleum engineers to know where to start and how to proceed to get to the final step of the analysis, i.e. estimating field gas in place. It also provides insights into challenges and uncertainties of the production analysis in gas-condensate

The gas compressibility factor, also known as Z-factor, plays the determinative role for obtaining thermodynamic properties of gas reservoir. Typically, empirical correlations have been applied to determine this important property. However, weak performance and some limitations of these correlations have persuaded the researchers to use intelligent models instead. In this work, prediction of Z-factor is aimed using different popular intelligent models in order to find the accurate one. The developed intelligent models are including Artificial Neural Network (ANN), Fuzzy Interface System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS). Also optimization of equation of state (EOS) by Genetic Algorithm (GA) is done as well. The validity of developed intelligent models was tested using 1038 series of published data points in literature. It was observed that the accuracy of intelligent predicting models for Z-factor is significantly better than conventional empirical models. Also, results showed the improvement of optimized EOS predictions when coupled with GA optimization. Moreover, of the three intelligent models, ANN model outperforms other models considering all data and 263 field data points of an Iranian offshore gas condensate with R2 of 0.9999, while the R2 for best empirical correlation was about 0.8334.