Iranian Journal of Oil and Gas Science and Technology

Abstract Control of well drilling operations poses a challenging issue to be tackled. The loss of well control could lead to the occurrence of blowout as a severe threat, involving the risk of human lives and environmental and economic consequences. Conventional proportional-integral (PI) controller is a common practice in the well control operation. The small existing margin between pore pressure and fracture gradients jeopardizes the efficiency of this conventional method to exercise an accurate and precise pressure control. There is a significant incentive to develop more efficient control methodologies to precisely control the annular pressure profile throughout the well bore to ascertain the down-hole pressure environment limits. Adaptive control presents an attractive candidate approach to achieving these demanding goals through adjusting itself to the changes during well drilling operations. The current paper presents a set of adaptive control paradigms in the form of self-tuning control (STC). The developed STC’s are comparatively evaluated on a simulated well drilling benchmark case study for both regulatory and servo-tracking control objectives. The different sets of test scenarios are conducted to represent the superior performance of the developed STC methods compared to the conventional PI control approach.

Abstract The most costly operation in the oil exploration is the well network drilling. One of the most effective ways to reduce the cost of drilling networks is decreasing the number of the required wells by selecting the optimum situation of the rig placement. In this paper, Balas algorithm is used as a model for optimizing the cost function in oil well network, where the vertical and directional drilling is performed. The model can determine optimal well placement as well as optimal paths to develop the field. The proposed model is implemented in an Iran southern gas field with five drilling rigs used to drill 44 wells from 14 positions on the surface. The results show a 17.4% reduction compared to the proposed cost.

Abstract Nanotechnology has various applications in oil and gas industry such as enhanced oil recovery (EOR). The main challenge in using nanoparticles in EOR processes is their stability in harsh conditions such as high temperature, high pressure, and intermediate to high salinity. However, most of the recent experimental works have been performed under unrealistic conditions such as the use of distilled water as the injected fluid and room temperature. The main objective of this work is to study the effect of these factors on the stability of nanoparticle dispersions through several methods such as direct observation, optical absorption measurement, and nanoparticle effective diameter in different periods of time. The critical salt concentration (CSC) was determined for two kinds of monovalent electrolytes in various particle concentrations and temperatures. The results have shown that CSC for potassium chloride (KCl) is less than sodium chloride (NaCl) and it decreases as nanoparticle concentration and temperature increase. Moreover, the influence of two types of surfactants on the stability of silica dispersions was studied and the results revealed that an anionic surfactant increases the CSC, while a nonionic surfactant leads to the instability of dispersion even at low electrolyte concentrations.

Abstract Wax deposition phenomenon changes the rheological behavior of waxy crude oil completely. In the current work, the rheological time-dependent and time-independent behaviors of waxy crude oil samples are studied and flow curve and compliance function are measured for the oil samples with various wax contents at different temperatures. A decrease in temperature and an increase in wax content lead to an increase in the viscosity and yield stress but a significant drop in compliance function. A modified Burger model is developed to predict the behavior of the compliance function and a modified Casson model is used to predict the flow curve of the waxy crude oil samples within a vast range of wax contents and temperatures. The proposed Burger and Casson models match with experimental results with R² of 99.7% and 97.33% respectively.

Abstract Nanotechnology has the potential to introduce revolutionary changes to several areas of oil and gas industry such as exploration, production, enhanced oil recovery, and refining. In this paper, the effect of different concentrations of Fe2O3 nanoparticles as a catalyst on the heavy oil viscosity at various temperatures is studied. Furthermore, the effect of a mixture of Fe2O3 nanoparticles and steam injection on heavy oil recovery is studied in laboratory. The experimental tests show that some of these nanoparticles decrease the heavy oil viscosity less than 50% at certain concentrations at different temperatures. The reason for this viscosity reduction is that, similar to a catalyst, these nanoparticles activate some reactions. Our results of steam injection tests show that the injection of a Fe2O3 nanoparticle mixture increases heavy oil recovery because of cracking reactions which crack the C-S, C=C, and C≡C bonds of the heavy components of heavy oil and change them to light components.

Abstract The improvement in the temporal resolution of seismic data is a critical issue in hydrocarbon exploration. It is important for obtaining more detailed structural and stratigraphic information. Many methods have been introduced to improve the vertical resolution of reflection seismic data. Each method has advantages and disadvantages which are due to the assumptions and theories governing their issues. In this paper, we improve the temporal resolution of reflection seismic data using the logarithmic time-frequency transform method. This method has minimum user-defined parameters. The algorithm uses valuable properties of both the time-frequency transform and the cepstrum to extend the frequency band at each translation of the spectral decomposing window. In this method, the displacement of amplitude spectrum by its logarithm is the basic idea of the algorithm. We tested the mentioned algorithm on both synthetic and real data. The results of the both tests show that the introduced method can increase the temporal resolution of seismic data.

Abstract Intelligent well technology has provided facility for real time production control through use of subsurface instrumentation. Early detection of water production allows for a prompt remedial action. Effective water control requires the appropriate performance of individual devices in wells on maintaining the equilibrium between water and oil production over the entire field life. However, there is still an incomplete understanding of using intelligent well concept to control unwanted fluids and the way this leads to improving hydrocarbon recovery. The present study proposes using intelligent well technology to develop a new integrated methodology for selecting/ranking the candidate wells/fields, interval control valve (ICV) size determination, and ICV setting optimization. Various technical and economical parameters weighted by expert opinions are used for candidate well/field ranking to implement the intelligent technology. A workflow is proposed for ICV size determination based on its effect on a predefined objective function. Inappropriate ICV size selection leads to suboptimum production scenarios. Furthermore, this study proposes an efficient ICV setting optimization in an intelligent well. The objective function can maximize cumulative oil, minimize water production, or conduct both. It was shown that for selecting the optimized cases, the balance between water and oil production under predefined criteria should be practiced. Real case studies were considered to demonstrate the effectiveness and robustness of the proposed methodology. A considerable improvement in the objective function was achieved using the developed methodology.

Abstract In this paper, an advanced analysis of a novel hybrid compression-absorption refrigeration system (HCARS) for natural gas dew point control unit in a gas refinery is presented. This unit separates the heavy hydrocarbon molecules in the natural gas, which is traditionally carried out by natural gas cooling in a compression refrigeration cycle (CRS). The power input required for the refrigeration cycle compressors is usually provided by gas turbines. The low efficiency of gas turbines and the excessive power required for running the CRS compressors have made it crucial to investigate different means to decrease the energy consumption of this cooling system.
The waste heat of gas turbines flue gas can be recovered and utilized as the heating source for running an absorption refrigeration system (ARS) to provide part of the needed cooling load; hence, a hybrid compression absorption refrigeration system (HCARS) is launched. In this work, the application of HCARS is extended to the Fajr-e-Jam gas refinery currently operating with a CRS, and an advanced exergetic analysis of the proposed ARS is performed to further improve the proposed system. The effect of different variables on the performance of the proposed HCARS is also inspected. The proposed system and these analyses are novel for the gas refinery dew point control unit. Real CRS operational data are utilized in all the investigations, and proper means are presented for the validation of the simulation results.
The proposed system resulted in 63% additional cooling capacity of the HCARS (12550 KW) in comparison to the current CRS (7670 kW) for the equal natural gas consumption, which overall saves about 50000 SCMD of natural gas. Based on the exergy analysis of all the equipment, the exergy efficiency of the proposed ARS is 0.155. In addition, the parametric study of the effects of the gas turbine flue gas exit temperature and flow rate, ambient temperature, partial load operation of CRS, absorption solution flow rate, and concentration on the HCARS performance is carried out. These studies should provide the information needed for operating the proposed system in different situations.

Abstract Sulfate scale deposition (BaSO₄, CaSO₄, and SrSO₄) is a common problem in oilfield operations around the world, which causes significant formation damage during production and injection activities. This paper presents the results of an experimental study on the permeability reduction of porous media due to sulfate scale deposition. A set of experiments were conducted to investigate the effects of cation (Ba²⁺, Ca²⁺, and Sr²⁺ ions) concentration and the number of cation species on the permeability reduction resulting from single sulfate scales (single BaSO₄, CaSO₄, and SrSO₄ scales) and mixed BaSO₄, CaSO₄, and SrSO₄ scale deposition in porous media during water injection. The experiments were performed at a constant temperature of 70 °C and a constant anion (SO₄^2- ion) concentration of 3968 ppm in the pack of glass beads as the porous media. The results show that the intensity of permeability reduction increases with increasing cation concentration. These results also declare that the permeability reduction of porous media due to mixed BaSO₄, CaSO₄, and SrSO₄ is clearly severer than single scales.

Abstract Several techniques have been used for sand production control in sandstone reservoirs.The main objective of this research is to present a suitable resin to be used as a consolidation agent in oil reservoirs. To achieve this purpose, urea-formaldehyde resin, phenol-formaldehyde resin, and modified urea-formaldehyde resin were selected to be studied. Core samples were made by the sand sample provided from the oil fields of southern parts of Iran with an average absolute permeability of 500-600 mD and an average porosity of 15-20% combined with various percentages of each resin. The core samples are tested for permeability, porosity, and compressive strength measurement. The results show that in the consolidation process with resin, modified urea-formaldehyde resin, as a consolidating agent, is more suitable than the other two types of resin. The consolidated sand samples of this resin had a compressive strength between 3100 and 4150 psi, permeability between 980 and 6823 mD, and porosity between 8 and 98%.

Abstract In this study, the modeling of hydrogen production process in microreactors by methanol-steam reforming reaction is investigated. The catalytic reaction of methanol-steam reforming producing hydrogen is simulated considering a 3D geometry for the microreactor. To calculate diffusion among species, mixture average correlations are compared to Stephan-Maxwell equations. The reactions occurring inside the microreactor include reforming of methanol with steam, methanol decomposition, and a reaction between carbon dioxide and hydrogen. The main objectives of this study are the prediction of temperature profile along the microreactor using either mixture average method or Stephan-Maxwell one and the comparison between the present predictions and some existing experimental data. The simulation results indicate that Stephan-Maxwell method conforms more suitably to the experimental results. The difference is more at lower feed flow rates since, when the flow rate increases, mass transfer mechanism changes from diffusion to convection, which in turn reduces the difference.

Abstract A new chemical compound is developed at Petroleum University of Technology to enhance the recovery of the free imbibition process and simultaneously hinder asphaltene precipitation. The compound is tested on heavy oil samples from Marun oil field, Bangestan reservoir. The effects of the chemical compound on viscosity, hydrocarbon composition, and average molecular weight of the heavy oil are investigated. It is found that the substance dramatically reduces oil viscosity and molecular weight and hinders the precipitation of asphaltene in the heavy oil. The results of free imbibition tests demonstrate a significant recovery enhancement after oil reacts with the compound and is used in water in an Amott cell. Finally, the new chemical compound causes a significant reduction in surface tension and contact angle. This is verified by the molecular analysis of heavy oil after reacting with this ionic compound.

Abstract Pore pressureis defined as the pressure of the fluid inside the pore space of the formation, which is also known as the formation pressure. When the pore pressure is higher than hydrostatic pressure, it is referred to as overpressure. Knowledge of this pressure is essential for cost-effective drilling, safe well planning, and efficient reservoir modeling. The main objective of this study is to estimate the formation pore pressure as a reliable mud weight pressure using well log data at one of oil fields in the south of Iran. To obtain this goal, the formation pore pressure is estimated from well logging data by applying Eaton’s prediction method with some modifications. In this way, sonic transient time trend line is separated by lithology changes and recalibrated by Weakley’s approach. The created sonic transient time is used to create an overlay pore pressure based on Eaton’s method and is led to pore pressure determination. The results are compared with the pore pressure estimated from commonly used methods such as Eaton’s and Bowers’s methods. The determined pore pressure from Weakley’s approach shows some improvements in comparison with Eaton’s method. However, the results of Bowers’s method, in comparison with the other two methods, show relatively better agreement with the mud weight pressure values.

Abstract The specific objective of this paper is to develop a fully implicit compositional simulator for modeling asphaltene deposition during natural depletion. In this study, a mathematical model for asphaltene deposition modeling is presented followed by the solution approach using the fully implicit scheme. A thermodynamic model for asphaltene precipitation and the numerical methods for performing flash calculation with a solid phase are described. The pure solid model is used to model asphaltene precipitation. The transformation of precipitated solid into flocculated solid is modeled by using a first order chemical reaction. Adsorption, pore throat plugging, and re-entrainment were considered in the deposition model. The simulator has the capability of predicting formation damage including porosity and permeability reduction in each block. A new set of independent unknowns in a fully implicit scheme is presented for asphaltene deposition modeling. In order to find the solution of these variables, the same number of equations is also presented. The description of how to solve the nonlinear system of equations is also described.

Abstract As a physiochemical property, asphaltenes are known to be one the most surface active compounds in crude oil. Due to such property, their behavior is most probably influenced by fluid-fluid interactions at the contact surface (interface). Potentially and naturally, in most cases, water is in contact with crude oil and is co-produced with it as well. Considering that asphaltene molecules are polar compounds similar to water molecules, asphaltenes are interfacially affected by water while they are absorbed to the interface. Such effects could be investigated by interfacial tension (IFT) changes when de-ionized water is used and dead-crude oil does not contain other surface active impurities like metallic compounds. In this study, extensive IFT experiments were conducted between three different oil samples and distilled water in a wide range of pressure from 2000 to 0 psia. The reversibility of asphaltene absorbance to the interface was also investigated by reversing the pressure path from 0 to 2000 psia. The results show that oil/water IFT changes with pressure, but upward/downward oscillations were detected. Such an oscillating behavior of IFT trends was related to asphaltenes surface activity as the oil samples used did not contain other impurities. Oscillations were reduced as resin to asphaltene ratio was increased, suggesting the non-absorbable behavior of the asphaltenes stabilized by resins. A microscopic surface experiment on one of the samples showed that at a certain concentration and particle size, a rigid film of absorbed asphaltenes was created at the interface instantaneously. The high rigidity of such a film gives rise to a hypothesis, which states that water affects asphaltene surface behavior possibly through strong hydrogen bonding (H-bond). Reversing the pressure path revealed that asphaltene surface absorbance is partially irreversible. The experiments were conducted three times, and each data set was presented along with an average of three sets for each sample.

Abstract A series of copper oxide (CuOx) catalysts supported by oxidized multi-walled carbon nanotubes (OMWNT’s) were prepared by the wet impregnation method for the low temperature (200 °C) selective catalytic reduction of nitrogen oxides (NOx) using NH₃ as a reductant agent in the presence of excess oxygen. These catalysts were characterized by FTIR, XRD, SEM-EDS, and H₂-TPR methods. The response surface methodology was employed to model and optimize the effective parameters in the preparation of CuOx/OMWNT’s catalysts in NOx removal by NH₃-SCR process. Three experimental parameters, including calcination temperature, calcination time, and CuOx loading were chosen as the independent variables. The central composite design was utilized to establish a quadratic model as a functional relationship between the conversion of NOx as a response factor and independent variables. The ANOVA results showed that the NOx conversion is significantly affected by calcination temperature and CuOx loading. At the optimal values of the studied parameters, the maximum conversion of NOx, 86.3 %, was obtained at a calcination temperature of 318 °C, a calcination time of 3.4 hr., and CuOx loading of 16.73 wt.%; the reaction conditions was as follows: T= 200 °C, P= 1 bar, NO = NH₃ = 900 ppm, O₂ = 5 vol.%, and GHSV = 30,000 hr.⁻¹. The regression analysis with an R²value of 0.9908 revealed a satisfactory correlation between the experimental data and the values predicted for the conversion of NOx. The XRD and H₂-TPR results of the best catalyst showed that the formation of CuO as the dominant phase of CuOx is the key factor in low temperature selective catalytic reduction (SCR) process.

Abstract Separation of nitrogen from a gaseous mixture is required for many industrial processes. In this study, the adsorption of nitrogen on zeolite 4A was investigated in terms of different adsorption isotherm models and kinetics. An increase in the initial pressure from 1 to 9 bar increases the amount of adsorbed nitrogen from 6.730 to 376.030 mg/(g adsorbent). The amount of adsorbed nitrogen increased from 7.321 to 40.594 mg/(g adsorbent) by raising the temperature from 298 to 333 K at a pressure equal to one bar; however, it then dropped to 15.767 mg/(g adsorbent) when temperature decreased to 353 K. Increasing the amount of the adsorbent from 1 to 4 g decreased the specific adsorption from 67.565 to 21.008 mg/(g adsorbent) at a temperature of 298 K and a pressure of 3 bar. Furthermore, it was found that the nitrogen adsorption experimental equilibrium data are consistent with Sips and Langmuir-Freundlich models. The highest overlap was achieved through second order and Ritchie’s models.

Abstract Fishing operations are one of the most essential parts of drilling operations. If the fishing operation fails, the other direction should be considered to continue drilling and reach the desired depth, which can be achieved using sidetracking operations. Long-term fishing operations increase the cost and time of the drilling operation; therefore, we should try to have a successful fishing operation in the shortest possible time. It can be stated that the execution of the fishing operation is economical as long as the costs are less or at least equal to the cost of the sidetracking operation. Therefore, the optimal fishing time must be determined to make the drilling operation economical. Many statistical analysis methods have been used to determine the optimal time, but they are not popular due to insufficient accuracy and time-consuming calculations. This study used a machine-learning (ML) model with a regression algorithm to estimate the optimal time for fishing operations in the Gachsaran oil field. The fishing cost rate and depth as input data were first collected and categorized based on different sections of the Gachsaran oil field to calculate the optimal fishing time. Then, the sidetracking cost was predicted by the machine learning model, and this cost was equated to the fishing cost in the worst conditions. As a result, the optimal fishing time was calculated for each section. The result showed that the model could estimate the cost of sidetracking with an error of less than 2%. Using the designed model and the input data of the Gachsaran oil field, considering the optimal fishing time, it was possible to save $1 million and 16 h in drilling a well.

Abstract One of the tertiary methods for enhanced oil recovery (EOR) is the injection of chemicals into oil reservoirs, and surface active agents (surfactants) are among the most used chemicals. Surfactants lead to increased oil production by decreasing interfacial tension (IFT) between oil and the injected water and to the wettability alteration of the oil reservoir rock. Since surfactants are predominantly expensive materials, it is required to consider an appropriate and high-performance plan for project economics when they are injected into oil reservoirs. One of the operational issues in surfactant flooding is the critical micelle concentration (CMC), which is usually achieved by the injection of surfactant at concentrations higher than CMC. Therefore, the lower the CMC is, the lower the amount of the material needed to be injected into the reservoir becomes, so it will help to economize the project. The salinity of the aqueous phase is a factor affecting the CMC, and with its optimal design, it can reduce the CMC. In this study, the variations of Triton X-100 CMC’s as a nonionic surfactant were measured by altering the concentration of three salts with divalent ions (CaCl₂, MgCl₂, and Na₂SO₄) and a single-capacity ion salt (NaCl), as the predominant salts in the porous medium of oil reservoirs, using surface tension (ST) method at ambient temperature and pressure. Each of these salts was dissolved at three concentrations of 0.1, 0.5, and 1 wt.% in distilled water containing specific concentrations of surfactant, and the surfactant CMC in the presence of these salt concentrations was measured. The results showed that increasing the concentration of each salt resulted in a decrease in the CMC, and, in the studied salts, NaCl produced the lowest CMC.

Abstract The effect of repeated repair welding, shielded with argon, on microstructural properties, corrosion resistance, and dry sliding wear behavior of aluminum alloy 5083/H116 were investigated. Samples were welded by metal inert gas welding method. 100% argon was used to protect fusion zone. Aluminum alloy 5356 was used as the filler metal. The samples for microstructure, corrosion, and wear tests were prepared from welded and repaired plates. To study the microstructural properties, the samples were mounted, polished, and then etched by the Keller^'s solution. Optical microscopy was used for metallurgical analysis. The corrosion behavior was evaluated in 3.5% NaCl solution and at a temperature of 25 °C using a potentiodynamic polarization and electrochemical impedance spectroscopy methods. Dry sliding wear behavior was evaluated by pin on disc method and scanning electron microscopy.

Abstract The present study is about the reduction of humic acids (HA) by electrocoagulation (EC) method. Undesirable color, odor, taste, reacting with chlorine to produce toxic materials in water, and making a complex with heavy metal ions are some unfavorable environmental consequences of HA. Platinum and graphite as anode electrodes and platinum, titanium, and aluminum as cathode electrodes were used for this purpose. Also, solutions consisting of sodium sulfate (Na₂SO₄), as the electrolyte support, and humic acids at a concentration of 30 mg.l^-1 were used in the reduction tests. We investigated the best condition for pollutant removal at pH values of 3, 5, and 7 and voltages of 5, 10, and 18. The samples were taken during the electrolysis and were analyzed by the pH meter and UV-visible spectrophotometer. Moreover, the oxidation phenomena on anodes surface were studied by cyclic voltammetry tests. The results confirm that the Gr/Al electrodes by coagulation phenomena shows the best performance in the elimination of HA at an electrolyte support concentration of 0.02 molar after approximately 23 min at a pH of 7 and a voltage equal to 10 V.

Abstract In the current study, the kinetics of asphaltene particle flocculation is investigated under a shear flow through numerical simulation. The discrete element method (DEM) is coupled with computational fluid dynamics (CFD) to model the agglomeration and fragmentation processes. In addition, a coalescence model is proposed to consider the attachment of colliding particles. The changes in mean asphaltene floc size, the particle size distribution (PSD) of asphaltene flocs over simulation time, and the average fractal dimension are presented. Moreover, the effect of fluid velocity on the kinetics of asphaltene flocculation is examined. The mean asphaltene floc size increases exponentially at first, and then the growth slows; finally, it ceases due to the establishment of a dynamic equilibrium between the agglomeration and fragmentation processes. As expected, asphaltene PSD’s move from fine to coarse sizes during the simulation. Log-normal distribution matches the PSDs best, which is in agreement with the nature of asphaltene. As fluid velocity increases, the dynamic equilibrium is attained more rapidly at a smaller mean floc size and higher average fractal dimension; furthermore, PSDs shift to smaller asphaltene floc sizes. The obtained average fractal dimensions of the asphaltene flocs are in the range of 1.65 to 1.74, which is compatible with the values reported in the literature. Eventually, a semi-analytical model is utilized to fit the simulation results. It is found out that the semi-theoretical model is capable of predicting the evolution of asphaltene particle size appropriately.

Abstract This paper addressed the application of new hydrophobic synthesized calcium oxide (CaO) and silicon dioxide (SiO₂) nanofluids to low permeability carbonate porous media. Crude oil and plugs were selected from one of oil reservoirs in the west of Iran. The main goal of this paper is comparing the results of improving water-oil relative permeability parameters in low permeability plugs of carbonate cores in the presence of new synthesized CaO and SiO₂ nanofluids. All the experiments were performed at a temperature of 40 °C and at a nanoparticle concentration of 45 ppm. The experimental approaches were designed into two main steps: 1) the effects of both nanoparticles on the changes in interfacial tension (between oil and brine) and oil viscosity 2) the effects of both nanoparticles on wettability (qualitatively) and relative permeability parameters. SiO₂ and CaO decreased interfacial tension from 46.414 mN/m to 41.772 mN/m and 32.860 mN/m respectively. Moreover, SiO₂ and CaO decreased oil viscosity from 9.90 cP to 8.61 cP and 8.01 cP respectively. Based on the obtained results in the core flood experiments, although CaO and SiO₂ nanofluids decreased effective water permeability, effective oil permeability and ultimate oil recovery increased. Moreover, it was seen that the CaO nanofluid improved oil flow in carbonate cores more than the commercial SiO₂ flooding. Finally, it was seen that both nanoparticles change the wettability from oil-wet to water-wet (qualitatively).

Abstract Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm) were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.

Abstract In this paper, decline curve analysis is used for estimating different parameters of bounded naturally fractured reservoirs. This analysis technique is based on rate transient technique, and it is shown that if production rate is plotted against time on a semi-log graph, straight lines are obtained that can be used to determine important parameters of the closed fractured reservoirs. The equations are based on Warren and Root model. The comparison between the results of this technique and those of the conventional methods confirms its high proficiency in transient well testing. It should be noted that in conventional decline curve methods, parameters such as interporosity flow parameter and storage capacity ratio must be first obtained by previous methods like the build-up analysis, but in the proposed method all the main reservoir parameters can be calculated directly, which is one of the advantages of this method. This paper focuses on the interpretation of rate tests, and the starting points and slopes of straight lines are utilized with proper equations to solve directly for various properties. The main important aspect of the presented method is its accuracy since analytical solutions are used for calculating reservoir parameters.