Iranian Oil Fields Research Articles

Bayesian Stochastic Inversion (A case study from an Iranian oil field)

We have implemented an estimation procedure whereby wireline data can be extrapolated away from existing well using geostatistical inversion of post-stack 3D seismic data. This procedure works directly in a fine-scale stratigraphic grid, and is conditioned by well and seismic data. It uses a Bayesian framework and a linearized, weak contrast approximation of the Zoeppritz equation to construct a joint log-Gaussian posterior distribution for Pwave impedances. Variograms are also estimated from well-log, seismic data and acoustic inversion results that define the expected degree of lateral smoothness away from the well. A sequential Gaussian Simulation algorithm is applied to sample the posterior PDF and generates multiple, high-resolution realizations of the acoustic impedance which can be utilized to generate stochastic realizations of petrophysical variables that not only honor the well-log data, but most importantly, that fully honor the 3D seismic. Sensitivity analysis was also performed to find the optimum values for S/N ratio, Variogram ranges and prior standard deviation. We had just one well in the area and preferred to keep it to perform quality control of the inversion results. Without constraining with well, geostatistical inversion still can be used to estimate acceptable static reservoir model for the subsequent simulation and planning of in-fill drilling and/or enhanced-oil-recovery operations.

Effect of salinity and ion type on formation damage due to inorganic scale deposition and introducing optimum salinity

Abstract Smart water injection is recognized as an effective EOR process to alter the wettability and interfacial tension to obtain higher micro/macro sweep efficiencies. This water contains reactive ions such as Mg 2 + , Ca 2 + and SO 4 2 - which can act as potential-determining ions and change the surface charge of calcite rocks. One of the major concerns in the execution of an effective water-flood process, especially in tight carbonate reservoirs, is the chemical incompatibility between the formation brine and the injecting water. In the present study, laboratory fluid compatibility tests were carried out and software simulation was done to investigate the most important challenges of the water-flooding process in one of the West Iranian oil fields with the possibility of developing formation damage. The chemical incompatibility between injected smart water and formation water leads to inorganic scale deposition. For this purpose, sea water as the basis of smart water was prepared to examine the impact of determining ions such as Mg 2 + , Ca 2 + , and SO 4 2 - and, to examine the effect of the salinity of the injected brine on the total amount of CaSO 4 scale precipitation. According to the obtained results, as the concentration of SO 4 2 - in the injecting sea water increases from 1 / 4 to 1 times its concentration in ordinary sea water, the CaSO 4 precipitation increases smoothly, and then it is accelerated as the concentration increases beyond that. Therefore, even though the increasing of the sulfate concentration improved the wettability alteration ability of the smart water, calcium sulfate deposition was noticed, resulting in permanent formation damage. The test results also showed that CaSO 4 deposition increases as the concentration of Ca 2 + in the sea water increases. On the other hand, the presence of Mg 2 + ion in sea water increases the solubility of CaSO 4 and subsequently, lower scale formation was noticed by increasing the concentration of magnesium. This study also showed that there is an optimum salinity in which the minimum amount of mineral scale is deposited, and 5 times dilution of sea water is determined as the optimum salinity. The findings would enable us to optimize the ion contents of smart water for both, better oil sweep efficiency and lower risk of formation damage. In the end, this paper discusses the laboratory fluid compatibility results and scale prediction analysis for different smart water injection utilizations.

Seismic modelling and its application on designing the seismic survey parameters, a case study of an Iranian oil field

ABSTRACTSpecifying survey parameters is more challenging when dealing with complex structures such as faults due to the complicated wavefield behaviour involved in these structures. Seismic modelling enables geophysicists to have a better understanding of the subsurface image before seismic acquisition, processing and interpretation. In this regard, seismic survey modelling is employed to construct a model close to the real structure, and to obtain very realistic synthetic seismic data. Therefore, a survey designer can investigate the effects of various survey parameters such as shot and receiver positions, and receiver line bearing on the resulting image using illumination and resolution analyses. The primary objective of this study is to apply seismic modelling to designing seismic survey parameters such as receiver line bearing, wavelets and their frequencies to analyse the resolution and illumination of the faulted target. A ray-based seismic model was built using 2D seismic data, geological reports and well logs. A pre-stack depth migration simulator was used to evaluate the effect of source wavelets with different peak frequencies (20, 60 and 80 Hz) on the resulting seismic image. This simulation indicated that a source wavelet with a peak frequency of 20 Hz could not image the target fault plane, but higher peak frequencies (60 and 80 Hz) show higher resolution and better recognition of the fault plane. Therefore, for any wavelet frequency, it is necessary to assess the ability of the fault illumination. The results proved that a survey designer could enhance the image of fault planes in a seismic section by applying ray-based analyses, concerning illumination and resolution of the fault image.

New empirical failure criterion for shale

A new failure criterion was presented to predict the ultimate strength of shale under the triaxial and polyaxial state of stress. A database containing 93 datasets was obtained from the results of the uniaxial, triaxial, polyaxial compressive tests, an indirect tensile test was collected from reliable references, and this test was carried out on the shale samples taken from the southwestern oilfields in Iran. The database was used to evaluate the proposed criterion, and its accuracy was compared against the popular failure criteria in rock mechanics, particularly those used for stability analysis such as the Hoek-Brown, Mohr-Coulomb, Drucker-Prager, and Mogi-Coulomb failure criteria. In order to evaluate the model, seven important statistical indices were selected. Subsequently, curves from various failure criteria were fitted to the triaxial and polyaxial data, and the corresponding coefficients and statistical indices were determined. The results obtained indicated that, in all cases, compared to the other failure criteria, the proposed criterion succeeded to predict the ultimate strength at a higher accuracy. Also the proposed criterion was used calculate the uniaxial compressive and tensile strengths with a minimum error. For a further examination of the proposed criterion, a series of results from the triaxial test including the ductile failure data were utilized for evaluation of the applicability of the proposed criterion to the ductile zone. It showed that the criterion could predict the ultimate strength of shale over a wide range of stresses.

The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity

The effectiveness of various thresholding techniques in segmenting micro X-ray computed tomography (XCT) images of porous carbonates has been investigated using experimental analysis. A comparison between directly measured and image-derived porosities clearly exhibited that the application of different segmentation methods produced vastly different results. The obtained results demonstrated the importance of the segmentation step for quantitative pore space analysis. In this research, three global thresholding methods and three locally adaptive thresholding methods were examined and only a few of the tested methods yielded acceptable results. Generally, locally adaptive methods generated better results; however, as the performance of global methods were reduced because of presence of anhydrite concentrations in the studied samples, a pre-segmentation for determining the anhydrite phase increased the performance of GT methods dramatically. As a result, two-threshold Otsu method yielded the best outcome. The novelty of this work is to compare the performance of different methods in segmentation of XCT images and to focus on the carbonates of one of the southern oilfields of Iran.

Sensitivity analysis and optimization on effective parameters during chemical enhanced oil recovery (CEOR) using experimental design and numerical simulation

ABSTRACTSeveral factors effect on choosing the best enhanced oil recovery process in a hydrocarbon reservoir. In the present study, effective factors on chemical enhanced oil recovery involved in polymer flooding, surfactant flooding, surfactant–polymer flooding, and alkaline–surfactant–polymer flooding are discussed in numerical simulation. The numbers of the simulation runs are estimated based on the number of factors and their maximum and minimum values using experimental design software. Oil recovery factor from reservoir simulation is considered as comparison factor in all of the run cases. The results from one parameter and two interaction factors during each flooding case were analyzed by Tornado and Parto plot. The oil recovery factor also was estimated using statistical analysis using Minitab. The results show that there is a strong correlation between simulation and statistical analysis. Furthermore, the results show that each selected factor has a different effect on oil recovery in each case of chemical flooding. The case study results on two Iranian oil fields at the end of the present study clarified that results of this study can be useful for selecting the best chemical process for oil reservoir based on reservoir properties.

Determination of Shale Volume, Shale Types and Effective Porosity Based On Cross Plotting

The evaluation of shaley formations has long been a difficult task. Presence of shale and shale types in some of the Iranian formations are one of the most important factors. Shale types have to be considered, because existence of shale reduces, porosity and permeability of the reservoir to some degree. Shale Distributed in formations in three basic types, Dispersed, Laminar and structural. Each of these shale types has different effect on porosity, permeability and saturation. Dispersed shale reduces porosity and permeability to a great degree, but, laminar shale and structural shale have little effect on petrophysical parameters. In this investigation, shale types, Shale volume and effective porosity of Kangan Formation have been determined from well log data and compared with crossplotting. In other words, a triangle Density-Neutron cross-plot is used to determine above parameters. The area of study lies in central oil fields of Iran, where one of the well is used (Tabnak Well C). Tabnak Well C selected to study Kangan Formation from Iranian oil field, in Pars onshore. This study illustrates that distribution of shale types in Kangan Formation is mainly dispersed shale with few of laminar shale, and percentage of effective porosity (φe) decreases with increasing depths for Kangan Formation.

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead chokes. It is useful to understand the relationship between flow rate through orifice meters (Qv) and the five fluid-flow influencing input variables: pressure (P), temperature (T), viscosity (μ), square root of differential pressure (ΔP^0.5), and oil specific gravity (SG). Here we evaluate these relationships using a range of machine-learning algorithms applied to orifice meter data from a pipeline flowing from the Cheshmeh Khosh Iranian oil field. Correlation coefficients indicate that (Qv) has weak to moderate positive correlations with T, P, and μ, a strong positive correlation with the ΔP^0.5, and a weak negative correlation with oil specific gravity. In order to predict the flow rate with reliable accuracy, five machine-learning algorithms are applied to a dataset of 1037 data records (830 used for algorithm training; 207 used for testing) with the full input variable values for the data set provided. The algorithms evaluated are: Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM), Radial Basis Function (RBF), Multilayer Perceptron (MLP), and Gene expression programming (GEP). The prediction performance analysis reveals that all of the applied methods provide predictions at acceptable levels of accuracy. The MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset studied. GEP and RBF also achieve high levels of accuracy. ANFIS and LSSVM perform less well, particularly in the lower flow rate range (i.e., <40,000 stb/day). Some machine learning algorithms have the potential to overcome the limitations of idealized streamline analysis applying the Bernoulli equation when predicting flow rate across an orifice meter, particularly at low flow rates and in turbulent flow conditions. Further studies on additional datasets are required to confirm this.

Estimation of rock uniaxial compressive strength for an Iranian carbonate oil reservoir: Modeling vs. artificial neural network application

Estimation of rock mechanic parameters is an important issue in reservoir management. Uniaxial compressive strength (UCS) and elastic modulus are the most important factors in determining the rock mechanic parameters in petroleum engineering studies. Accessibility to the parameters in fields such as designing fracture, analyzing of wellbore stability and drilling programming are very useful. The most accurate method to assign the aforementioned parameters is measuring these parameters in a laboratory. Laboratory determination of these parameters is problematic work due to technology issues, lack of laboratory equipment and coring problems in oil and gas wells, so indirect estimation of these parameters is required. Using well log data is the cheapest and most available approach in order to indirectly estimate these parameters. In this investigation, different models including multiple linear regression (MLR) and artificial neural network (ANN) (i.e., multi linear perceptron (MLP) and radial basis function (RBF)) were utilized for prediction of UCS via the three parameters of porosity, density and water saturation. These data were obtained from analysis of sonic, neutron, gamma ray and electric logs. The best results were obtained from a 3-15-1 MLP network which included one hidden layer and 15 neurons from the hidden layer using the trial and error method, and a 3-17-1 RBF which included 17 hidden neurons and a spread ó of 1.6. The core data from one of the carbonate Iranian oil fields (Asmari reservoir) were utilized for training, validation and testing of the networks, and correlation coefficients of 0.68, 0.90 and 0.83 were obtained for MLR, MLP and RBF, respectively.

Modified imaging condition for reverse time migration based on reduction of modelling time

Reverse time migration (RTM) is considered as a high-end imaging algorithm due to its ability to image geologically complex environments. However, this algorithm suffers from very high computational costs and low-frequency artefacts. The former drawback is the result of the intensive computations and huge memory allocation involved in RTM. Wave propagation modelling, as a kernel of RTM, demands intensive computations, and conventional imaging conditions are associated with huge memory allocation. In this paper, a modification of imaging condition is proposed that improves the efficiency of RTM as a reduction of computational cost, memory (RAM) allocation and low-frequency artefacts. The proposed imaging condition is similar to the conventional imaging condition but with the reduction of modelling time to near half the maximum time of recording. As the main idea of the proposed imaging condition, the impact of wave propagation modelling time is investigated on the quality of RTM and illumination of reflectors. The performance of the proposed method is considered using two synthetic models (SEG/EAGE and BP) and a real dataset from an Iranian oilfield in the south of Iran. Results showed that the new imaging condition can properly image the reflectors and enhance the efficiency of RTM. By using the proposed imaging condition, we achieved ~25% increase in CPU performance and 50% decrease in the memory allocation. Despite the improvement of the performance, results showed that the proposed imaging condition had no significant effect on the illumination.

Experimental investigation of EOR using low-salinity water and nanoparticles in one of southern oil fields in Iran

ABSTRACTThe increasing demand for hydrocarbons has led to a hope of developing more efficient recovery techniques such as nanotechnology and low-salinity water (LSW). In this study, interfacial tension (IFT) reduction was proposed as an important enhanced oil recovery mechanism of nanofluid injection, so IFT measurement between crude oil and nanofluid was performed by using pendant drop method. Introducing silica hydrophilic nanoparticles into the brine-oil system was observed to generate lower IFT and had the potential to produce more oil. Therefore, for LSW injection method, the effects of three different ions and total dissolved solid are investigated. In order to investigate the recovery mechanisms of nanofluids, IFT and contact angle have been measured. The experimental results indicate that the nanofluids can reduce the IFT and make the rock surface more water wet.

Characterization of micro-fractures in carbonate Sarvak reservoir, using petrophysical and geological data, SW Iran

In this paper, an integrated study of micro-fractures is carried out in Sarvak Formation. To achieve this goal the data from three wells (A, B and C) in one of the southwestern Iranian oil fields were used. In this study petrographic thin sections, porosity-permeability core data, image logs, Dipole Shear Sonic Imager (DSI) log, Velocity Deviation Log (VDL), Hydraulic Flow Units (HFU) concept, resistivity logs and drilling mud reports were used to identify and analyze micro-fractures in Sarvak Formation. The micro-fractures were first detected in thin sections and image logs directly, then the results of other methods in micro-fracture zones were investigated. The results show there is an abnormal trend of porosity-permeability in core data which is compatible with micro-fracture zones. Also, the relationship between micro-fractures and flow units was studied. Eight HFUs were recognized using porosity-permeability core data and FZI* (Flow Zone Indicator-Star) method. Later each of the HFUs was divided into two sub-HFUs; one of them is well-matched with the micro-fracture zones. The negative parts of VDL were in good agreement with the micro-fracture zones. The slowness anisotropy was mainly affected by the micro-fracture zones, but the energy and traveltime anisotropies were under the influence of the other phenomena such as solution seams, shale layers and borehole washouts. The fast shear wave azimuth was mainly aligned with the SHmax (maximum horizontal stress) direction, however, in micro-fracture zones, the direction suddenly changes. It was also observed that the micro-fractures do not have a considerable effect on Stoneley wave reflection, while the borehole washouts and shale layers have a great impact on this. The mud loss and mud invasion were very low in micro-fractured zones, which is related to the lack of lateral continuity of micro-fractures in the formation. The measured fracture apertures using thin section and image logs are often less than 0.1 mm, but the calculated values using Pezard-Anderson equations are very high.

Experimental study of polymer injection enhanced oil recovery in homogeneous and heterogeneous porous media using glass-type micromodels

In water flooding process, volumetric sweep efficiency and oil recovery can be enhanced using polymer to increase the viscosity of water. As a result, polymer flooding has higher recovery as compared to water flooding due to front stability and reduction of fingering problem. In this research work, a set of polymer flooding runs were carried out using glass-type micromodels. The micromodels were fabricated to have homogeneous and heterogeneous flow patterns. They were positioned horizontally and saturated with a heavy crude oil sample taken from an Iranian oil field before starting the injection. Three commercial polymers were used in this study. Whole process was photographed continuously with a high-resolution camera to monitor the displacement of polymer solution in the micromodels. As a part of this study, the effect of different parameters including polymer solution concentration, injection flow rate and heterogeneity on performance of polymer flooding was investigated. On top of the regular homogeneous and heterogeneous flow patterns used in this study, a heterogeneous flow pattern mimicking sandstone reservoirs was created based on the image of a thin section of a sandstone (outcrop) and polymer front movement was observed during injection.

Integrated Well Placement and Completion Optimization using Heuristic Algorithms: A Case Study of an Iranian Carbonate Formation

Determination of optimum location for drilling a new well not only requires engineering judgments but also consumes excessive computational time. Additionally, availability of many physical constraints such as the well length, trajectory, and completion type and the numerous affecting parameters including, well type, well numbers, well-control variables prompt that the optimization approaches become imperative;. The aim of this study is to figure out optimum well location and the best completion condition using coupled simulation optimization on an Iranian oil field located in southwest of Iran. The well placement scenarios are considered in two successive time intervals during of the field life, i.e., exploration and infill drilling phase. In the former scenario, the well-placement optimization is considered to locate the drilling site of a wildcat well, while the later scenario includes the optimum drilling location of a well is determined after 10-years primary production of nine production wells. In each scenario, two stochastic optimization algorithms namely particle swarm optimization, and artificial bee colony will be applied to evaluate the considered objective function. The net present value to drill production wells through the field life is considered as an objective function during our simulation-optimization approach. Our results show that the outcome of two population-based algorithms (i.e., particle swarm optimization and artificial bee colony) is marginally different from each other. The net present value of the infill drilling phase attains higher value using artificial bee colony algorithm.

Numerical simulation of a wellbore stability in an Iranian oilfield utilizing core data

Breakouts and induced tensile fractures are the two most important consequences of drilling operations. Breakouts are more important due to their influences on the formation of plastic zone around borehole wall, collapse and subsequent size changes of the borehole. According to the principles of fracture mechanics, borehole breakouts often occur with complicated failure mechanisms, involving both tensile and shear fractures. In this study, a boundary element numerical code (TDDQCR) is used to simulate the process of formation of borehole breakout after twenty steps of crack propagation in a vertical well in the south west of Iran. Then, these numerical simulation results are validated by FMI data which are obtained from the target well. This validation process shows that a very narrow and unclear breakout zone can be predicted by both numerical simulation and FMI data. The extension of plastic zone around the borehole wall is also simulated by a finite difference code (i.e. FLAC3D software). However, this numerical result did not predict any failure zone around the borehole wall. Therefore, based on this research, the target well is claimed to be in the stable condition.

Application of extended elastic impedance (EEI) inversion to reservoir from non-reservoir discrimination of Ghar reservoir in one Iranian oil field within Persian Gulf

Application of extended elastic impedance (EEI) inversion to reservoir from non-reservoir discrimination of Ghar reservoir in one Iranian oil field within Persian Gulf

Investigating subsurface structures of Gachsaran oil field in Iran using 2D inversion of magnetotelluric data

The magnetotelluric method is most often considered in hydrocarbon exploration in cases which are difficult for seismic imaging. In 2012, the National Iranian Oil Co. (NIOC) conducted an electromagnetic survey which included magnetotelluric and time domain electromagnetic (TEM) methods in the Gachsaran oil field in the south-west of Iran to delineate the reservoir formation of the region. Magnetotelluric data were collected at 5215 sites and a regular site spacing of 200 m was utilised. We chose a 16.5 km profile perpendicular to the main geological strike direction in the study area. The Bahr’s skew and Mohr diagrams were used to perform the dimensionality analysis of the magnetotelluric (MT) data and indicated that the subsurface structures are one dimensional or two dimensional at shallow depths, whereas they are mainly three dimensional at lower depths. The phase tensor showed that the dominant geoelectrical strike in the survey area is in the NW–SE direction. Two dimensional inversion was utilised to acquire a realistic resistivity model that was compromise between the spatial smoothness of the inversion model and the MT data fit. Apparent resistivity and phase data were modelled using the smoothness-constrained least-squares method. Models obtained of the TM, TE and TM+TE mode data were examined to have the best possible interpretation. The resulting 2D model revealed the main anticline and overthrust zone in the region. The near surface layer in the model which has a low resistivity, was identified as the cover rock of the region. The formation of the top of the reservoir in the region is estimated to be located at the depth of 1400–1900 m below sea level. The resistivity model is in good correlation with the geological features and the results of well drilling.

A new approach in petrophysical rock typing

Petrophysical rock typing in reservoir characterization is an important input for successful drilling, production, injection, reservoir studies and simulation. In this study petrophysical rock typing is divided into two major categories: 1) a petrophysical static rock type (PSRT): a collection of rocks having the same primary drainage capillary pressure curves or unique water saturation for a given height above the free water level, 2) a petrophysical dynamic rock type (PDRT): a set of rocks with a similar fluid flow behavior. It was shown that static and dynamic rock types do not necessarily overlap or share petrophysical properties, regardless of wettability.In addition, a new index is developed to define PDRTs via the Kozeny-Carman equation and Darcy's law. We also proposed a different index for delineation of PSRTs by combining the Young–Laplace capillary pressure expression and the Kozeny-Carman equation. These new indices were compared with the existing theoretical and empirical indices. Results showed that our indices are representatives of previously developed models which were also tested with mercury injection capillary pressure, water-oil primary drainage capillary pressure, and water-oil relative permeability data on core plugs from a highly heterogeneous carbonate reservoir in an Iranian oil field. This study enabled us to modify the conventional J-function to enhance its capability of normalizing capillary pressure data universally.

Investigating the effects of rock porosity and permeability on the performance of nitrogen injection into a southern Iranian oil reservoirs through neural network

In this work, a comprehensive model for Nitrogen injection into an oil reservoir (southern Iranian oil fields) was developed and used to investigate the effects of rock porosity and permeability on the oil production rate and the reservoir pressure decline. The model was simulated and developed by using ECLIPSE300 software, which involved two scenarios as porosity change and permeability changes in the horizontal direction. We found that the maximum pressure loss occurs at a porosity value of 0.07, which later on, goes to pressure buildup due to reservoir saturation with the gas. Also we found that minimum pressure loss is encountered at porosity 0.46. Increases in both pressure and permeability in the horizontal direction result in corresponding increase in the production rate, and the pressure drop speeds up at the beginning of production as it increases. However, afterwards, this pressure drop results in an increase in pressure because of reservoir saturation. Besides, we determined the regression values, R, for the correlation between pressure and total production, as well as for the correlation between permeability and the total production, using neural network discipline.

Prediction of ultimate strength of shale using artificial neural network

A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimate strength of shale, and comparison was made with support vector machine (SVM), multiple linear regression models, and the widely used conventional polyaxial failure criteria in the stability analysis of rock structures, Drucker-Prager, and Mogi-Coulomb. For building the model, the corresponding results of triaxial and polyaxial tests have been performed on shales by various researchers. They were collected from reliable published articles. The results obtained showed that a feed forward back propagation multi-layer perceptron (MLP) was used and trained using the Levenberg–Marquardt algorithm, and the 2-4-1 architecture with root-mean-square-error (RMSE) of 24.41 exhibits a better performance in predicting the ultimate strength of shale in comparison with the investigated models. Also for further validation, triaxial tests were performed on the deep shale specimens. They were prepared from the Ramshire oilfield in SW Iran. The results obtained were compared with ANN, SVM, multiple linear regression models, and the conventional failure criterion prediction. They showed that the ANN model predicted ultimate strength with a minimum error and RMSE being equal to 43.81. Then the model was used for prediction of the threshold broken pressure shale layer in the Gachsaran oilfield in Iran. For this, a vertical and horizontal stress was calculated based on a depth of shale layer. The threshold broken pressure was calculated for the beginning and ending of a shale layer to be 154.21 and 167.98 Mpa, respectively.