Iranian Oil Fields Research Articles

Optimization of the Markov chain for lithofacies modeling: an Iranian oil field

Reconnaissance and interpretation of underground heterogeneity, particularly lithofacies, always plays an important role in evaluation and management of hydrocarbon resources. Between various methods presented for modeling discrete characteristics of hydrocarbon reservoirs such as lithofacies, one with a more proper conformity with actual condition of reservoir facies is of great advantage. Formed on the basis of probability and presenting transition matrix, the Markov method is widely applied as a powerful tool for modeling the facies. In the present study, first, the method is introduced in details; then, in order to optimize it for conditions with insufficient well data, two suggestions are made based on changing the motion direction of the chain and increasing the conditional boundary in simulation procedure. The case study is a 12-km-long 110-m-thick section of anhydrite and three major members of the Asmari Formation from an oil field in southwest Iran. This section is modeled through Markov classical procedure, changing chain motion direction and finally adding one seismic horizon as another conditional boundary. The models set indicated that on the basis of using the data from three wells and seven seismic horizons, the best result with 86 % accuracy is for the state of using two conditional boundaries.

The Optimization of a Slotted Liner Design for a Horizontal Well

Sand production is one of the most common production problems worldwide. During fluid flow through porous media, some loose particles may leave rock structure and associate with the production fluid. These particles can impose erosion, corrosion, and other operational problems in flow line and production facilities. Various methods has been proposed and developed to prevent and control sand production. Slotted liner completions are by far the most common devices run in horizontal wells. The authors probed the parameters that can affect on the performance of this method in a horizontal well in one of Iranian oil field. Results are reported in terms of slotted liner total skin factor. The study shows that the total skin is strongly governed by some designing parameters, especially in low open areas (1%). If using sand screens with lower open area is inevitable, due to installation condition, some remedies needed to lower the total skin to an acceptable amount.

Estimation of Flow Zone Indicator Distribution by Using Seismic Data: A Case Study from a Central Iranian Oilfield

Flow unit characterization plays an important role in heterogeneity analysis and reservoir simulation studies. Usually, a correct description of the late ral variations of reservoir is associated with uncertainties. From this point of view, the well da ta alone does not cover reservoir properties. Because of large well distances, it is difficult to build the model of a heterogenic reservoir, but 3D seismic data provides regular sampling that can imp rove reservoir spatial description. In this study, seismic attribute analysis was used to predict flow zone indicator (FZI) values of a carbonate reservoir by using seismic and well log d ata. First, a 3D acoustic impedance volume was created as an external attribute for seismic data a nalysis. To improve the ability of FZI prediction, the maximum number of attributes from multiattribute analysis was computed by using a step-wise regression technique. To verify the results of mult iattribute technique, the cross plot analysis of multiattribute method was performed. It was found t hat the R 2 value of the correlation between the predicted and actual FZI is as high as 0.859 with a n average error value of 2.34 µm. The analysis of the results of multiattribute technique showed that it was an effective technique for FZI prediction i n hydrocarbon reservoirs. Such accuracy in building a 3D distribution of FZI provides a good insight into reservoir production zones. The results clearl y indicate that the methodology proposed herein can successfully be used to specify the locations of ne w wells for the purpose of future production or injection plans.

Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

Accurate prediction of pore pressures has become almost essential to exploration, completion and drilling wells with higher-than-normal pore pressures. There are some limitations with the current existing pore pressure estimation methods, as they are based on empirical relations and constants which can differ from basin to basin. In this study, a feed-forward network with back-propagation algorithm and a generalised regression neural network have been developed to predict pore pressure gradient in Asmari reservoir in one of oilfields in Iran. To show the accuracy of the method, the results of the developed models have been compared with Eaton’s method.

Prediction of the Best EOR Method by Artificial Intelligence

In this article, five type of enhanced oil recovery (EOR) methods and seven of the most important reservoir parameter get noticed. Also, data from more than 200 oil fields from all around the world collected where at least one of the EOR methods has been applied. It has been tried via Adaptive Network-based Fuzzy Interface System (ANFIS) along clustering and normalizing techniques to design a model proficient to foresight and compare behavior of the five EOR methods in case of cumulative oil production based on seven input parameters. Designed model applied to one of the Iranian oil fields and results have been discussed.

Prediction of Iron Carbonate Scale Formation in Iranian Oilfields at Different Mixing Ratio of Injection Water with Formation Water

Enhanced oil recovery methods are used to recover the percents of oil that are not naturally recoverable from reservoirs. Water injection, as a secondary recovery, is used to maintain the pressure in water-driven reservoirs. An important point for having a successful injection is the compatibility of injection and formation waters. This article presents the predicting scale tendency and scale precipitation of iron carbonate at different mixing ratios of injection water with formation water in Iranian oilfields. The experimentally measured chemical analyses of formation water and injection water were used by OLI ScaleChem software to determine the formation of this scale. This software has been applied to investigate the potential of iron carbonate scale formation in Iranian oilfields, as a method of secondary recovery or reservoir pressure maintenance.

Prediction of the Amount of Calcium Carbonate Scale Formation in Iranian Oilfields at Different Pressures

Enhanced oil recovery methods are used to recover the percents of oil, which are not naturally recoverable from reservoirs. Water injection, as a secondary recovery, is used to maintain the pressure in water-drive reservoirs. An important point for having a successful injection is the compatibility of injection and formation waters. This article presents the predicting of calcium carbonate precipitation in formation water, injection water, and mixing of injection water with formation water at different pressures. The experimentally measured chemical analyses of formation water and injection water were used by OLI ScaleChem software to determine the amount of CaCO3 scale that can be formed. This software has been applied to investigate the potential of calcium carbonate scale precipitation in Iranian oilfields, either in onshore or offshore fields where water injection is being performed for desalting units' water disposal purpose or as a method of secondary recovery or reservoir pressure maintenance. As pressure was increased, the amount of CaCO3 precipitation decreased, as was predicted by OLI ScaleChem software.

Support Vector Machine Based Facies Classification Using Seismic Attributes in an Oil Field of Iran

Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases associated with supervised classification methods. In this study, we follow supervised classification scheme under classifiers, the support vector classifier (SVC), and multilayer perceptrons (MLP) to provide an opportunity for directly assessing the feasibility of different classifiers. Before choosing classifier, we evaluate extracted seismic attributes using forward feature selection (FFS) and backward feature selection (BFS) methods for logical SFA. The analyses are examined with data from an oil field in Iran, and the results are discussed in detail. The numerical relative errors associated with these two classifiers as a proxy for the robustness of SFA confirm reliable interpretations. The higher performance of SVC comparing to MLP classifier for SFA is proved in two validation steps. The results also demonstrate the power and flexibility of SVC compared with MLP for SFA.

Development of drilling trip time model for southern Iranian oil fields: using artificial neural networks and multiple linear regression approaches

One of the main goals of drilling venture is the minimum drilling cost. The minimum cost for every drilling interval depended upon the trip time. As general rule for any area where the trip time is not given, 1 h for 1,000 ft is used in calculation of cost per foot of drilling. In this study, an attempt is made to develop a model for estimating drilling trip time in the southern Iranian oil fields. For this purpose, drilling data from the drilling daily reports of the drilled wells in three southern Iranian oil fields were gathered. In this work, both an artificial neural networks (ANN) model and a multiple linear regression model have been developed for estimating drilling trip time. The results indicate that the ANN model predicts trip time more accurately than the multiple linear regression model. However, the multiple linear regression model is more usable.

Fuzzy classifier based support vector regression framework for Poisson ratio determination

Abstract Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

Reservoir lithofacies analysis using 3D seismic data in dissimilarity space

Seismic data interpretation is one of the most important steps in exploration seismology. Seismic facies analysis (SFA) with emphasis on lithofacies can be used to extract more information about structures and geology, which results in seismic interpretation enhancement. Facies analysis is based on unsupervised and supervised classification using seismic attributes. In this paper, supervised classification by a support vector machine using well logs and seismic attributes is applied. Dissimilarity as a new measuring space is employed, after which classification is carried out. Often, SFA is carried out in a feature space in which each dimension stands as a seismic attribute. Different facies show lots of class overlap in the feature space; hence, high classification error values are reported. Therefore, decreasing class overlap before classification is a necessary step to be targeted. To achieve this goal, a dissimilarity space is initially created. As a result of the definition of the new space, the class overlap between objects (seismic samples) is reduced and hence the classification can be done reliably. This strategy causes an increase in the accuracy of classification, and a more trustworthy lithofacies analysis is attained. For applying this method, 3D seismic data from an oil field in Iran were selected and the results obtained by a support vector classifier (SVC) in dissimilarity space are presented, discussed and compared with the SVC applied in conventional feature space.

Integrating Artificial Neural Networks Technique and Geostatistical Approaches for 3D Geological Reservoir Porosity Modeling with an Example from One of Iran's Oil Fields

Porosity is a crucial reservoir parameter with a significant effect on reservoir management and field operations. In uncored intervals and especially heterogeneous formation, porosity estimation from conventional well logs is complex and time consuming to solve by conventional methods. The authors propose an intelligent technique using artificial neural networks to determine reservoir porosity from well logs and compares its result with that of multiple linear regression technique. Actually neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core porosity measurements, finally optimum results will be fed to different geostatistical approaches to construct 3D geological reservoir porosity models for a better understanding of reservoir. The mentioned method is demonstrated with an application to field information of a carbonate oil reservoir with a high level of heterogeneity, located in southwest of Iran, and validity of the results is checked by statistical methods such as histogram analysis and cross validation approach. The results show that suggested technique can make more accurate and reliable reservoir porosity estimation compared with conventional computing methods and also the constructed 3D geostatistical models provide realistic descriptions of reservoir porosity considering available geological interpretations of studied area.

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Networks (NN) algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS) and Truncated Gaussian Simulation (TGS). The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

Optimization of Gas Allocation to a Group of Wells in Gas Lift in One of the Iranian Oil Fields Using an Efficient Hybrid Genetic Algorithm (HGA)

A hybrid genetic algorithm (HGA) was introduced to allocate optimum amount of gas. This method was applied to a group of wells in gas lift in the case of availability limited amount of gas. For testing the ability of the proposed HGA, the results of this work with those of previous works in a field with six wells were compared. Then for an Iranian southern west oil field with nine wells, gas allocation is performed using different amount of available gas. The results show that the introduced method (HGA) is very efficient tool in gas allocation issue.

Prediction of mud loss in reservoir rock by geostatistical method

In Iranian oil fields, Asmari Formation has great importance during the drilling operation because most of the oil reservoirs are located in this formation. One of the problems commonly encountered by petroleum engineers during drilling in the Asmari Formation is mud loss. The geostatistical methods can convert the qualitative experience to quantitative and provides the approach for better results. There are different methods for the three-dimensional (3D) modelling of a reservoir. In this study, the distribution model of mud loss was designed for Asmari Formation using geostatistical methods and based on data of 389 wells drilled in an oil field in Southwest Iran.

Application of wavelet transform for evaluation of hydrocarbon reservoirs: example from Iranian oil fields in the north of the Persian Gulf

Abstract. The Persian Gulf and its surrounding area are some of the biggest basins and have a very important role in producing huge amounts of hydrocarbon, and this potential was evaluated in order to explore the target for geoscientists and petroleum engineers. Wavelet transform is a useful and applicable technique to reveal frequency contents of various signals in different branches of science and especially in petroleum studies. We applied two major capacities of continuous mode of wavelet transform in seismic investigations. These investigations were operated to detect reservoir geological structures and some anomalies related to hydrocarbon to develop and explore new petroleum reservoirs in at least 4 oilfields in the southwest of Iran. It had been observed that continuous wavelet transform results show some discontinuities in the location of faults and are able to display them more clearly than other seismic methods. Moreover, continuous wavelet transform, utilizing Morlet wavelet, displays low-frequency shadows on 4 different iso-frequency vertical sections to identify reservoirs containing gas. By comparing these different figures, the presence of low-frequency shadows under the reservoir could be seen and we can relate these variations from anomalies at different frequencies as an indicator of the presence of hydrocarbons in the target reservoir.

The Study of Calcium Sulfate Scale Formation during Water Injection in Iranian Oil Fields at Different Pressures

Scale formation in surface and subsurface oil and gas production equipment has been recognized to be one of the major operational problems. It has also been recognized as one of the major causes of formation damage either in injection or producing wells. Scale contributes to equipment wear and corrosion and flow restriction, thus resulting in a decrease in oil and gas production. This article presents the predicting of calcium sulfate scale tendency of formation water, injection water, and mixing of injection water with formation water at different pressures. The experimentally measured chemical analyses of formation water and injection water were used by OLI ScaleChem software to determine the tendency of scale formation. This software has been applied to investigate the potential of calcium sulfate scale tendency in Iranian oil fields, either in onshore or offshore fields where water injection is being performed for desalting units' water disposal purpose or as a method of secondary recovery or reservoir pressure maintenance.

Permeability Prediction Based on Hydraulic Flow Units (HFUs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS) in an Iranian Southern Oilfield

An accurate description of reservoir is necessary for reservoir simulation and performance prediction. It goes without saying that permeability and porosity are two important fundamental parameters to be known for reservoir characterization. Since it is not conceivable to perform coring operation for any place in a reservoir because of cost and time, different methodologies have been introduced to calculate these parameters from different well logs that are usually available for every well in a reservoir. Hydraulic flow unit (HFU) is one of the practical methods in reservoir simulation. HFU is defined as volume of rock that according to petrophysical and geological properties has similar flow of fluid. The concept of HFU has been developed to integrate geological and petroleum engineering data to estimate permeability in cored zone and then use their result to uncored zone. For estimation of HFUs there are some approaches according to data available that some of these methods are gamma ray, flow zone indicator, cumulative flow and storage capacity curve, and stratigrafic modified Lorenz plot. Computational intelligences are another approach that be used to estimate permeability and porosity in cored zone and related the results to uncored zone. Fuzzy logic, genetic algorithm, artificial neural network, and adaptive neuro-fuzzy inference systems (ANFIS) are some kind of computational intelligences. The authors used ANFIS and HFU models to estimate permeability in a southern Iranian oilfield.

An Experimental Investigation of Permeability Impairment Under Dynamic Flow Conditions Due to Natural Depletion in an Iranian Oilfield

Asphaltene deposition is an issue that has received much attention since it has been shown to be the cause of major production problems. It leads to permeability reduction under the processes of natural depletion as well as hydrocarbon gas/CO2 injection. Though a great deal of researches have focused on studying permeability impairment in reservoir rocks, little is known about the asphaltene deposition mechanisms that control the permeability reduction for Iranian reservoirs. In this work, an experimental effort is made to investigate the permeability impairment of core samples of Iranian oil reservoirs. The experiments are performed on both sandstone and carbonate rock types at reservoir temperature and pressure. The mass balance was used for evaluating of porosity reduction during the experiments. The results indicate that the dominant deposition mechanism changes as production proceeds. In addition, it has been found that the primary mechanism in permeability impairment is surface deposition. On the other hand, entrainment of asphaltene particles is manifested when outlet pressure drops from 4,200 to 3,800 Psig for both sandstone and carbonate samples. It can be drawn that asphaltene entrainment dependence to pressure is much more than that to the injected pore volume. This research illuminates the deposition mechanisms and determines dynamic parameters of asphaltene deposition, which are necessary to devise reliable prevention strategies.

Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.