Arash Kamari

Compositional Model for Estimating Asphaltene Precipitation Conditions in Live Reservoir Oil Systems

Asphaltenes form the most polar fractions in crude oil, which decrease considerably the rock permeability and the oil recovery and in total can cause operational problems. Hence, it is important to estimate the asphaltene precipitation as a function of operating conditions, crude oil composition, and characterization. In this article, a reliable and robust model, namely, the least squares support vector machine, is applied to predict the onset pressures of asphaltene precipitation in live oil systems as well as oil saturation conditions. To pursue our objective, we used literature-reported onset and saturation (bubble point) pressures data of various live oils from different regions, but mostly from the Middle East, with different amounts of asphaltenes. The results indicate that the proposed strategy provides reasonably satisfactory predictive results. Additionally, the obtained results demonstrate not only the validation of the proposed method but also pose an interesting alternative to the classic methods of estimating asphaltene precipitation due to the low number of adjustable parameters used in our model.

Efficient screening of enhanced oil recovery methods and predictive economic analysis

Abstract Oil demand for economic development around the world is rapidly increasing. Moreover, oil production rates are getting a peak in mature reservoirs and tending to decline in the near future, which has led to considerable researches on enhanced oil recovery (EOR) methods. Therefore, an efficient technical and economical screening to appropriate selection of EOR methods can make savings in time and cost. The purpose of this communication is to present a method to select an efficient EOR process and investigate its economic parameters. A database of reservoir parameters of rock and fluid properties along with successful EOR techniques has been collected and analyzed. First, an artificial neural network (ANN) was developed to classify the EOR methods technically. Then, an economical EOR screening model was designed, and then, future cash flows on the use of EOR methods were predicted. The results show that the ANN system can select proper EOR methods and classify them. Moreover, the obtained results indicate that the economic analysis performed in this study is efficient and useful to predict future cash flows.

Evaluating the unloading gradient pressure in continuous gas-lift systems during petroleum production operations

Evaluating the performance, applicability, and field testing of various artificial lift methods, in particular continued gas-lift, can be time consuming and costly. To overcome these drawbacks, it is needed to propose a reliable model to estimate gas-lift applicability in advance of the installation under specific well operational conditions such as tubing size and design oil rate. In this study, the robust least square modification of support vector machine (LSSVM) methodology is implemented to propose a computer program, by which the unloading pressure gradient region can be determined in various design oil production rates and also tubing sizes. The developed LSSVM model results indicate 1.084% average absolute relative deviation from the corresponding unloading pressure gradient literature values, and squared correlation coefficient of 0.9994.

Modeling the permeability of heterogeneous oil reservoirs using a robust method

Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was utilized. Statistical and graphical error analyses have been employed separately to evaluate the accuracy and reliability of the proposed model. Furthermore, this model performance has been compared with a newly developed multilayer perceptron artificial neural network (MLP-ANN) model. The obtained results have shown the more robustness, efficiency and reliability of the proposed CSA-LSSVM model in comparison with the developed MLP-ANN model for the prediction of permeability in heterogeneous carbonate reservoirs. Estimations were found to be within acceptable agreement with the actual field data of permeability, with a root mean square error of approximately 0.42 for CSA-LSSVM model in testing phase, and a R-squared value of 0.98. Additionally, these error parameters for MLP-ANN are 0.68 and 0.89 in testing stage, respectively.

Modeling of the Properties of Gasoline and Petroleum Fractions Using a Robust Scheme

Gasoline is one of the most recognized products of the petroleum industry due to its use as a liquid fuel worldwide. As a result, it is of great importance to accurately determine the properties of gasoline, so as to evaluate its quality. In this article, an effective mathematical and predictive strategy, namely least squares support vector machines (LSSVM) is applied to predict some gasoline properties, viz. specific gravity (SG), motor octane number (MON), research octane number (RON), and Reid vapor pressure (RVP). A comprehensive error analysis is also undertaken to compare the values predicted from the model with actual data which enables one to evaluate the performance of the model developed in this study. The results indicate that the model developed has reasonable accuracy and prediction capability. The correlation indices, R2, are 0.990, 0.933, 0.955, and 0.920 for SG, MON, RON, and RVP, respectively.

On the determination of breakthrough time for water coning phenomenon in oil reservoirs

ABSTRACT The water coning phenomenon leads to decrease the wellhead pressure with moving of water into oil production zone, which is regarded as one of most serious problems during oil production. Therefore, the development of reliable models is important to predict the water coning breakthrough time, and consequently avoid the water coning phenomenon and production of water. To this end, the artificial neural network modeling strategy optimized with particle swarm optimization, least square support vector machine (LSSVM) approach coupled with the coupled simulated annealing optimization method, and finally decision tree method are implemented in current study to accurately predict the dimensionless breakthrough time of water coning. The results obtained in the present study demonstrate that the models proposed provide acceptable results in predicting the dimensionless breakthrough time of water coning. Furthermore, comparative study conducted illustrates the superiority of LSSVM methodology in terms of accuracy compared to the other methods investigated.

The Evaluation of the Impact of Wettability Alteration and Oil Relative Permeability Changes With Temperature During Cyclic Steam Injection in Naturally Fractured Reservoirs Using Horizontal Wells

In this communication, first, cyclic steam injection process in an Iranian heavy oil reservoir was simulated using three horizontal wells and the effect of various operational parameters on the performance was studied. This study has been done on the fractured reservoirs, as there are few studies on cyclic steam injection and the effect of temperature changes on the oil relative permeability in such reservoirs. Then, some practical values of irreducible water saturation and residual oil saturation at different temperatures have been considered for study of their effects on the oil recovery and oil relative permeability, because these practical values are so useful for prediction of production performance. The conclusions indicate that irreducible water saturation and residual oil saturation have significant impact on recovery factor and cumulative steam oil ratio. Comparison of four various methods show the difference in calculated oil relative permeability at various water saturations.

Determination of Triethylene Glycol Concentration in Natural Gas Dehydration Systems Using Support Vector Machine Algorithm

Triethylene glycol (TEG) liquid stream is used in natural gas dehydration units in order to prevent gas hydrate formation, the blockage of transportation pipelines, and liquid water condensation during transmission of natural gases in pipelines. Hence, it is needed to propose a reliable model to estimate the properties of TEG for better understanding of its performance. In this study, the robust least square modification of support vector machine (LSSVM) methodology is implemented to propose a computer program, by which the TEG concentration in natural gas dehydration unit can be determined at various reboiler pressure and temperature. In the final analysis, the Leverage approach (Williams plot) is applied to determine the applicability domain of the model and to detect any probable erroneous data points. It is found that the proposed TEG model is capable in low pressures compared to high ones. Additionally, the developed LSSVM model results indicate 0.08% average absolute relative deviation from the corresponding TEG concentration literature values, and squared correlation coefficient of 0.98.

An Efficient Approach for the Determination of Oil Production Rate During the Water-flooding Recovery Method

In this work, a mathematical methodology namely, least square support vector machine (LSSVM) is implemented to predict the variation of oil production rate as a function of oil water viscosity ratio and water injection rate for water-flooding. Furthermore, the coupled simulated annealing (CSA) optimization technique is coupled with LSSVM to find the optimal architecture and parameters of the LSSVM. The obtained results demonstrate that the CSA-LSSVM estimations are in a satisfactory agreement with literature-reported data and the previously published correlation. Consequently, the R2 and average absolute relative deviation of CSA-LSSVM model in testing phase are reported 0.979 and 8.15, respectively.

New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs

The determination of water saturation is a key step for the reservoir characterization and prediction of future reservoir performance in terms of production. The importance of water saturation has been further identified when the reservoirs refer to rocks with low porosity and permeability such as shale and tight formations. In this communication, two advanced artificial intelligence strategies consisting of least square support vector machine (LSSVM) and gene expression programming (GEP) have been applied in order to develop reliable predictive models for the calculation of water saturation of shale and tight reservoirs. To this end, an extensive core and log data bank has been analysed from 12 wells of a Mesaverde group tight reservoir located in the largest Western US. The results indicate that the estimated water saturation data by the models developed in this study are in satisfactory agreement with the actual log data. Furthermore, new methods proposed in this study are useful for the characterization of shale and tight reservoirs and can be applied to the relevant software.