Research Paper
Volume 2, Issue 3 , Summer 2013
Research Paper
Majid Bagheri; Mohammad Ali Riahi
Abstract
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 ...
Read More
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.
Research Paper
Morteza Nouri Taleghani; Sadegh Saffarzadeh; Mina Karimi Khaledi; Ghasem Zargar
Abstract
Porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. It is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. Log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon ...
Read More
Porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. It is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. Log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon parameters are obtained by virtue of petrophysical data. There are three main petrophysical logging tools for the determination of porosity, namely neutron, density, and sonic well logs. Porosity can be determined by the use of each of these tools; however, a precise analysis requires a complete set of these tools. Log sets are commonly either incomplete or unreliable for many reasons (i.e. incomplete logging, measurement errors, and loss of data owing to unsuitable data storage). To overcome this drawback, in this study several intelligent systems such as fuzzy logic (FL), neural network (NN), and support vector machine are used to predict synthesized petrophysical logs including neutron, density, and sonic. To accomplish this, the petrophysical well logs data were collected from a real reservoir in one of Iran southwest oil fields. The corresponding correlation was obtained through the comparison of synthesized log values with real log values. The results showed that all intelligent systems were capable of synthesizing petrophysical well logs, but SVM had better accuracy and could be used as the most reliable method compared to the other techniques.
Research Paper
Mohsen Karimian; Nader Fathianpour; Jamshid Moghaddasi
Abstract
Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with ...
Read More
Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions.
Research Paper
Hadi Zolfaghari; Alireza Zebarjadi; Omid Shahrokhi; Mohammad Hosein Ghazanfari
Abstract
Several studies have shown that oil recovery significantly increased by low salinity water flooding (LSWF) in sandstones. However, mechanism of oil recovery improvement is still controversial. CO2 that develops buffer in presence of water is expected as a deterrent factor in LSWF efficiency based on ...
Read More
Several studies have shown that oil recovery significantly increased by low salinity water flooding (LSWF) in sandstones. However, mechanism of oil recovery improvement is still controversial. CO2 that develops buffer in presence of water is expected as a deterrent factor in LSWF efficiency based on mechanism of IFT reduction due to pH uprising. No bright evidence in literature supports this idea. Here, a set of core floods including a pair of CO2 WAG and a pair of water injection tests are conducted and, the efficiency of LSWF and high salinity water flooding (HSWF) were compared for each pair. HSWF was followed by LSWF in tertiary mode. Results showed that not only CO2 does not deteriorate LSWF recovery efficiency, but also improves recovery. Since CO2-low salinity WAG showed best performance among types by constant pore volume injected. Positive results in both secondary and tertiary modes with Kaolinite free samples used here showed that Kaolinite release is not the critical phenomenon in LSWF brisk performance. Also different pressure behaviour of CO2 WAG processes in comparison with reported behaviour of LSWF proves that LSWF performance may not depend on how pressure changes through flooding.
Research Paper
Mohsen Esmaeili; Ali Heydarian; Abbas Helalizadeh
Abstract
Chemical flooding, especially alkaline/surfactant/polymer flooding, is of increasing interest due to the world increasing oil demand. This work shows the aspects of using alkaline/surfactant/polymer as an enhanced oil recovery method in the porous media having a high dead-end pore frequency with various ...
Read More
Chemical flooding, especially alkaline/surfactant/polymer flooding, is of increasing interest due to the world increasing oil demand. This work shows the aspects of using alkaline/surfactant/polymer as an enhanced oil recovery method in the porous media having a high dead-end pore frequency with various dead-end pore parameters (such as opening, depth, aspect ratio, and orientation). Using glass micromodels makes it possible to manipulate and analyze the pore parameters and watch through the porous media precisely. The results show that polyacrylamide almost always enhances oil production recovery factor (up to 14% in comparison with brine injection) in this kind of porous media. Except at low concentrations of polyacrylamide and sodium carbonate, sodium dodecyl sulfonate improves oil recovery (even 15% in the case of high polyacrylamide concentration and low sodium carbonate concentration). Increasing alkaline concentration reduces recovery factor except at low concentrations of polyacrylamide and high concentrations of surfactant.
Research Paper
Mohammad Khalili; Riyaz Kharrat; Karim Salahshoor; Morteza Haghighat sefat
Abstract
One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best ...
Read More
One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best solution. Since it is required to run the reservoir simulator hundreds of times, the process is very time consuming and cumbersome. In this study a new intelligent method of optimization, called “global dynamic harmony search” is used with some modifications in combination with a commercial reservoir simulator (ECLIPSE®) to determine the optimum solution for fluid injection problem unknowns. Net present value (NPV) is used as objective function to be maximized. First a simple homogeneous reservoir model is used for validating the developed method and then the new optimization method is applied to a real model of one of the Iran oil reservoirs. Three strategies, including gas injection, water injection, and well placement are considered. Comparing the values of NPV and field oil efficiency (FOE) of gas injection and water injection strategies, it is concluded that water injection strategy surpasses its rival. Considering water injection to be the base case, a well placement optimization is also done and best locations for water injection wells are proposed. The results show the satisfying performance of the algorithm regarding its low iterations.
