Hamid Heydari; Jamshid Moghadasi; Reza Motafakkerfard
Abstract
Cementation factor is a critical parameter, which affects water saturation calculation. In carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. Hence developing a reliable mathematical strategy to determine these properties ...
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Cementation factor is a critical parameter, which affects water saturation calculation. In carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. Hence developing a reliable mathematical strategy to determine these properties accurately is of crucial importance. To this end, genetic algorithm pattern search is employed to find accurate cementation factor by using formation resistivity factor and the porosity obtained from laboratory core analyses with considering the assumption that tortuosity factor is not unity. Subsequently, particle swarm optimization (PSO) fuzzy inference system (FIS) was used for the classification of cementation factor according to the predominated rock pore type by using the input variables such as cementation factor, porosity, and permeability to classify the core samples in three groups, namely fractured, interparticle, and vuggy pore system. Then, the experimental data which was collected from Sarvak formation located in one of the Iran southwestern oil fields was applied to the proposed model. Next, for each class, a cementation factor-porosity correlation was created and the results were used to calculate cementation factor and water saturation profile for the studied well. The results showed that the constructed model could predict cementation factor with high accuracy. The comparison between the model presented herein and the conventional method demonstrated that the proposed model provided a more accurate result with a mean square error (MSE) of around 0.024 and led to an R2 value of 0.603 in calculating the water saturation.
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 ...
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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.
