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Bubble point pressure (Pb), one of the most important parameters of reservoir fluids, plays an important role in petroleum engineering calculations. Accurate determination of Pb from laboratory experiments is time, cost and labor intensive. Therefore, the quest for an accurate, fast and cheap method of determining Pb is inevitable. In this communication, a sophisticated approach was followed for formulating Pb to temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. Firstly, support vector regression (SVR), a supervised learning algorithm plant based on statistical learning (SLT) theory, was employed to construct a model estimating Pb. Subsequently, an alternating conditional expectation (ACE) was used to transform input/output data space to a highly correlated data space and consequently to develop a strong formulation among them. Eventually, SVR and ACE models are combined in a power-law committee machine structure by virtue of genetic algorithm to enhance accuracy of final prediction. A comparison among constructed models and previous models using the concepts of correlation coefficient, mean square error, average relative error and absolute average relative error reveals power-law committee machine outperforms all SVR, ACE, and previous models.

Deposition of asphaltene during crude oil production is a challenging issue in oil industry which causes considerable loss of production efficiency as well as imposes negative impacts on production rates. Upon variation in pressure, temperature and crude oil composition, asphaltene begins to precipitate and deposits in reservoir rock and consequently causes formation damage owing to mechanisms of wettability alteration and pore throat blockage. In the present study a sophisticated method, called committee machine with optimized intelligent systems was utilized to predict the amount of asphaltene precipitation from experimental titration data. The committee machine is composed of optimized neural network and optimized fuzzy logic. Stochastic optimization of neural network and fuzzy logic by virtue of hybrid genetic algorithm-pattern search technique significantly enhances their efficiencies. The committee machine provides a further improvement in accuracy of final prediction through integrating optimized intelligent systems and consequent reaping of their benefits. The committee machine model was applied to experimental data reported in the open-source literature. It was observed that there was an acceptable agreement between experimental data and committee machine predicted values. Finally, performance of committee machine model was compared with other intelligent systems used for prediction of asphaltene precipitation. Results showed superiority of committee machine in asphaltene precipitation modeling to optimized neural network and optimized fuzzy logic.