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AbstractKnowledge of chemical and physical properties of formation water is very important in various reservoir engineering computations especially in water flooding and production. Ideally, those data should be obtained experimentally. On some occasions, these data are not either available or reliable; then, empirically derived correlations are used to predict brine PVT properties. These correlations offer a handy and an acceptable approximation of formation water p… Show more
“…The recent success of applying artificial neural networks (ANN) to solve various engineering problems has drawn the attention to its potential applications in the petroleum industry. This has been recently demonstrated by several investigators in the prediction of various pressure volume-temperature (PVT) properties, e.g., Osman et al [10], AI-Marhoun and Osman [1], Osman and Al Marhoun [11], and more recently, Elshafei and Hamada [4], and Oloso et al [9]. It was observed that ANN models have superior performance and accuracy in performing predictions compared to those of the available correlations.…”
The paper presents a novel neural network based method for predicting the performance of the multi-stage gas/oil separation plants in crude oil production. The developed method can play an important role during planning and operation of oil production facilities. It predicts important oil properties, which are usually obtained from expensive and time-consuming laboratory tests. The prediction is based mainly on the basic oil composition analysis from standard lab tests. The neural networks were trained using data collected from laboratory tests. The objective of these tests is to find the best oil/gas separation stages to minimize the separator gas/oil ratio and to improve the resulting oil specific gravity. The neural networks accept the initial and final pressures and temperatures of each stage and then try to utilize the oil composition information to predict the stage gas/oil ratio. Two neural networks were built, one for the initial stage with difference of pressure over 400 psi, and the second for the separator stages covering the lower range of pressure. The method can also be useful simulation tool in optimizing the oil production operation.
“…The recent success of applying artificial neural networks (ANN) to solve various engineering problems has drawn the attention to its potential applications in the petroleum industry. This has been recently demonstrated by several investigators in the prediction of various pressure volume-temperature (PVT) properties, e.g., Osman et al [10], AI-Marhoun and Osman [1], Osman and Al Marhoun [11], and more recently, Elshafei and Hamada [4], and Oloso et al [9]. It was observed that ANN models have superior performance and accuracy in performing predictions compared to those of the available correlations.…”
The paper presents a novel neural network based method for predicting the performance of the multi-stage gas/oil separation plants in crude oil production. The developed method can play an important role during planning and operation of oil production facilities. It predicts important oil properties, which are usually obtained from expensive and time-consuming laboratory tests. The prediction is based mainly on the basic oil composition analysis from standard lab tests. The neural networks were trained using data collected from laboratory tests. The objective of these tests is to find the best oil/gas separation stages to minimize the separator gas/oil ratio and to improve the resulting oil specific gravity. The neural networks accept the initial and final pressures and temperatures of each stage and then try to utilize the oil composition information to predict the stage gas/oil ratio. Two neural networks were built, one for the initial stage with difference of pressure over 400 psi, and the second for the separator stages covering the lower range of pressure. The method can also be useful simulation tool in optimizing the oil production operation.
“…Examination of the model including all fluids type revealed a lower level of error compared to tuned equation of state models. Osman and AlMarhoun (Osman and Al-Marhoun, 2005) applied radial basis functions and multilayer perceptron neural networks and developed two intelligent models to predict oil field brines PVT properties. In 2009, Khoukhi et al (Khoukhi et al, 2011) published predictive models based on three different artificial intelligent models.…”
“…The CI techniques achieved this by establishing nonlinear relations between the log measurements and the core values for prediction. CI techniques have also been reported to outperform the statistical regression tools (Mohaghegh 2000;Goda et al 2003;Osman and Al-Marhoun 2005;Zahedi et al 2009;Al-Marhoun et al 2012).…”
Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.
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