Download Soft Computing for Reservoir Characterization and Modeling by P. M. Wong, F. Aminzadeh, M. Nikravesh (auth.), Dr. Patrick PDF

By P. M. Wong, F. Aminzadeh, M. Nikravesh (auth.), Dr. Patrick Wong, Dr. Fred Aminzadeh, Dr. Masoud Nikravesh (eds.)

The quantity is the 1st complete booklet within the region of clever reservoir characterization written through best specialists in academia and undefined. It comprises cutting-edge recommendations to be utilized in reservoir geophysics, good logging, reservoir geology, and reservoir engineering. It introduces the fundamental thoughts of sentimental computing ideas together with neural networks, fuzzy common sense and evolutionary computing utilized to reservoir characterization. a few complicated statistical and hybrid versions also are provided. the explicit purposes comprise various reservoir characterization issues resembling prediction of petrophysical houses from good logs and seismic attributes.

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Example text

Gas-bearing property is only attached to reservoir sandstone units. In Iwafune Oki field, it is mainly attached to Lower and Upper 2, lOOm zone sandstones and in Pantai Pakam Timur field, the property is only attached to 1,275m zone sandstone. 2 Pseudo well simulation As stated before, it is not enough to use only factual well data for establishing the relationship between reservoir properties and seismic attributes in the study areas because available wells are either few (PPT field) or too concentrative (Iwafune Oki field).

By accepting the reliability of the two maps, through the evaluation using the two approaches, information regarding lateral distribution of porosity and water saturation for the study area is at disposal for further development of the field. 4 Further discussion As in the case of most reef-limestone reservoirs, the EJ limestone reservoir tends to be problematic due to its high degree of heterogeneity. e. porosity, water saturation, Al, and Poisson ratio), especially the data from wells with low porosity values.

Nodes are displayed in gray scales relative to their contribution to outputs, where the darker the node is, the higher the contribution to outputs is. The curves showing network performances indicate the normalized RMS values and the correlation between the values estimated by neural network and the actual values. The smaller the RMS is, the higher reliability resulted from neural network is. Therefore, a strong relationship is indicated between gas colunm height and seismic attributes, suggesting that prediction result is reliable, whereas a weak relationship is indicated between sandstone porosity and seismic attributes, suggesting that prediction result is less reliable in the Pantai Pakam Timur field (Fig.

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