This paper presents a new approach to well interpolation using interpolation neural networks (INETs). Traditional methods such as geostatistics have been applied to the spatial mapping of reservoirs. However, these methods are not able to make use of qualitative information, such as previously‐constructed “expert knowledge ” in the form of iso‐porosity contours or structural maps of sand body geometry, in a simple manner.
This paper demonstrates the usefulness of INET via a case study of a fluvial sandstone reservoir at an oilfield in the Asia Pacific region. The proposed method is applied to porosity interpolation based on data from spatially‐dispersed wells and regional geological knowledge. The results from this study show that an INET is not only able to incorporate expert advice, but also that it is easy to implement in a desktop computer or workstation. This allows an effective transfer of geological knowledge to reservoir modelling.
In view of the recent oil and gas dicoveries in the Bombay Offshore basin, its detailed geology has been worked out in terms of its source rock and reservoir potential. An agewise, layer‐cake lithofacies analysis and five depositional model maps from Paleocene to Middle Miocene age, together with a number of paleotectonic sections has led to the reconstruction of a xeneralized depositional model of the basin as a whole. This proposed depositional model envisages the Bombay Offshore basin as a shelf‐to‐basin carbonate model. During Paleogene and Early Neogene time the clastic supply by the proto‐Narmada river from the NE resulted in delta progradation up to the Dahanu depression, which was a region of pro‐deltas and lagoons with a considerable thickness of finer clastics. Beyond the Dahanu depression, the Bombay High and Bassein‐Alibag‐Ratnagiri shelf remained open carbonate platforms, while the DCS area was the locale of shelf‐edge carbonate build‐up.
The potential targets for exploration are the deltaic sandstone to the north in the Tapti area—particularly where the sandy facies interfingers with the shales and the porous limestone horizons in the Bombay Platform, Bassein‐Alibag‐Ratnagiri shelf and the DCS trend. The Dahanu depression is thought to be the main source rock area.
This paper presents an overview of soft computing techniques for reservoir characterization. The key techniques include neurocomputing, fuzzy logic and evolutionary computing. A number of documented studies show that these intelligent techniques are good candidates for seismic data processing and characterization, well logging, reservoir mapping and engineering. Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty.
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