Effective fusion of multiple data, including geographical, geological, geophysical, geochemical and dynamic data for hydrocarbon potential mapping, involves both a fusion algorithm and a convenient modelling platform. In this study, fuzzy logic and a geographical information system (GIS) are used to fuse geological and geophysical interpretations in mapping the gas potential of the Kazakhstan Marsel Territory Carboniferous system based on the assumed gas-accumulation model. Non-linear membership functions are used to transform the input data, while the gamma operator is used to combine the multiple datasets. Finally, the Carboniferous system targets, the Visean (C 1 v) and Serpukhovian (C 1 sr) units, are mapped. Gas testing in situ validated our results.

Symbols are considered as the language of a map; hence, accurate understanding of the meaning of symbols is crucial when obtaining geographical information from a map: the symbolisation of spatial data is of key importance in cartography. A geographical information system (GIS) provides a convenient mapping platform and powerful functions for spatial data symbolisation, while the presence of various mapping standards impedes the understanding of maps and sharing of map information. On the other hand, the available GIS platforms find it difficult to deal with automatic conversion between maps and different mapping standards. To resolve this problem, an approach for symbol recognition and automatic conversion is proposed, and a conversion system based on the approach and the ArcGIS Engine platform is developed to realise automatic conversion between maps produced based on different mapping standards. To test these conversion effects of the proposed system, the petroleum sector is chosen as the research field and the mutual conversion of a map in practical work among the three mapping standards (i.e. the Chinese Petroleum, Shell and USGS standards) governing this field is taken as a case study. The results show that the conversion system has a high conversion accuracy and strong applicability.

No abstract

No abstract

No abstract