In oil and gas exploration of Block K in Amu Darya basin Uzbekistan, the reservoir lithologies are mainly in different carbonate rocks, the more types of rocks, the more various reservoir space is, as a result, it brings some difficulties to the reservoir quantitative evaluation. Therefore, according to this situation that the difficulty in identification of complex carbonate lithologies is, in this study block, artificial neural network analysis method is used in this paper. The method combines mud logging, cutting, core data, well logging, studies logging response characteristics of the different types of carbonate rocks, establishes lithology identification index. In this study, the method is used in identifying the types of carbonate rocks, the identified result compared to actual rocks displays about 70.51~87.23%, and it plays the positive role for reservoir quantitative evaluation.
A probabilistic method named discovery process modeling is described for estimating the quantity of undiscovered oil and gas resources in Aral sea area in the North Ustrurt basin. In this model, the pool size distribution was demonstrated, and the numbers and sizes of undiscovered pools were estimated. The most likely remaining plays potential in Area sea area is 3447.2 Billions of standard cubic meters of gas in place. The eastern Jurassic-Cretaceous play bears 2901.5 Billions of standard cubic meters of undiscovered gas in place, and 17 gas pools are yet to be discovered; the paleogene-Neogene play bears 545.7 Billions of standard cubic meters of undiscovered gas in place, and 13 gas pools are yet to be discovered. Based on resources analysis, the Aral sea area is a prospecting exploration area for gas, and the emphasis should be strengthened on the eastern Jurassic-Cretaceous play.
In waveform classification for which abundant seismic data are fully used, neural network algorithm is applied to compare and classify the actual seismic waveforms by traces for one specific formation, so as to delineate the lateral variation of seismic signal in details and thus acquire the seismic facies maps corresponding to geologic characteristics. Moreover, through analysis of drilling data, logging data and depositional facies, the depositional facies belts are further divided for formation and lithologic reservoir prediction. Carbonate reservoir in the Central Block in the east margin of Pre-Caspian Basin is discussed as an example to introduce the application of waveform classification and depositional facies demarcation in the Carboniferous Carbonate reservoir. Favorable reservoir beds are also predicted, contributing to a big breakthrough for risk exploration in this area.
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