No abstract

No abstract

Traditional methods of multivariate statistical analyses, used to recognize anomalous hydrocarbon signatures in petroleum exploration, have at least four shortcomings: (1) it is difficult to isolate anomalies where the data are not normally distributed; (2) it is difficult to separate distinct anomaly populations corresponding to distinct formation mechanisms while separating anomalies from background; (3) it is not fitting to present illustrations of multivariate anomalies on contour maps; and (4) it is not suitable for preparation for multivariate pattern recognition. These present serious obstacles to the application of exploration geochemistry in hydrocarbon exploration. This study demonstrates that the Back Propagation Artificial Neural Network (BP-ANN) with logic multiplication cluster analysis (a new cluster analysis proposed in this paper) overcomes the difficulties exhibited by traditional multivariate methods. The logic multiplication cluster analysis was designed to produce a training set for the BP-ANN. This approach was established on the basis of geochemical characteristics and origin of the various populations in geochemical data, such as background, micro-seepage anomalies and seepage anomalies. The topology of the BP-ANN was optimized using the outputs of the BP-ANN and the correct rate. In order to illustrate the multivariate anomalies recognized using BP-ANN on contour maps, we designed the expression functions for BP-ANN application in this field. With traditional methods of anomaly recognition, acidextractable hydrocarbons in soils have not proven to be efficient indicators for hydrocarbon potential in East Anan Sag, Inner Mongolia. However, the BP-ANN has indicated that these indicators are efficient and that areas of East Anan Sag have potential reserves of petroleum.

No abstract

Mercury intrusion capillary pressure (MICP), nuclear magnetic resonance (NMR), routine core analysis, thin sections, and scanning electron microscope (SEM) analysis were used to gain insight into the pore structure of the Eocene Sha-3 (the third member of the Shahejie formation) low-permeability sandstones in the Raoyang sag, including pore type, pore geometry, and pore size. Quantitative NMR parameters and petrophysical properties were integrated to build up the relationship between microscopic pore structure and macroscopic performance. The pore systems of Sha-3 sandstones are dominantly of residual intergranular pores, intragranular dissolution pores, and intercrystallite micropores associated with authigenic clay minerals. The high threshold pressure and low mercury withdrawal efficiencies from MICP analysis indicate the poor pore connectivity and strong heterogeneous. Both uni-and bimodal transverse relaxation time (T 2 ) spectrum can be found because of the coexistence of small and large pores, and the T 2 of major pore size occurring at about 1.0 to 100 ms. The Sha-3 sandstones have a relatively high irreducible water content and short T 2 components in the T 2 range. Long T 2 components can only be observed in samples rich in large pores or microfractures. T 2gm (the geometric mean of the T 2 distribution) correlates well with irreducible water saturation and permeability. A methodology for pore structure classification is presented integrating NMR parameters of T 2gm , bulk volume of immovable fluid (BVI), and petrophysical parameters such as reservoir quality index (RQI) and permeability. Consequently, four types of pore structures (types A, B, C, and D) are identified, and characteristics of individual pore structure are summarized. The comprehensive analysis of NMR measurements combined with thin sections, SEM and MICP analysis is useful for describing microscopic pore structure, which is important to maintaining and enhancing petroleum recovery in low-permeability sandstone reservoirs.