Reservoir characteristics,formation mechanisms and petroleum exploration potential of volcanic rocks in China

Characterized by complex lithology and strong heterogeneity, volcanic reservoirs in China developed three reservoir space types： primary pores, secondary pores and fractures. The formation of reservoir space went through the cooling and solidification stage （including blast fragmentation, crystallization differentiation and solidification） and the epidiagenesis stage （including metasomatism, filling, weathering and leaching, formation fluid dissolution and tectonism）. Primary pores were formed at the solidification stage, which laid the foundation for the development and transformation of effective reservoirs. Secondary pores were formed at the epidiagenesis stage, with key factors as weathering and leaching, formation fluid dissolution and tectonism. In China, Mesozoic-Cenozoic volcanic rocks developed in the Songliao Basin and Bohai Bay Basin in the east and Late Paleozoic volcanic rocks developed in the Junggar Basin, Santanghu Basin and Ta- rim Basin in the west. There are primary volcanic reser- voirs and secondary volcanic reservoirs in these volcanic rocks, which have good accumulation conditions and great exploration potential.

Factors influencing physical property evolution in sandstone mechanical compaction:the evidence from diagenetic simulation experiments

In order to analyze the factors influencing sandstone mechanical compaction and its physical property evolution during compaction processes, simulation exper- iments on sandstone mechanical compaction were carried out with a self-designed diagenetic simulation system. The experimental materials were modem sediments from dif- ferent sources, and the experiments were conducted under high temperature and high pressure. Results of the exper- iments show a binary function relation between primary porosity and mean size as well as sorting. With increasing overburden pressure during mechanical compaction, the evolution of porosity and permeability can be divided into rapid compaction at an early stage and slow compaction at a late stage, and the dividing pressure value of the two stages is about 12 MPa and the corresponding depth is about 600 m. In the slow compaction stage, there is a good exponential relationship between porosity and overburden pressure, while a good power function relationship exists between permeability and overburden pressure. There is also a good exponential relationship between porosity and permeability. The influence of particle size on sandstone mechanical compaction is mainly reflected in the slowcompaction stage, and the influence of sorting is mainly reflected in the rapid compaction stage. Abnormally high pressure effectively inhibits sandstone mechanical com- paction, and its control on sandstone mechanical com- paction is stronger than that of particle size and sorting. The influence of burial time on sandstone mechanical compaction is mainly in the slow compaction stage, and the porosity reduction caused by compaction is mainly con- trolled by average particle size.

Genetic mechanisms of secondary pore development zones of Es_4~x in the north zone of the Minfeng Sag in the Dongying Depression,East China

The genetic mechanisms of the secondary pore development zones in the lower part of the fourth member of the Shahejie Formation（Es_4／6x） were studied based on core observations,petrographic analysis,fluid inclusion analysis,and petrophysical measurements along with knowledge of the tectonic evolution history,organic matter thermal evolution,and hydrocarbon accumulation history.Two secondary pore development zones exist in Es_4~x,the depths of which range from 4200 to 4500 m and from 4700 to 4900 m,respectively.The reservoirs in these zones mainly consist of conglomerate in the middle fan braided channels of nearshore subaqueous fans,and the secondary pores in these reservoirs primarily originated from the dissolution of feldspars and carbonate cements.The reservoirs experienced ‘‘alkaline–acidic–alkaline–acidic–weak acidic＇＇,‘‘normal pressure–overpressure–normal pressure＇＇,and‘‘formation temperature increasing–decreasing–increasing＇＇ diagenetic environments.The diagenetic evolution sequences were ‘‘compaction/gypsum cementation/halite cementation/pyrite cementation/siderite cementation–feldspar dissolution/quartz overgrowth–carbonate cementation/quartz dissolution/feldspar overgrowth–carbonate dissolution/feldspar dissolution/quartz overgrowth–pyrite cementation and asphalt filling＇＇.Many secondary pores（fewer than the number of primary pores） were formed by feldspar dissolution during early acidic geochemical systems with organic acid when the burial depth of the reservoirs was relatively shallow.Subsequently,the pore spaces wereslightly changed because of protection from early hydrocarbon charging and fluid overpressure during deep burial.Finally,the present secondary pore development zones were formed when many primary pores were filled by asphalt and pyrite from oil cracking in deeply buried paleoreservoirs.

Neural decoding based on probabilistic neural network

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.