Summary Heat flow data is essential for understanding lithospheric dynamics. As a petroliferous basin, a large number of boreholes have been drilled during hydrocarbon exploration and production in the northern part of Songliao Basin, Northeast China. Meanwhile, the data on crustal structures, core samples, and formation temperatures have been accumulated, which provide an opportunity for understanding the thermal state of the basin. Based on the temperature data from both Drilling Stem Test and continuous steady-state logging profiles, together with the systematic analysis of the thermal properties of rock samples, we present a new heat flow map of the northern Songliao Basin with significantly increased number of heat flow sites. The northern Songliao Basin is characterized by relatively high geothermal gradients and high heat flow for sedimentary basins. The heat flow values range from 44.4 to 95.0 mW/m2 with an average of 67.2 ± 12.8 mW/m2, and the geothermal gradients range from 21 to 59°C/km with an overall average of 41.7°C/km. Heat production from sedimentary covers accounts for about 4.5 mW/m2 at the site of Well SK-2. Furthermore, based on the crustal structures revealed by previous seismic studies, lithospheric thermal structures are analyzed and compared among different structural units of the basin. A thinned thermal lithosphere with a thickness of ∼65 km is found beneath the Central Downwarp and the Southeast Uplift in which a large part of the heat flow is mantle derived. The Western Slope exhibits a moderate heat flow value and a thicker thermal lithosphere with thickness greater than 110 km. From the perspective of the geothermal state of the lithosphere, the regional geodynamics related to the Mesozoic lithosphere stretching and the subduction of the Pacific Plate are discussed.
Identifying remaining oil distribution is an essential study at the Third District in North Saertu of the West Block in DaqingOilfield. This field is known as a flooding fine potential tapping demonstration zone, characterized by a long-developing history and complex well history. Based on tectonic features and sedimentary characteristics of the study area, the methods of facies controlled reservoir 3D geological modeling and numerical simulation are used in the process of establishing the geological 3D static model. In this paper, we summarized the causes and distribution law of remaining oil in the study area by using the method of fine reservoir numerical simulation to provide a reliable basis for the development and adjustment of the oil field. In combination with fine exploration such as water drive fracturing, water plugging, reperforating and injection-production segment, the recoverable reserves recovery rate could be effectively increased.La identificación de la distribución del remanente de petróleo es un estudio esencial en el tercer distrito del norte de Sartu, que corresponde al bloque occidental del campo petrolífero de Daqing. Este campo es conocido como una zona ejemplar para aprovechar el potencial de explotación por inundación y que se caracteriza por una historia compleja y de largo desarrollo de sus pozos. Con base a las características tectónicas y sedimentarias del área de estudio se utlilizaron los métodos de modelado geológico 3D en depósitos con facies controladas y la simulación numérica en el proceso de establecer el modelo geológico 3D estático. En este artículo se establecen las causas y la ley distributiva del remanente de petróleo en el área de estudio a través del método de simulación numérica de depósitos de alta resolución que provea una base fiable para el desarrollo y ajuste del campo petrolero. Con la combinación de métodos de exploración como la fractura dirigida con agua, taponamiento acuático, reperforación y segmentos de inyección-producción, el índice de recuperación de reservas podría incrementar efectivamente. ABSTRACT RESUMEN
With abundant oil and gas as well as geothermal resources, Songliao Basin is a famous large continental sedimentary basin in China. So far, the research of geothermal resources in North Songliao Basin has mainly concentrated in the shallow layers where low-temperature geothermal resource is mainly developed. However, the research of deep geothermal resource in high-temperature hot dry rock is still in a blank state. Based on the Songliao Basin exploration and research achievements as well as oil exploration well data, this paper further improves geothermal resource exploration and development theories of North Songliao Basin by researching geothermal resources in high-temperature hot dry rock. Hot dry rock distribution is relatively concentrated in the North Songliao Basin, mainly in the central, southern and southeastern part of the study area, where the depth of 150 ℃ isothermal surfaces substantially less than 5km is conducive to the development of hot dry rocks. Studies have shown that two high value areas of terrestrial heat flow in the North Songliao Basin are respectively located in the southern part of the central depression area as well as the junction of three first-order tectonic belts including central depression, southeast and northeast uplift areas, where geothermal resource potential of hot dry rock is great. By using enhanced geothermal system, mining hot dry rock is feasible. This study has some guidance and reference for the development and utilization of deep geothermal resources in hot dry rock of the North Songliao Basin.
Accurate identification of oil and water layers is the basis of qualitative evaluation of reservoir fluid properties or industrial value and selection of testing layers of the well. The traditional oil and water layer identification is mainly based on the extensive use of the well’s logging and logging data, which is inefficient and easy to leak interpretation or misinterpretation for those reservoirs with complex geological conditions. In this paper, the random forest method of machine learning is used to select the lithology, porosity, permeability, movable fluid, oil saturation, S0, S1, S2, Tmax of rock as characteristics; smote oversampling is used to expand the sample, and the packet estimation is used to establish the oil and water layer identification model. This method is simple and easy to use, not prone to severe overfitting, and can find the potential rules in the data. The classification performance is excellent, and the accuracy rate can reach more than 89.9%, which solves the problem of low accuracy in oil-water layer identification in the past.
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