The existing method to infer relative permeability from resistivity data was modified by including more parameters such as residual oil saturation. Both oil-water relative permeability and resistivity were measured simultaneously in the same core sample at a room temperature in order to verify the modified model. Altogether 16 core samples in 2 wells from Daqing oil field, China have been tested. The permeability ranged from about 10 to 800 md. The oil-water relative permeability data were measured using a dynamic displacement technique. Oil-water relative permeability data were inferred from the resistivity data measured in the laboratory and logged from the well using the modified model. The model data were then compared to the experimental data. We demonstrated that the relative permeability of both oil and water calculated from the resistivity data measured in the same core samples and logged from the same wells were close to the experimental data measured using a dynamic displacement approach. The modified model had a greater accuracy compared with the existing models. Using the modified model, it would be possible to obtain the different distribution of relative permeability characteristics in different kinds of formations in a reservoir. It may also be feasible to infer relative permeability data while drilling if resistivity well logging is being taken. Introduction Relative permeability is one of the important parameters controlling multiphase fluid flow in porous media. These data are traditionally obtained with experimental measurements. However, relative permeability is expensive, difficult, and time-consuming to measure in the laboratory, especially for the rocks from unconventional oil and gas reservoirs such as shale plays, tight sands, and extremely low permeability reservoirs. It is also difficult to maintain exact reservoir conditions in taking a core or a fluid sample from the reservoir and bringing it to surface and it is almost impossible to conduct the measurements in real time. Consequently, there has been a decades-long research effort to develop methods and procedures to infer relative permeability using network modeling. Recently, the industry has been researching new methods to extract relative permeability in-situ including the utilization of specially designed permanent downhole electric resistivity array, pressure, and flow rate measurements. Relative permeability can also be derived from other parameters such as capillary pressure data. Mahmoud et al. (2013) predicted the capillary pressure from well logging data in carbonate reservoir and sandstone reservoir. Purcell (1949) reported a mathematical model to calculate the relative permeability from capillary pressure data. From then on, many researchers worked on this area. Li (2005, 2007 and 2010), Li and Horne (2006) and Li and Williams (2006) have made a lot of contribution for estimating the relative permeability using resistivity well logging data. Based on the reaserch on the interrelation between capillary pressure, resistivity and relative permeability reported by Li (2010), Alex et al. (2012) considered to modify the model in double porosity systems. They developed a method to calculate relative permeability and caplillary pressure from resistivity well logging data in naturally fractured reservoirs.
Developing a model that can accurately predict internal fractured reservoirs in the context of the ultra-low physical properties of carbonate rocks by only employing conventional mathematical methods can be very challenging. This process is challenging because the relationship between basic fracture parameters and the logging response in carbonate reservoirs has not been studied, and the traditional method lacks adaptability due to the complex relationship between basic fracture parameters and the logging response. However, data-driven approaches supplemented by machine learning algorithms based on multi-layer perceptrons (MLP) provide a more reliable solution to this challenge. In this paper, a classical fracture parameter evaluation data set is established using fracture porosity, fracture density, fracture length, and fracture width data that can be identified by resistivity and acoustic imaging logging. Another data set can be composed of different types of logs, and it can be used to identify reservoirs. Two different data sets were validated by regression task evaluation indicators in machine learning, and the correlation coefficient R2 is greater than 0.82. This means that the model accuracy of the algorithm can reach 82%. Combined with the comparison results of eight conventional machine learning algorithms, the reliability and application validity of the MLP model are verified. This method’s accuracy is also verified by oil test data, which show that the MLP machine-learning algorithm can effectively simulate the relationship between lithology and fracture development. In addition, it can be used to predict key exploration horizons before drilling. The relationship between lithology and fracture development degree is well-simulated by the MLP machine learning algorithm, which shows that the degree of fracture development is mainly affected by fractures, indicating that the method can be used to predict key exploration horizons before drilling.
Here we present an example of using PP and PS converted-waves for characterizing volcanic gas reservoirs in Daqing Oilfield in Northeast China. The volcanic targets are buried at depth raging from 2800m to 3600m, which often give rise to incoherent P-wave response. To overcome this problem, a multicomponent seismic experiment was set up to evaluate the converted-waves recorded by digital MEMS (micro-electro-mechanical system) sensors. The experiment includes six 2D lines, passing through ten boreholes drilled for the volcanic reservoirs. Several multicomponent VSPs have also been acquired for correlation purposes. Analysis the P-and S-waves from the target formation at the ten borehole locations reveals very consistent P-and S-wave amplitude anomalies. From the gas producing wells, the Pwave reflection is consistently weak and scattered, whilst the PS-wave reflection are consistently strong and continuous. In contrast from the non-producing wells, both PP-and PS-waves show continuous and strong reflections. The gas reservoirs can then be delineated from joint PP-and PS-amplitude analysis and the results agree with the drilling results in the study area. This provides conclusive evidence demonstrating the benefit of multicomponent seismic data from digital MEMS sensors.
Oil–water relative permeability is an important parameter that affects fluid flow in porous media. It is usually obtained in a laboratory. Since rock resistivity and relative permeability are both effects of water saturation, they should theoretically have a relationship. Based on the parallel conduction principle of fluid and skeleton in porous media, the pore structure and fluid distribution can be simplified using the Kozeny–Carman permeability correction equation and the Archie formula, and the relative permeability model of the water phase can be deduced under different wetting conditions. In this study, the resistivity and relative permeability experimental data of 20 rock samples from four inspection wells were compared and verified. The results show that the proposed oil–water relative permeability model agrees well with a reservoir having a porosity range of 17.6–30.7% and an air permeability of 0.16–973 × 10−3 μm, and it may explain why the relative permeability of the water phase decreases as water saturation increases. This model could provide a new technique to construct the relative permeability curves of sandstone reservoirs.
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