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The micro distribution of residual oil in low-permeability sandstone reservoirs is closely related to pore structure, and the differences in pore structure often determine the reservoir's productivity and development effectiveness from a macro perspective. On the basis of in-depth research, this paper analyzes the distribution law of the remaining microscopic oil, establishes the digital core multi-stage pore network modeling of the strongly sorted heterogeneous conglomerate reservoir in the Lower Wuerhe Formation of Block 8 of the Karamay Oilfield, the three-phase seepage simulation method considering the release of dissolved gas, and the three-phase permeability curve test. The research results are as follows: (1) Conventional physical property analysis shows that the permeability of core samples exhibits an inverse rhythmic distribution with layer depth. (2) CT core analysis and mercury injection experiments indicate that the area with porosity ranging from 9% to 21% accounts for 79% and is the main seepage channel area. Larger pores play an important role in seepage. (3) Through comparative experiments on cores with different permeability, it was found that the degassing phenomenon of low-permeability rock samples is more severe. In the actual process of reservoir development, it is necessary to reasonably handle the impact of water injection on development effectiveness, select appropriate water injection methods and cycles, and avoid premature water breakthrough in ultra low-permeability reservoirs. [ABSTRACT FROM AUTHOR]

Pore structure impacts the capability of seepage pattern of subsurface fluid mineral and mineral exploitation efficiency. Because of the strong heterogeneity in carbonate reservoirs, the pore structure is nonlinearly varying and complex in reservoirs. It is necessary to establish a method for pore structure type (PST) prediction. Machine learning provides an efficient prediction method by finding the relationship between core test data and well‐logging data. In this paper, a reservoir identification method based on Gradient Boost Decision Trees (GBDT) is proposed for the pore structure characteristics of carbonate reservoirs integrating core test and logging data. Core testing data are utilized to form the training set for PST prediction. Then, we adopt the mutual information method to optimize the logging data as the input of the machine learning model. GBDT hyperparameters are optimized using the learning curve to make it have optimal performance. Finally, we compare the predicted result of GBDT with K‐Nearest Neighbor and support vector machines, demonstrating the superior performance of GBDT. The results indicate that the four PS are different in some properties, such as permeability, pore radius and shape of pore and throat. 20%, 10 and 0.15 are recommended as the optimal parameter values for Maximum feature, iteration number and Sub‐sample number to improve GBDT accuracy in carbonate reservoir PST prediction. Evaluation indicators of machine learning, including the Kappa coefficient, Heming distance, Jaccard similarity coefficient and Confusion matrices, indicate that GBDT has better performance. This research proves the high accuracy and applicability of machine learning technology in the carbonate reservoir and also provides a meaningful reference for the identification of the carbonate reservoirs in the central area of Iraq. [ABSTRACT FROM AUTHOR]

The Neogene Guantao Formation Reservoir in the PLOilfield is a unconsolidated sandstone with high porosity and high permeability. The reservoir diagenesis is weak and dominated by compaction diagenesis. At present, insufficient research into the relationship between the pore characteristics and sedimentary microfacies of shallow delta sandstone reservoirs restricts the prediction of favorable reservoir distribution. This article takes the unconsolidated sandstone reservoir of the Guantao Formation as the research object and analyzes the potential coupling relationship between pore characteristics and sedimentary microfacies. In this study, seven typical sedimentary microfacies were identified and the microscopic characteristics of different sedimentary microfacies reservoirs are described. The results show that the pore structure of various sedimentary microfacies is comprehensively influenced by the sedimentary rock texture, siliceous minerals, and clay mineral distribution. Characterized by more abundant hard quartz and feldspar minerals, positive skewness, and lower pore sorting coefficients, the arenaceous microfacies possess larger pore throat radius and lower pore fractal dimensions than argillaceous microfacies. Finally, due to the difficulty of coring in offshore oilfields and the lack of data on mercury injection experiments, empirical formulas for the porosity, permeability, and pore throat radius of conventional core tests were established, and the pore radius that corresponded to 35% mercury saturation (r35) was used as the characteristic pore structure parameter. The grey correlation method was used to analyze the influence of sedimentary structure factors and mineral content differences on r35 and to determine the main controlling factors. [ABSTRACT FROM AUTHOR]

There are various sedimentary structures developed in the black shales of the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation in Luzhou area, southern Sichuan Basin, southwestern China. In order to evaluate the controlling effects of sedimentary structures on the key parameters of shale reservoir properties, collected classification of sedimentary structures, scanning electron microscope observation, analysis of shale fabric and porosity characteristics, low‐pressure nitrogen adsorption, methane isothermal adsorption, and conventional triaxial compression test were carried out based on shale samples from Wells A‐H1, B‐H1, C‐H1 and D‐H1 in the study area. The sedimentary structures recorded in these shales are categorized into five types: homogeneous bedding, weak horizontal bedding, strong horizontal bedding, discontinuous ripple bedding, and gravel‐bearing massive bedding. In general, the palaeo‐hydrodynamics and oxygen content of water columns were weak when homogeneous bedding shale was deposited. The organic matter is the most abundant and the total organic carbon content is up to 4.01% on average. The average quartz content is 43.5%, and the brittleness index is the highest. The porosity and adsorption capacity were the highest, with an average of 4.57% and 79.5 scf/ton, respectively. Combined with its mechanical properties of low Poisson's ratio and high Young's modulus, homogeneous bedding sedimentary structure is associated with the shale units possessing the highest organic matter abundance, the strongest reservoir property, and the best fracability, which is the priority target for exploration and development of the Wufeng to lower Longmaxi Formation in the Sichuan Basin, southwestern China. [ABSTRACT FROM AUTHOR]

Due to the diversity of pore types, it is challenging to characterize the Middle East's Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method and r apex can characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability, r apex , flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery. [ABSTRACT FROM AUTHOR]

Particle migration in oil and gas reservoirs is a common phenomenon in the process of oil and gas development, and is considered to be an important reason for the damage of reservoir permeability and the reduction of oil and gas productivity. The mechanism of this phenomenon includes the desorption, migration, and precipitation of particles, which eventually clogs the throat and causes reservoir damage. Therefore, it is necessary to accurately characterize the complex mechanism of particle migration and identify the main controlling factors of particle migration, which is very important for efficient oilfield development and plugging solution. First, the reservoir types are divided into three types and the pore structure models of different types of reservoirs are established. Then, computational fluid dynamics and discrete element coupling method numerical simulation and microscopic visualization of pore throat structure model were combined, to characterize the rules of particles and migration, and analyze the main controlling factors. Finally, a typical model of particle migration and clogging is established. The results show that particle size/throat and particle concentration are the key factors affecting particle plugging, and particle migration has the least effect on the permeability of Type I reservoir and the greatest damage to Type III reservoir. According to the mechanical and hydrodynamic behavior of particles in porous media, three mechanisms and six modes of particle plugging are proposed. [ABSTRACT FROM AUTHOR]