Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation

With the high-speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. This article presents a workflow analysing the main controlling factors of oil saturation variation utilizing machine learning algorithms based on static and dynamic data from actual reservoirs. The dataset in this study generated from 468 wells includes thickness, permeability, porosity, net-to-gross (NTG) ratio, oil production variation (OPV), water production variation (WPV), water cut variation (WCV), neighbouring liquid production variation (NLPV), neighbouring water injection variation (NWIV), and oil saturation variation (OSV). A data processing workflow has been implemented to replace outliers and to increase model accuracy. A total of 10 machine learning algorithms are tested and compared in the dataset. Random forest (RF) and gradient boosting (GBT) are optimal and selected to conduct quantitative analysis of the main controlling factors. Analysis results show that NWIV is the variable with the highest degree of impact on OSV; impact factor is 0.276. Optimization measures are proposed for the development of this kind of sandstone reservoir based on main controlling factor analysis. This study proposes a reference case for oil saturation quantitative analysis based on machine learning methods that will help reservoir engineers make better decision.

The Characteristics and Main Controlling Factors for the Formation of Micropores in Shale from the Niutitang Formation, Wenshuicun Section, Southwest China

The characteristics of shale micro-pore development and its main influencing factors have important theoretical guiding significance for shale gas exploration and resource evaluation. In order to clarify the micro-pore development characteristics of lower Cambrian shale and the main controlling factors of micro-pore development, we used the lower Cambrian Niutitang formation shale, in the Wenshuicun section of the Guizhou Province in southwest China. The micro-pore development characteristics of the shale in the region were studied by argon ion profile field emission scanning electron microscopy and a low-temperature liquid nitrogen adsorption and desorption experimental system. The relationship between micro-pore and kerogen maceral composition, total organic carbon (TOC) content and different mineral content was analyzed in combination with mineral and geochemical characteristics. Inorganic pores (clay mineral pores, dissolution pores and pyrite intergranular pores) and micro-fractures (clay mineral shrinkage crack, tectonic fractures and overpressure fractures) were the main type of pore developed in the shale of the Niutitang formation in the Wenshuicun section, and no organic pores had developed. The pore size of shale is usually 2–50 nm, accounting for 58.33% of shale pores, e.g. mesopores. Clay mineral content has an obvious positive correlation with macropore volume and average pore diameter, and an obvious negative correlation with micropore volume. In addition, the content of feldspar in brittle minerals has a strong negative correlation with macropore volume and average pore diameter, and a strong positive correlation with micropore volume and BET-specific surface area. TOC content and the content of different kerogen macerals have no obvious correlation with the development of shale micropores in this region. It is concluded that inorganic mineral composition is the main controlling factor of micro-pore development within lower Cambrian shale, and organic matter abundance and maceral content have little influence on the micro-pore development. This study provides a case study for the characteristics of micropores in lower Cambrian shale in China.

Sensitivity Analysis of Anchored Slopes under Water Level Fluctuations: A Case Study of Cangjiang Bridge—Yingpan Slope in China

This paper presents an improved slope stability sensitivity analysis (ISSSA) model that takes anchoring factors into consideration in umbrella-anchored sand and clay slopes under reservoir water level fluctuation. The results of the ISSSA model show that the slope inclination and the layout density of anchors are the main controlling factors for sand slope stability under fluctuation of the water level, while the slope inclination and water head height are the main controlling factors for slope stability in the Cangjiang bridge—Yingpan slope of Yunnan province in China. Moreover, there is an optimum anchorage angle, in the range of 25–45 degrees, which has the greatest influence on slope stability. The fluctuation of the reservoir water level is an important factor that triggers slope instability; in particular, a sudden drop in the surface water level can easily lead to landslides; therefore, corresponding measures should be implemented in a timely manner in order to mitigate landslide disasters.