Abstract

Abstract The Yinggehai Basin has become the main battlefield for natural gas exploration in recent years in China, but the formation pressure system in this area has the characteristics of poor horizontal distribution regularity and severe vertical distribution changes. This paper proposes a new method for predicting the horizontal and vertical distribution trend of formation pressure for the situation of limited offshore drilling data. The method is based on the topological triangulation algorithm, combined with the data set to fit the multivariate interpolation function, and conveniently realizes the prediction and visualization of the three-dimensional horizontal and vertical distribution trend of regional formation pressure.On this basis, through the analysis of the relevant data of the complex accident points that have been drilled, the characteristics of the single well safety density window and the regional safety density window are clarified. The study found that there is no high pressure in the shallow layer of the block, and the formation pressure spreads smoothly in the lateral direction; the high pressure top surface is at 3500m, and the formation pressure gradually increases with the increase in longitude and decrease in latitude in the lateral direction. The formation pressure rises extremely rapidly with the increase of depth in the longitudinal direction, which has the characteristic of "broken line pressurization". The safety density window of a single well is in the shape of a funnel with the characteristic of turning back, and the turning section is between 3500m and 4000m. The safety density window feature of the block can be divided into three stages according to the depth., and the window appears extremely narrow after 3500m (<0.3g/cm3).According to the method proposed in this paper, it is possible to efficiently and conveniently predict and visualize the three-dimensional horizontal and vertical distribution trend of regional formation pressure under the condition of limited formation data during deep-water drilling.

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