Open AccessA new prediction method of reservoir porosity based on improved Kriging interpolation
Author(s) -
Tongchun Hao,
Liguo Zhong,
Tianyin Zhu,
Xiaocheng Zhang,
Xiaopeng Wang,
Lei Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1707/1/012018
Subject(s) - kriging , petroleum engineering , interpolation (computer graphics) , porosity , geology , reservoir engineering , water injection (oil production) , geostatistics , petroleum reservoir , oil field , drilling , geotechnical engineering , engineering , petroleum , computer science , mathematics , mechanical engineering , telecommunications , paleontology , statistics , frame (networking) , machine learning , spatial variability
In the process of oil and gas field development, the pore characteristics of reservoir are the main factors that affect the reservoir capacity and production efficiency. The physical property parameters and pressure distribution of layers have changed greatly, and the pressure system of multi-pressure layer is formed underground of multi-layer reservoir and heterogeneous sandstone oil reservoir after water injection and chemical injection. Accurate prediction of reservoir physical properties and formation pressure distribution after changes is significance for efficient production and safe operation of reservoirs. In this paper, based on geostatistics theory and percolation mechanics theory, combined with reservoir geology, drilling, development and testing data of Bohai Oilfield, the prediction method of reservoir porosity based on improved Kriging interpolation and effective thickness has been established. This prediction method can provide certain guidance for the formulation of oilfield injection and production distribution plans at the oilfield and the shut-in and adjustment plans for injection-production wells during drilling and completion operations.