Carbonate rock reservoir logging identification method based on empirical mode decomposition and energy entropy discrimination

Abstract

The invention discloses a carbonate rock reservoir logging identification method based on empirical mode decomposition and energy entropy discrimination. According to outcrop and rock core information, carbonate rock reservoirs are divided into three types, i.e., karst type reservoirs, karst fracture-cave composite type reservoirs and micro-pore type reservoirs, therefore making clear microscopic and macroscopic characteristics of different reservoirs. Single-well reservoirs are calibrated by means of rock core information, imaging logging and array acoustic logging information, and typical logging response characteristics of different reservoirs are qualitatively described. An automatic reservoir identification method based on logging information empirical mode decomposition and energy entropy discriminant analysis is proposed, thereby effectively improving the reservoir identification accuracy rate. The method gets rid of uncertainty of traditional mono-stratum evaluation, relatively meticulously extracts the change law of inter-stratum logging response, and effectively solving the problem that it is difficult to identify the reservoir type during carbonate rock petroleum reservoir exploitation.

Description

technical field [0001] The invention relates to a logging method, in particular to a logging identification method for carbonate reservoirs based on empirical mode decomposition and energy entropy discrimination. Background technique [0002] Carbonate oil and gas reservoirs are widely developed in the world, and oil and gas production has accounted for 60% of the world's total oil and gas production. However, carbonate oil and gas reservoirs have strong heterogeneity, various types of storage spaces, and great differences in different regions and layers. It is outside the preferred zone of previous oil and gas exploration and development, and oil-bearing in micro-pores and micro-fractures controlled by primary and secondary causes is also a hot spot in tight oil research in recent years. How to accurately identify effective reservoir types at different scales is the basis for later exploration and development. At present, scholars at home and abroad mainly use conventiona...

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Problems solved by technology

At present, scholars at home and abroad mainly use conventional logging data through multifractal clustering, logging optimization processing, adaptive effective element method, curved edge unit modeling technology, multifractal theory, two-dimensional effective element numerical simulation, and dynamic fuzzy discrimination of entropy function. Algorithms such as Algorithms have been used to study the reservoir identification and quantitative characterization of carbonate rocks, but the accuracy of conventional logging data is limited, and the single-layer calculation error is relatively large

With the continuous improvement of detection methods, the use of imaging logging, Stoneley wave logging, remote detection logging, CT scanning 3D imaging, nuclear magnetic resonance logging, etc. to visually identify and calibrate reservoirs and their fluids has become the focus of logging research. new direction, but the new logging method has higher costs, is more difficult to popularize, and is more difficult to apply in practice

In addition, scholars have also calibrated the logging data by simulating the response characteristics of fractured-cavity reservoirs. However, the numerical simulation is ideal due to the constraints of the model conditions and cannot meet the complex actual geological needs.

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