Oil and gas dessert prediction method and storage medium

Abstract

The invention discloses an oil and gas dessert prediction method and a storage medium, and relates to the oil gas exploration field. The method comprises the following steps: obtaining logging data and various earthquake attributes of an oil gas dessert area; obtaining an oil and gas dessert area indication curve according to the logging data, and using the indication curve and various earthquakeattributes as machine learning samples; optimizing features of the learning samples; carrying out machine learning according to optimized learning samples, thus obtaining a prediction model; predicting oil gas desserts in a random target area according to the prediction model. The invention provides the oil and gas dessert prediction method and the storage medium, aims to improve the prediction accuracy, and can improve the prediction resolution, thus quantitatively predicting oil gas desserts, preventing artificial factor influences, and simplifying a calculation process; the method can obtain high resolution and high precision prediction results without making complex redundant calculations.

Description

technical field [0001] The invention relates to the field of oil and gas exploration, in particular to a method for predicting oil and gas sweet spots and a storage medium. Background technique [0002] In the process of oil and gas resource evaluation and exploration and development, in order to accurately locate oil and gas accumulation areas, describe oil and gas reservoir quality, and evaluate oil and gas resource reserves, it is necessary to carry out accurate reservoir evaluation and reservoir description, and determine the location of oil and gas sweet spots. [0003] At present, the existing oil and gas sweet spots prediction and evaluation methods usually use qualitative or semi-quantitative analysis methods when evaluating oil and gas sweet spots, which require manual participation in the prediction, and are prone to uncertainty due to human factors such as the technical experience and professional ability of the interpreters. Generally, there are problems such as ...

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Problems solved by technology

[0003] At present, the existing oil and gas sweet spots prediction and evaluation methods usually use qualitative or semi-quantitative analysis methods when evaluating oil and gas sweet spots, which require manual participation in prediction, and are prone to uncertainty due to human factors such as technical experience and professional ability of interpreters. Influence, generally have problems such as low resolution and low precision

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