Dense clastic rock natural gas well productivity prediction method based on reservoir classification

Abstract

The invention discloses a dense clastic rock natural gas well productivity prediction method based on reservoir classification. The method includes the steps that the effective thickness of each reservoir and the effective thickness of each thin interbed are acquired according to logging information; the shale content of each reservoir is calculated according to a curve of natural gamma and three porosity; the porosity of each reservoir is calculated according to a curve of sound waves, neutrons and density, and the reservoirs are classified; the permeability and gas saturation of the reservoirs are calculated according to core analysis pore permeability and an Archie equation; reservoir classification calculation and summing are conducted through thickness weighting, and then a comprehensive gas index is acquired; the conception of the thin interbeds is introduced according to the differences of reservoir depositional microfacies, and then the improved comprehensive gas index is acquired; the relation between the improved comprehensive gas index and test productivity is built, and then the productivity of other reservoirs is predicted with the relation as a model. On the basis of ensuring simple operation, the method has high reservoir productivity prediction accuracy.

Description

technical field [0001] The invention belongs to the technical field of petroleum geological exploration, in particular to a method for predicting the production capacity of tight clastic rock gas wells. Background technique [0002] Productivity prediction of natural gas wells is a technology for comprehensive evaluation of oil and gas production capacity of reservoirs, which is of great significance to the exploration and development of oil and gas fields. Planning provides an important scientific basis. [0003] At present, there are mainly two methods for production capacity prediction: one is to establish a prediction model by simply fitting multiple reservoir parameters with actual production capacity, although this method is simple and quick, but the accuracy is low; Technology, establish a reservoir classification model, and establish the relationship between the reservoir index and production capacity according to the proportion of different types of reservoirs, so ...

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Problems solved by technology

[0003] At present, there are mainly two methods for production capacity prediction: one is to establish a prediction model by simply fitting multiple reservoir parameters with actual production capacity, although this method is simple and quick, but the accuracy is low; technology, establish a reservoir classification model, and establish the relationship between reservoir index and production capacity according to the proportion of different types of reservoirs, so as to predict production capacity

This method has a significant application effect only when there are enough learning samples, and there are few sample points of oil and gas reservoirs in the early stage of exploration, the accuracy of this neural network prediction method is low, and the operation of this method is relatively complicated. Unable to provide timely and effective forecast data results

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