Well testing interpretation method based on artificial intelligence
A technology of well test interpretation and artificial intelligence, applied in the field of well test analysis of oil and gas reservoirs, can solve the problems of slow efficiency, interpretation error, cumbersome interpretation process, etc., and achieve the effect of high efficiency, small interpretation error, and simple interpretation process
Inactive Publication Date: 2019-12-20
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology
[0005] The purpose of the present invention is: in order to solve the problems of cumbersome interpretation process, slow efficiency, and certain interpretation errors in the current well test interpretation, the present invention conducts well test analysis based on artificial intelligence, which can be self-adjusting, self-learning, self-association, and optimization work process, improve work efficiency and decision-making quality, avoid the influence of subjective judgment, and realize fully automatic well test interpretation
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[0018] The present invention will be further described below in conjunction with the embodiments and the accompanying drawings.
[0019] The invention provides an artificial intelligence-based well test interpretation method, which includes the following steps: importing measured pressure data and production data, and processing the data based on filtering and denoising, improving and optimizing the wavelet transform algorithm, and analyzing the pressure The derivative is preprocessed to form a continuous smooth diagnostic curve; use Tensorflow to implement a complete convolutional neural network, and use this convolutional neural network to recognize handwritten digit data sets (MNIST); write programs to process training samples, and establish convolutional neural networks based on convolutional neural networks. Network well test interpretation model; improve the genetic algorithm to improve the fitting speed and parameter fitting accuracy, the model adjusts the optimal networ...
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The invention relates to a well testing interpretation method based on artificial intelligence, and belongs to the field of well testing analysis. The method solves the problems that in existing welltesting interpretation, the interpretation process is tedious, the efficiency is too low, and certain interpretation errors exist. The technical scheme of the method is as follows: preprocessing thepressure derivative based on filtering and denoising, programming training samples, establishing a well testing interpretation model based on a convolutional neural network, improving a genetic algorithm to improve fitting speed and parameter fitting accuracy, adjusting an optimal network structure by the model through experiment and theoretical research, identifying a characteristic section of aderivative curve, and intelligently diagnosing the model by the system in combination with flow section characteristics and a data trend of the well testing model. Well testing analysis is carried outbased on artificial intelligence, self-adjustment, self-learning and self-association can be realized, the work flow is optimized, the work efficiency and the decision quality are improved, the influence of subjective judgment is avoided, and full-automatic well testing interpretation is realized.
Description
technical field [0001] The invention relates to an artificial intelligence-based well test interpretation method, which belongs to the field of oil and gas reservoir well test analysis. Background technique [0002] Well testing is an important means of reservoir engineering. It is a method to study the geology of oil and gas reservoirs and the engineering parameters of oil and gas wells based on the theory of oil and gas seepage mechanics and by means of pressure, temperature and production testing. That is to test the wells (oil wells, gas wells and water wells), measure the pressure and production changes of the wells (oil wells, gas wells and water wells) due to the change of the working system, and study the formation parameters and test wells through the analysis of these change processes. Productivity and completion quality, as well as dynamic issues related to oil and gas reservoirs and test wells, to analyze the effect of test well stimulation. [0003] Compared wi...
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Login to View More IPC IPC(8): G06F17/50G06F17/14G06N3/04G06N3/08
CPCG06F17/141G06F17/148G06N3/08G06N3/045
Inventor 杨思涵刘启国
Owner SOUTHWEST PETROLEUM UNIV