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Method for sedimentary microfacies logging recognition based on support vector machine

A technology of support vector machine and depositional microfacies, which is applied in the field of logging identification of depositional microfacies based on support vector machine, which can solve the problems of many constraints, inability to meet the requirements of high-precision quantitative identification, and poor applicability.

Inactive Publication Date: 2017-03-29
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a logging identification method for sedimentary microfacies based on support vector machines, aiming to solve the problem of using logging data to identify sedimentary microfacies with many restrictions, poor applicability, and inability to meet the requirements of high-precision quantitative identification The problem

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  • Method for sedimentary microfacies logging recognition based on support vector machine
  • Method for sedimentary microfacies logging recognition based on support vector machine
  • Method for sedimentary microfacies logging recognition based on support vector machine

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[0066] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0067] The present invention is based on the machine learning method, and the SEA technology (Sediment Microfacies Automatic Recognition Method) is a machine learning method based on the VC dimension theory of statistical learning theory and the principle of structural risk minimization. It shows many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems, realizes automatic classification and identification of logging facies in uncored well intervals, and thus achieves automatic interpretation of sedimentary microfacies, which can be used for oilfield exploration The producti...

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Abstract

The invention discloses a method for sedimentary microfacies logging recognition based on a support vector machine and relates to the technical field of exploration and development. The novel method which is based on an SVM (support vector machine) algorithm and applies logging materials to conduct sedimentary microfacies quantitative recognition is characterized in that each sedimentary microfacies can be represented by a group of logging curve characteristic parameters, samples of a microfacies type are mapped into a higher-dimensional space through nonlinear conversion, and optimal classification faces between each microfacies are sought in the high-dimensional characteristic space; and finally, a decision making function is constructed, so that microfacies types of unknown samples can be determined, and the sedimentary microfacies judgment can be achieved. According to the invention, an SEA technology (sedimentary microfacies automatic recognition method) is used for sedimentary microfacies automatic recognition of a clastic rock reservoir, and judgment accuracy is high; and a reliable judgment basis is provided for optimal selection of beneficial target reservoirs in oil-gas exploration and development, so exploration cost is reduced, and values of application and promotion are high.

Description

technical field [0001] The invention belongs to the technical field of exploration and development, in particular to a method for identifying sedimentary microfacies logging based on a support vector machine. Background technique [0002] Pirson was the first to systematically sort out the geological application of well logging data. Its core is to use the well logging data for the study of sedimentology in oil areas, and then describe the oil and gas reservoirs. Logging facies research is the key to interpret sedimentology with continuous logging data after establishing a forward modeling model through comprehensive analysis of sedimentary facies and logging data under the core scale. At present, with the development of logging methods and interpretation methods, the study of sedimentary microfacies using well logging data has gradually developed towards high precision, automation and intelligence. Complex geological phenomena cannot be clearly described by classical mathe...

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Application Information

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IPC IPC(8): E21B49/00E21B47/00G06K9/62
CPCE21B47/00E21B49/00G06F18/2411
Inventor 彭军王大海夏青松李斌
Owner SOUTHWEST PETROLEUM UNIV
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