Logging reservoir identification and prediction method based on machine learning

A technology of reservoir identification and machine learning, applied in the field of machine learning, can solve problems such as poor applicability of logging interpretation models, complex pore structure, and strong reservoir heterogeneity

Pending Publication Date: 2021-11-12
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with the traditional well logging interpretation method, when identifying tight gas, low porosity and low permeability reservoir fluids, due

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Logging reservoir identification and prediction method based on machine learning
  • Logging reservoir identification and prediction method based on machine learning
  • Logging reservoir identification and prediction method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0029] Embodiment 1: (taking 28 wells in identifying Li 8-Li 57 as an example)

[0030] A method for identifying and predicting well logging reservoirs based on machine learning, specifically including the following four steps:

[0031]Step S1: Prepare a large number of well logging data samples, build a well logging database, select conventional well logging data and gas test data as input, and mark the data, obtain the data information of existing wells according to the pre-feature selection rules, and select jing , ceng, DEPT, AC, PE, SP, CAL, GR, U, TH, K, AC, CNL, DEN, RLLS, and RLLD were used as characteristic factors, and 2544 pieces of logging data were obtained, including 738 pieces of gas layer, gas and water There are 549 layers, 125 poor gas layers, 747 gas-bearing water layers, 233 water layers, and 152 dry layers, and the data of each layer is marked with the task category number;

[0032] Step S2: According to the collected data set, preprocess the data and nor...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a logging reservoir identification and prediction method based on machine learning. The invention aims at solving the problems that when a conventional well logging interpretation method is used for tight gas low-porosity and low-permeability reservoir fluid recognition, due to the fact that the reservoir is high in heterogeneity and the pore structure is complex, a well logging interpretation model is poor in applicability and low in accuracy, and a traditional single machine learning algorithm is prone to falling into the problems of overfitting, local optimum and the like. According to the invention, the classic ensemble learning algorithm xgboost is adopted, the gas testing logging result and the conventional logging data are combined, the tight sandstone reservoir is recognized, the prediction precision can be effectively improved, the time for testing personnel to judge the reservoir is shortened, and therefore a large amount of cost is saved.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to a method for identifying and predicting well logging reservoirs based on machine learning. Background technique [0002] Integrated learning is a branch direction of machine learning. It mainly forms a strong learner by combining multiple weak learners, and adopts an appropriate combination strategy to synthesize the prediction results of each learner to obtain a more comprehensive and reliable judgment, thereby improving The accuracy of the model's predictions. Compared with a single machine learning algorithm, it is not easy to fall into problems such as overfitting and local optimum. Among them, xgboost is a more classic algorithm for integrated learning. xgboost is based on the idea of ​​GBDT, using first-order and second-order derivative information. It can also be said that it is a kind of GBDT because it is also based on Gradient and Boosting ideas, but The difference from t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q50/02G06N20/20G06N3/04G06N3/08
CPCG06Q10/04G06Q50/02G06N20/20G06N3/088G06N3/045
Inventor 周伟文宏川郑滋觉赵海航邓粤鹏赖富强黄兆辉易军赵怡恒
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap