Artificial intelligence method and system for calculating geological parameters by utilizing logging-while-drilling data

Abstract

The invention provides an artificial intelligence method and system for calculating geological parameters by utilizing logging-while-drilling data. Big data pre-processing is carried out through a software-defined artificial system; data learning of predictive learning and ensemble learning is included; indicative learning of data action guidance is realized based on a Merton's law; by means of amulti-branch synchronous learning system and method, exploration on logging data over time is solved by utilization of machine predictive learning; exploration on the logging data in terms of spatialdistribution is solved by utilization of ensemble learning; and the generation direction of geological parameters to be calculated is solved by utilization of indicative learning. According to the artificial intelligence method and system provided by the invention, geological parameter calculation is carried out through downhole logging data; therefore, stratigraphic structure parameters and electrical parameters capable of describing the geological occurrence are obtained; the data transmission amount in a drilling process can be greatly reduced; simultaneously, geological information reflected by the logging data is visual and quantitative; and thus, the artificial intelligence method and system have the important significance for geological orientation and logging interpretation.

Description

technical field [0001] The invention relates to the technical field of oilfield development, in particular to an artificial intelligence method and system for calculating geological parameters using logging-while-drilling data. Background technique [0002] With the widespread application of horizontal well and highly deviated well drilling technology, the application of wireline logging is subject to certain restrictions, and logging data acquisition is widely used in the form of measurement while drilling. Due to the short drilling time of resistivity while drilling, the measurement point is less affected by mud invasion, and it can provide logging curves at different detection depths. Resistivity data while drilling can reflect the real parameters of the formation, so logging while drilling tools have broad application prospects. With the continuous deepening of exploration and development of complex oil and gas fields, the wide application of highly deviated wells, hori...

AI Technical Summary

Problems solved by technology

The contradiction is that in the process of geosteering, in order to meet the needs of real-time steering decision-making, it is necessary to use mud pulses for real-time data transmission, and mud pulses are limited by the bandwidth display, which cannot meet the real-time upload of all information

In this context, engineering usually selects a number of data curves that can represent geological parameters for uploading. Due to the limited number of uploaded curves, real-time data can only qualitatively reveal changes in geological parameters and cannot meet the needs of precise geosteering.

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