Multi-well big data guided drilling parameter optimization method and device and storage medium

By combining fuzzy C-means clustering and multi-objective optimization algorithms with a mechanical drilling rate prediction model, geological units are identified and drilling parameters are optimized. This solves the problem of insufficient adaptability of drilling parameters in traditional methods and achieves a more efficient and safer drilling process.

CN122113601APending Publication Date: 2026-05-29CHINA UNIV OF PETROLEUM (BEIJING)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNIV OF PETROLEUM (BEIJING)
Filing Date
2026-02-04
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Traditional drilling parameter optimization methods rely on manual experience or big data statistical analysis, failing to effectively utilize data from adjacent drilled wells. This results in insufficient adaptability and poor robustness of drilling parameters under complex geological conditions.

Method used

By acquiring prior information and real-time drilling data of the target area, a fuzzy C-means clustering algorithm is used to identify geological units. Combined with a mechanical drilling rate prediction model and a mechanical specific energy calculation formula, a multi-objective optimization algorithm is used to select the optimal combination of drilling parameters.

Benefits of technology

This enhances the adaptability and robustness of drilling parameter optimization to unseen formations, thereby improving drilling efficiency and safety.

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Abstract

The application provides a multi-well big data guided drilling parameter optimization method and device and a storage medium, and belongs to the technical field of oil and gas exploration. The method comprises the following steps: acquiring prior information of drilled wells in a target area; acquiring real-time drilling data of a target well in a drilling process; determining a geological unit division standard according to the prior information; identifying a real-time geological unit corresponding to the target well at the current moment based on the real-time drilling data, the prior information and the geological unit division standard by using a fuzzy C-means clustering algorithm; determining a plurality of groups of candidate drilling parameters in the real-time drilling data and historical logging data matched with the real-time geological unit; respectively determining mechanical drilling speed and mechanical specific energy corresponding to each group of candidate drilling parameters by using a mechanical drilling speed prediction model and a mechanical specific energy calculation formula; and filtering out a target drilling parameter combination from the plurality of groups of candidate drilling parameters by using a multi-objective optimization algorithm, and executing the target drilling parameter combination.
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