Real-Time ROP Prediction via Context-Specific ML Models
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Solution Overview
Problem
Determining optimal drilling parameters to maximize the rate of penetration (ROP) during well drilling operations is challenging due to the numerous operational and physical variables involved, making it difficult to efficiently drill wellbores and reduce drilling time and costs.
Innovation Solution
The development of context-specific predictive models using raw data sets from drilling operations, which include pre-processing, feature extraction, and training algorithms to predict ROP based on drilling parameters and operating conditions, allowing for real-time adjustments to optimize drilling performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If drilling parameters are adjusted to maximize ROP, then drilling speed increases, but determining optimal parameters becomes more difficult due to numerous variables
Solution Approach 1:
The patent replaces complex manual analysis and trial-and-error methods with a machine learning-based predictive model that automatically processes drilling data and recommends optimal parameters, substituting human cognitive effort with an automated computational system
Solution Approach 2:
The system creates a virtual model of the drilling process using historical data and machine learning algorithms that replicates the complex relationships between drilling parameters and ROP, allowing optimization without physical experimentation
2Measurement precision
If more data processing and model training are performed, then prediction accuracy improves, but computational time and resources increase
Solution Approach 1:
The system performs preliminary data processing, feature extraction, and model training during periods when real-time optimization is not critical, preparing predictive models in advance so that when optimization is needed, pre-computed models can be quickly applied
Solution Approach 2:
The patent divides the data processing and model training into distinct stages: data collection, preprocessing, feature extraction, model training, and validation. This segmentation allows parallel processing and optimization of each stage independently, reducing overall computational time
Data Source
AI summary
An example method includes receiving raw data sets containing drilling parameter and operating condition values generated during subterranean drilling operations. The raw data sets may be separated into training data sets based, at least in part, on the types of the subterranean drilling operations. At least one predictive model may be generated based, at least in part, on at least one training data set. The at least one predictive model may determine a rate of penetration (ROP) for a drilling operation of the same type to which the at least one training data set corresponds.