Procedure of the hybrid physics-ML ROP modeling and Dataset Analysis

Tech breakthrough in ROP modeling: Hybrid physics-ML approach

In the world of drilling, predicting the Rate of Penetration (ROP) accurately is crucial for optimizing drilling parameters and ensuring efficient operations. Traditionally, ROP prediction models have relied on either physical models or Machine Learning (ML) models, each with its own set of advantages and limitations. However, a new hybrid approach that combines the strengths of both physics and ML models is gaining traction in the industry.

The hybrid physics-ML ROP modeling procedure involves a series of steps to ensure accurate predictions tailored to specific drilling fields. By integrating physical models with ML algorithms, this approach aims to enhance accuracy and interpretability in ROP predictions. The procedure begins with data collection from drilled wells, followed by data denoising, feature selection, and dataset division for processing.

One of the key aspects of the hybrid ROP modeling procedure is the selection of suitable physical and ML models based on performance evaluation metrics. The study introduces novel approaches for hybrid physics-ML modeling, including residual modeling, integrated coupling, simple average, and bagging. These approaches offer different ways to combine physics-based predictions with ML algorithms for improved ROP modeling.

To demonstrate the effectiveness of the hybrid approach, comprehensive logging data from an ultra-deep well in the Halahatang oil field of the Tarim Basin were used. The dataset included various drilling parameters and formation characteristics relevant to ROP prediction. Through data denoising and feature selection, the study identified the most relevant variables for ROP prediction.

The hybrid physics-ML models were evaluated using performance metrics such as root mean square error (RMSE), average absolute percentage error (MAPE), and coefficient of determination (R2). These metrics help assess the accuracy and reliability of the hybrid models in predicting ROP.

Overall, the hybrid physics-ML ROP modeling procedure represents a significant advancement in the field of drilling operations. By combining the strengths of physical models and ML algorithms, this approach offers a more robust and adaptable solution for ROP prediction in diverse drilling fields. As technology continues to evolve, hybrid modeling approaches like this one are likely to play a key role in optimizing drilling operations and improving overall efficiency in the industry.