AI-Based Analysis of Formation Heterogeneity and Drill String Dynamics in Hole Deviation Management

Effective hole deviation management is crucial for optimizing drilling operations, particularly in challenging geographic environments. This paper presents Deep Learning (DL) based DACRNET (Dynamically Adaptive Convolutional Regression Network) to address the formation of heterogeneity and drill string dynamics. The proposed approach integrates multiple stages: data acquisition, preprocessing, Exploratory Data Analysis (EDA), feature creation and transformation, data partitioning, and DACRNET model. The high-frequency drilling data, including measurements from sensors on drill strings and formation properties, are collected by the data acquisition process. Data preprocessing and EDA techniques visualize the trends and patterns, allowing for the identification of key variables influencing deviation. The feature creation and transformations enhance the dataset representation, facilitating improved learning performance. The dataset is then partitioned into training and testing subsets to ensure robust model evaluation. DACRNET, a DL model specifically designed for this task, leverages sequential and spatial patterns in the data to identify and predict deviations with high accuracy. This model is implemented using Python Software, from the evaluation, the minimized Mean Absolute Error (MAE) of $\mathbf{0. 0 2 7}$, Mean Square Error (MSE) of $\mathbf{0. 0 0 9 9}$, Root MSE(RMSE) value is $\mathbf{0. 0 9 9 4}$, and Mean Absolute Percentage Error (MAPE) of 4.69 are significantly attained. Predictions are used to guide real-time deviation management strategies, ensuring optimal drilling outcomes.