Integrating Automated Offset Well Selection and Machine Learning for Enhanced ROP Prediction and Optimization in Oil and Gas Drilling
Authors: Hashemi Abdolnabi , Todorov Dimitar 
Published: February 2025

The objective of this paper comprises two primary goals. Firstly, we propose a novel methodology for the automated selection of offset wells within mature fields. Our approach systematically identifies the most suitable offset wells for new well plan projects, considering diverse criteria such as geological information, well trajectories, casing designs, drilled intervals, and operational parameters. By automating this selection process, we mitigate human bias and ensure a comprehensive representation of drilling conditions. Secondly, we demonstrate how this approach can be utilized to fine-tune machine learning algorithms and enhance their accuracy in predicting the Rate of Penetration (ROP) in new well plans.

We have developed a set of similarity indices and an automated algorithm to quantify the resemblance among a group of wells within a field. These similarity indices enable drilling engineers to effectively discern and compare the similarities between previously drilled wells and the specifications of a new well plan. The indices cover various aspects including geological information (such as lithology and layer thickness), well trajectories, casing designs, drilled intervals, and operational parameters. By calculating these indices, we establish an overall similarity index for each well. Subsequently, these overall similarity indices are compared with those of a new candidate well plan, allowing for the identification of the most relevant offset wells. The shortlisted offset wells are then utilized to refine the machine learning algorithms within our in-house ROP prediction and optimization microservice.

The results demonstrate that the automated offset well selection algorithm not only enhances the accuracy of well planning and programming but also improves the efficiency of our ROP prediction solution's training process. Our approach incorporates three essential components: Automated Offset Well Selection, where our in-house microservice systematically selects offset wells, ensuring a diverse training dataset for machine learning algorithms. Feature Engineering involves extracting pertinent features from offset wells to enrich the input data for training machine learning models, encompassing lithology, well trajectories, casing designs, and operational parameters. Model Training and Optimization employ state-of-the-art techniques to optimize models for ROP prediction based on selected offset well features. Continuous refinement enhances prediction accuracy, optimizing drilling efficiency, and reducing operational costs. This data-driven methodology synergizes with traditional geological considerations, facilitating informed decision-making in well planning and execution. Moreover, our approach holds promise for well clustering and benchmarking applications.

The novelty of this paper lies in its integration of automated offset well selection with machine learning for enhanced ROP prediction and optimization. This approach improves accuracy in well planning, programming, and training processes, while also enabling potential applications in well clustering and benchmarking.