ARCHITECTURAL APPROACHES AND METHODS OF PARAMETRIC OPTIMIZATION OF DISTRIBUTED DATA STORAGE IN THE DRILLING ECOSYSTEM

Abstract

The current complex problems in the field of well construction and drilling automation require the introduction of innovative technologies to improve the efficiency of information support systems. In the context of the transition to the Drilling 4.0 concept, the rapid increase in the number of sensors and the implementation of high-frequency telemetry systems leads to the generation of massive volumes of heterogeneous data (Big Data). Transmitting this data to centralized cloud storage in real-time is often impossible due to the limited bandwidth of satellite communication channels and strict latency requirements. The scientific research represented in this article offers an integrated approach that provides a critical analysis of existing commercial drilling automation solutions, revealing that most of them focus on the algorithmization of mechanical processes, neglecting the architectural optimization of data flows. Traditional centralized methods of data management often result in significant latency and communication resource costs. With the increasing volume and complexity of telemetry data to be interpreted, it is becoming increasingly important to use new, automated architectural approaches.

The purpose of this study is to improve conventional methods of data flow management by introducing a transition from a centralized to a hierarchical three-level architecture of distributed data storage (Edge-Fog-Cloud). A logical model of the system's operation has been developed, covering primary signal processing at the Edge level, aggregation at the Fog level, and global analytics at the Cloud level. The well-defined purpose of the study allows us to focus on the possibilities of introducing the latest computational technologies into the automated drilling process. The structure of the work is logically organized, ensuring consistency and coherence of the presentation. The methodological part of the study is characterized by an ideal combination of theory and practice, which makes the proposed approach understandable and useful for the scientific community. For the first time, the problem of parametric optimization of data distribution is formalized, and the use of Reinforcement Learning methods is substantiated for the dynamic adaptation of system parameters. A significant advantage is the development of algorithmic principles that take into account the specific needs and limitations when working with high-frequency telemetry data. This is a significant contribution to solving the problem of computational and network complexity that often arises in the context of remote drilling operations. The scientific novelty of the research consists in the implementation of adaptive optimization methods that open up new perspectives in the automation of data distribution processes based on the continuous collection of telemetry signals.

Consequently, this scientific article is intended not only to improve the theoretical foundations of distributed databases but also to contribute to the improvement of existing approaches to drilling operations in the field. To practically verify and illustrate the proposed approaches, a custom software web application was developed. This module simulates the reception of streaming telemetry measurements, the real-time calculation of derived indicators (such as mechanical specific energy), and the formation of structured data packets for subsequent transmission from the Edge to the Fog level. Provided results can be effectively integrated into the practice of modern drilling automation, demonstrating broad potential for further development in this area by ensuring the operation of real-time control loops, guaranteeing data integrity, and reducing operational costs for IT infrastructure.