A Sequential Feature-Based Rate of Penetration Representation Prediction Method by Attention Long Short-Term Memory Network

Abstract

In the petroleum and gas industry, optimizing cost-effectiveness remains a paramount objective. One of the key challenges is enhancing predictive models for the rate of penetration (ROP), which are intricately tied to the delicate interplay between significant parameters and drilling efficiency. Recent research has hinted at the potential of temporal and sequential elements in drilling, but a detailed exploration and understanding of these dynamics remain underdeveloped. Addressing this research gap, our primary innovation is not just the introduction of a model but rather the employment of the attention-based long short-term memory (LSTM) network as a tool to deeply analyze the role of sequential features in ROP prediction. Beyond merely applying the model, we furnish a robust foundation for sequential analysis, detailing data processing methods and laying out comprehensive data analytics guidelines for such temporal assessments. The utilization of the LSTM network, in this context, ensures meticulous capture of real-time drilling data nuances, providing insights that are both profound and actionable. Through empirical evaluations with real-world data sets, we accentuate the vital importance of time-sequential dynamics in refining ROP predictions. Our methodological approach, tailored for the oilfield domain, is both rigorous and illuminating, achieving an R2 score of 0.95 and maintaining a relative error under 10%. This effort goes beyond simply proposing a new predictive mechanism. It establishes the centrality of sequential analysis in the drilling process, charting a course for future research and operational optimization in the petroleum and gas sector. We not only offer enhanced modeling strategies but also pioneer insights that can shape the next frontier of industry advancements.