In this paper, we introduce a fresh perspective on the life cycle of mud motor power sections. Rather than following the conventional steps associated with this well-established mechanical tool, we have reevaluated and reimagined the entire process. Our innovative approach leverages the digital to facilitate the development, optimization, and maintenance of power sections. By implementing this approach, we can augment the value and functionality of power sections without any costly redesigns.
Our focus is on three essential elements of the power section life cycle: designing the power section to meet field requirements, selecting the ideal power section for the job based on drilling conditions, and monitoring the health of the power section during operation. To achieve these goals, we integrated a state-of-the-art physical model of the power section with the power of machine learning (ML) and data science. With this model, we can simulate the power section's performance and durability during the design and optimization stages and monitor its fatigue life in real time using a digital twin approach.
The utilization of digital capabilities enables adoption of a systematic approach toward the mud motor power section life cycle. This utilization resulted in increased drilling performance, reduced nonproduction time, and a significant decrease in field failures.
Digitalizing the development of the power sections reduced time-to-market and produced customized products by addressing field requests through modeling. It also helps identify the optimal downhole environment for maximum model performance.
To optimize the power section for drilling, a modeling method was used to select the best model and define drilling parameters based on drilling requirements and equipment. This approach helps to significantly improve rate of penetration (ROP) while reducing power section damage. Defining power sections with high performance and durability alongside optimal drilling parameters enhances drilling efficiency.
Our maintenance process incorporates an industry-unique prognostic and health management method. This approach enables real-time tracking of the power section's remaining useful life, minimizing the likelihood of failure. By accurately determining each power section's remaining resources, we can decide on its further utilization or retirement, leading to the fleet optimization.
In summary, our complex solution based on a digital approach offers a dependable and effective tool for achieving top-notch life cycle management for power sections. The proposed approach is a first-of-its-kind solution that combines all the critical stages of the mud motor power section life cycle. This innovative approach showcases the significant value of digital technology in providing additional functionality and creating new services for traditional mechanical power sections. The proposed solution offers a comprehensive and unique solution that significantly enhances the cumulative commercial impact on all three stages of the power section life cycle.
