Prescient

The foundation of any efficient drilling operation? Reliable, real-time #data. But when that data comes from hundreds of sensors across rigs, handling it efficiently becomes a major challenge, especially at scale. On top of that, there is managing compute costs. With 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐛𝐥𝐨𝐜𝐤 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐥𝐨𝐰 𝐜𝐨𝐝𝐞 𝐞𝐝𝐠𝐞 𝐝𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬, operators can achieve: ✅ 𝐔𝐥𝐭𝐫𝐚-𝐥𝐨𝐰 𝐜𝐨𝐦𝐩𝐮𝐭𝐞 𝐜𝐨𝐬𝐭 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 – Processing billions of real-time data points per day without database slowdowns. ✅ 𝐅𝐚𝐮𝐥𝐭-𝐭𝐨𝐥𝐞𝐫𝐚𝐧𝐭 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 – Automatic workload balancing across #rigs, preventing crashes and bottlenecks. ✅ 𝐍𝐞𝐚𝐫-𝐳𝐞𝐫𝐨 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐨𝐯𝐞𝐫𝐡𝐞𝐚𝐝 – Deploy new data workflows with 95% less engineering time, enabling rapid adaptation to changing field conditions. Want to know more about managing high-volume, high-speed sensor data at scale without trade-offs in performance or cost? Learn here: https://rb.gy/u34mb4

What can digital transformation projects in oil and gas learn from the automotive industry? In his guest post, Russell Whitney, IIoT Manager at Precision Drilling, shares his perspective from the field. He draws parallels between the Agile methodology embraced by the automotive sector and the small, measurable steps that drive sustainable digital transformation in oil and gas. This thoughtful read sheds light on how to bridge the gap between legacy systems and continuous innovation. Read the full article: https://lnkd.in/dKJeSiVy #oilandgas #thoughtleadership #digitaltransformation #digitaltwin

We had an incredible time at SPE OKC Oil and Gas Symposium this year. Thank you to everyone who joined us in Oklahoma City, and a huge thank you to Russell Whitney IIoT Manager at Precision Drilling for joining our CEO, Andy Wang, PhD for our joint presentation, "Distributed Database for EDR Data Processing." For those who weren't able to make it, you can still get our slide deck here: https://lnkd.in/dBVZZZcs #SPEOKC #oilandgas

Just a few hours away! Andy Wang, PhD, CEO at Prescient, is headed to the SPE OKC Oil and Gas Symposium 2025 along with Russell Whitney, IIoT Manager at Precision Drilling We’re excited to be part of the conversation around solving some of the industry’s biggest challenges and are all set to tune in closely on how to: ➡️ Identify the bottlenecks in data processing for a large-scale asset digital twin ➡️ Explore how the edge data engine and distributed database alleviate data processing challenges across multiple sources For context, at Prescient, we help #energy teams solve the following: ➡️ High-volume, high-speed EDR, #sensor #data, and maintenance logbook data processing ➡️ Monitoring component asset lifetime across 100+ #rigs Let’s talk at OKC if you’ll be there! Event Details: https://rb.gy/l7q3o5 #conference #SPEOKC #oilandgas

Everything you need to know about Prescient's participation at SPE OKC #oilandgas Symposium 2025 ⬇

Meet us at the SPE OKC Oil and Gas Symposium 2025! On April 15th, join Andy Wang, PhD, CEO at Prescient, and Russell Whitney, IIoT Manager, Precision Drilling for the 'Distributed Database for Scaling Up Real-time EDR Data Processing' session. Expect a deep dive into the implementation of distributed databases that facilitate the scaling of real-time Electronic Drilling Recorder (EDR) data processing across multiple sources. Find us at Area D3, 10:30 to 11:00 AM CST — and feel free to stop by to say Hi! See you there! Register at: https://rb.gy/l7q3o5 #conference #usa #drilling #oilandgas

Managing industrial data at scale is tough. But managing high-speed sensor data across global sites? That’s a whole new level of complexity. To solve this, Bosch needed a solution for their Integrated Asset Performance Management (IAPM) that could handle real-time #data at scale, ensuring #predictivemaintenance and process optimization without overwhelming their infrastructure. 𝐓𝐡𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞? ❌ 24kHz data rates flooding their systems - traditional pipelines couldn’t process it fast enough. ❌ Scattered sensor data from multiple devices - integration was a bottleneck. ❌ Scaling across global sites - without causing performance trade-offs. 𝐇𝐨𝐰 𝐰𝐞 𝐰𝐞𝐫𝐞 𝐚𝐛𝐥𝐞 𝐭𝐨 𝐡𝐞𝐥𝐩? Bosch leveraged our edge data pipeline, built on distributed functional block programming, to: ✅ Process high-speed sensor data in real-time, without database slowdowns. ✅ Automate sensor configuration - enabling seamless data flow across IO-Link sensors, PLCs, and edge devices. ✅ Ensure scalability - deploying digital twin solutions across thousands of industrial sites without delays. Through this collaboration, Bosch was able to transform their industrial data infrastructure and turned raw data into real-time, actionable insights at scale. 🔗 Link to the full case study: https://rb.gy/i8cp0h

Implementing Condition-Based Maintenance (CBM) involves more than just monitoring sensor data. Without properly processing operational data to contextualize pressure, speed, and torque, even the best CBM systems can misinterpret normal operational variations as faults. A Context-aware CBM system can: ✅ Enhance prediction accuracy by combining sensor and operational data ✅ Improve comprehension of maintenance alerts ✅ Automate analytics adjustments based on real-time operational context Our operational digital twin technology ensures that your CBM strategy is built on high-quality, real-time data - eliminating false positives and optimizing asset reliability. 🔗Learn how it works: https://rb.gy/q0m84d #digitaltwin #cbm

What if you could predict equipment failures months in advance? Today, most Condition-Based Maintenance (CBM) solutions predict equipment failures hours or days in advance. While this is great, it is still somewhat reactive. We've developed an Asset Life Model that predicts equipment failures months in advance. You can think of it as a data-driven fatigue model. The model takes into account the cumulative fatigue on the equipment based on its operating conditions, across it's life time, so that it can make equipment or component life predictions from the day they are put into service. What's the implication of this? You can anticipate maintenance needs months in advance, and you have full supply chain predictability. What did it take to build and run this model? It took about 600-Billion data points to train, and at scale, it monitors over 7,000 equipment components and processes 6-Trillion data points per day for inference. What amazing things we can do today:) https://lnkd.in/einkfipR

Scaling digital twins can feel overwhelming. Complex systems, scattered data sources, and resource-intensive engineering often slow progress. But what if there were a better way? Precision Drilling’s digital twin adoption journey shows that building and scaling #digitaltwin solution doesn’t have to be a drawn-out process. Their team deployed a highly advanced digital twin across 101 rigs in just over a year — enabling real-time insights into asset lifecycles and improving operational efficiency. Here’s how they made it happen: ➡️ Rapid Iteration: Starting with a blank slate in January 2023, they focused on a single asset class (mud pumps) and delivered the first version in just two months. ➡️User-Centric Development: Weekly feedback loops with field teams drove continuous improvement, with new features deployed within days. This built trust and encouraged adoption from the ground up. ➡️Massive Data Processing: The system handled 25,000 high-speed data tags daily, translating to 2.1 billion #data points processed every day across #rigs. ➡️95% Less Engineering Time: Automation streamlined processes, enabling rapid development and reducing resource strain. By prioritizing iterative development, automation, and collaboration, Precision team scaled their digital twin organically, delivering measurable outcomes like reduced downtime and cost savings. Watch how they did it: https://rb.gy/42smjq