ZerdaLab provides on-demand, high-performance PDC drill bits for budget-sensitive drilling applications like geothermal energy. The company simplifies bit designs for manufacturing and repair, uses machine learning for objective bit selection and performance validation, and offers customer-owned, repairable steel-body bits. This approach reduces costs while improving the speed and quality of bit selection for challenging drilling operations.
Funding
Funding not disclosed
Founders
Product
Problem
High-specification PDC drill bits are expensive and often prohibitively costly for budget-sensitive applications like geothermal drilling. Selecting the right bit is a complex, time-consuming, and subjective process that requires accounting for numerous variables, and an incorrect selection leads to poor performance and wasted investment.
Solution
ZerdaLab reduces the cost of high-specification drill bits while improving the speed and quality of bit selection for the customer's application. The company designs bits that are simplified for manufacturing and repair, using unified cutter sizes and fewer unique components, and offers steel-body bits that are sustainable, repairable, and recyclable. ZerdaLab employs machine learning-enhanced bit selection and rock failure physics-based models to provide data-driven, objective recommendations. Customers can own and brand their bit designs, with performance validated through ML-enhanced models and automated dysfunction identification based on drilling data.
Target Audience
Primary customers are drilling operators in budget-sensitive and challenging applications, particularly geothermal drilling, as well as oil and gas companies seeking cost-effective, high-performance PDC bits.
Features
- Simplified manufacturing with unified cutter sizes, reduced unique components, and fewer critical dimensions
- Steel-body construction that is sustainable, repairable, recyclable, and globally available
- Improved hydraulics with reduced nozzle housing footprint and flexible port locations
- Customer-owned and branded bit designs with adaptable manufacturing specifications
- Machine learning-enhanced performance validation and in-house cutter placement models
- Data-driven bit selection using rock failure physics-based models and automated dysfunction identification