infusing intelligence in oil extraction
Project impact
frog and SLB partnered on an intensive 10-week sprint to define ICAP: a modern product vision for turning Intelligent Completion data into clearer, faster operational decisions.
The work moved from research to MVP definition and high-fidelity prototypes, translating a highly technical domain into a product strategy, navigation model, and realistic end-to-end experience.
User Interviews
Deliverables
What was blocking adoption
Intelligent Completion hardware could capture real-time well data, but the software experience was still making interpretation slow and fragmented. The opportunity was to turn raw measurements into actionable decisions for operators, engineers, and business stakeholders.
From insight to product vision
Designing for oil extraction meant entering a domain of high-stakes decisions, dense data, safety protocols, and shifting operational roles. The challenge was not to simplify the domain, but to make its complexity usable.
We ran two research tracks in parallel: stakeholder and user interviews to understand business and field needs, and a UX audit of the legacy tool to identify friction, missing patterns, and opportunities for modernization.
Instead of relying on static personas, we built a flexible user matrix around five macro-steps: set thresholds, monitor, inspect assets, make decisions, and take action. This connected user needs to measurable workflow moments.
Three primary POVs emerged: reservoir-focused users, field operators, and production-focused users. Cross-referencing these POVs with the journey map helped us isolate the two experience flows that best proved ICAP’s value.
What changed in the product
ICAP transformed the legacy WWA workflow into a modern decision platform. The experience introduced a three-level navigation model — field, well, and equipment — so users could move from overview to action without losing context.
The product also introduced interactive data visualization, custom analysis views, built-in calculations, and proactive monitoring. Instead of exporting data or interpreting static tables, users could investigate, calculate, and respond inside one platform.
