DQ – Making Analytics for Oil Wells Easier for Technical Teams
Project
DQ (Industrial AI & DataOps Platform)
Role
UI/UX Designer
Users
Drilling Engineers, Geologists, Analysts, Operations Managers
Tools
Figma, Photoshop, Figjam
Scope
Designed key modules for oil well comparison, clustering, ranking, and detailed analysis
The Challenge
Drilling teams work with massive datasets across different well types. They need to filter, compare, and analyze wells based on trajectory, bit type, cost, depth, and more, all while making fast, data-backed decisions.
The problem? Most platforms are either too rigid or too technical, making workflows slow and frustrating.
DQ aimed to simplify this process with one platform. My task was to help make their interface clean, intuitive, and easy for technical users to explore large datasets for oil wells without losing context.
Projects Goals
Make project and well setup quick and clear
We wanted users to easily create new wells or projects without getting lost in long forms. A clean layout and smart defaults helped users get started without confusion.
1
Help users compare offset wells more easily
Engineers needed a more effective way to identify the best-performing wells. We added visual scoring, filtering, and sorting so they could find matches faster without needing to switch to Excel.
2
Turn raw data into visual insights
To help users make sense of performance, we designed interactive charts like Pareto plots, cluster views, and ranking plots. This made trends easier to spot at a glance.
3
Keep the experience consistent
Since I reused many existing components, I focused on applying them consistently, using the same layout logic, filter behavior, and visual patterns, so users wouldn’t feel like each module was a different product.
4
Research & Inputs
Since this was a live product with existing users, here’s how I guided my design decisions:
Internal Review
I studied existing flows and screens to identify bottlenecks. Many actions (like filtering or scoring) took too many steps or were buried inside dense tables.
Stakeholder Feedback
PMs and engineers explained the importance of quick filtering, cluster visibility, and easy exporting for internal reports.
Industry Context
Before getting into design, I reviewed how existing oil and gas tools approach offset well comparison, specifically, Petrolink and Peloton. Both platforms were widely used in the industry but showed clear limitations when it comes to usability for engineers in the field.
What I Observed
- Heavy reliance on dense scatter plots
- Visuals lacked interaction with contextual scoring
Extracting key insights often required extra interpretation
- Text-heavy dashboards filled with operational logs
- Reports were useful, but required manual parsing
- Lacked visual prioritization and comparison features
Opportunity for DQ
These tools were build for data access, not decision-making
- Clear clustered visualizations
- Direct comparison tables with scoring
- Fast, guided filtering for engineers to reach insights quickly
Why We Made These UX Decisions
Scoring Cards in Offset Well Table
Why?
Engineers had trouble understanding why some wells ranked low. They needed a fast, scannable way to compare multiple criteria.
Decision:
We introduced scoring badges for each metric (Distance, Depth, Trajectory, etc.) with color-coded indicators to highlight strengths and weaknesses at a glance.
Heat Map Breakdown
Why?
Decision:
We introduced color-coded heat maps that visualize similarity scores by category, making it easy to identify patterns and high-potential wells at a glance.
Key Screens I Worked On
- Create New Well Flow
- Offset Well Table & Filters
- Ranking Plot View + Filtering
- Pareto Analysis Module
- Clustering Module with Weight Controls
- Well Descriptive Charts
- Full Well Details (Casing, Lithology, Activity Logs)
Outcome
Stakeholders appreciated how we reorganized the filters to mirror the way engineers think.
Reused components kept the design scalable across modules.
Visual scorecards and cluster charts reduced user reliance on raw spreadsheets.
The new structure made it easier for teams to export and report their analysis.
What I Learned
Even in highly technical tools, clarity matters the most. If engineers have to dig through 5 levels of menus, they’ll just go back to Excel.
Collaboration with SMEs is key — their feedback helped shape the filter logic and ranking model.
I learned how to design better visual logic for scientific users — everything needs to be clear, repeatable, and traceable.
