FieldState Turns Brownfield Data Into Scalable Reservoir Surveillance
A reviewable workflow for fragmented production data, legacy documents and recurring QA/QC, from production baseline to reservoir evidence.
01Recover value from production data you already have.
FieldState reviews every brownfield well on a recurring cadence and turns production signals into an evidence-backed action backlog: monitor, fix data, validate, or approve intervention.
See the asset-level backlog first, then drill into the well-level evidence.
Each candidate is ranked with screening-level economics before your team spends field or service dollars.
Well C-018
Required validation
Confirm service scope and field constraint before approval.
Concise at the top. Verifiable at the bottom.
Leadership gets the conclusion first. Engineers can drill into source data, formulas, segments and proxy assumptions before trusting the recommendation.
Business conclusion
Recommended stance, value, urgency, owner and reason the prior read no longer applies.
Source lineage
Production coverage, data-quality gates, selected intervals and excluded periods stay visible.
Formulas and segments
DCA fits, economic inputs, segment choices, Basic NPV, IRR and confidence measures sit beside the result.
Proxy assumptions
Directional phase and rel-perm proxies are marked as proxies, not reservoir-model claims.
Calculation check
Technical pages let the operator trace the recommendation back to the underlying numbers.
Reliable Interpretation Starts With Reliable Data
Before any reservoir surveillance can be trusted, two interdependent workstreams have to move together: the data foundation that makes information usable, and the engineering interpretation built on top of it.
Data foundation and tools
Make structured and unstructured brownfield data usable without repeated manual collection, checking and re-formatting.
- Scan, read and recognize legacy documents
- Extract tables, facts, events and engineering signals
- Normalize wells, rates, events and identifiers
- Create shared analytical storage with traceable evidence
Engineering interpretation
Turn prepared and incoming data into a reviewed view of asset behavior that can be refreshed as conditions change.
- One-time interpretation of existing data
- Continuous interpretation as new data arrives
- Operational event and intervention effects
- Pressure, injection and connectivity signals
The bottleneck is not only fragmented data. It is the manual effort required to make that data usable every time the question changes.
02Mature Assets Need Continuous Engineering Capacity, Not a One-Off Interpretation
A conventional service project can deliver a one-off interpretation. The harder problem is keeping interpretation accurate while assets keep changing, manual work accumulates and engineering capacity stays limited.
Point-in-time snapshot
A one-off report explains the data available today, then starts aging as soon as the asset changes.
- Production baseline
- Initial event chronology
- Reservoir questions and data gaps
Manual change load
Even existing assets keep changing through rates, events, interventions, pressure updates and operating constraints, which creates repeated preparation and review work.
- More trends to monitor
- More assumptions to refresh
- More evidence to review
Scaled engineering capacity
Continuous workflows help the same experts cover more fields while improving accuracy and reducing repeated manual rework.
- Portfolio-level scaling
- Reusable review logic
- More current decisions
The value is not only interpretation quality. It is the ability to keep that quality current across more assets without scaling manual effort linearly.
03Five Phases Turn Manual Data Work Into Repeatable Decision Gates
Each phase turns scattered inputs and manual checks into a concrete output and a decision gate. The timing reflects the current overlapping roadmap rather than a sequential waterfall.
- Rates, days-on, well metadata
- Legacy documents
- Extract, normalize, QA/QC
- Build decline baseline
- Trusted production baseline
- Do-nothing forecast + economic limit
- What the asset does today
- How long it lives as-is
- Workovers, uptime, choke settings
- Operating events
- Create event model
- Build what-if and loss logic
- Operational scenario workspace
- Opportunity-loss view
- Which levers matter
- What to test first
- Pressure, injection, well tests
- Connectivity evidence
- Run RTA and connectivity
- Interpret pressure support
- Pressure-aware reservoir behavior
- Recovery impact view
- Where support is real
- Where losses and recovery remain
- Well models, equipment limits
- Surface constraints, historian path
- Connect nodal/VLP logic
- Add constraints and integrations
- Constraint-aware workspace
- Daily optimization view
- Which actions are feasible
- Which constraints block value
- Validated rules and review gates
- Workflows and evidence packs
- Codify DAG workflows
- Add selective AI reasoning
- Reusable engineering workflow
- Review logic and traceability
- Which decisions repeat
- What can be reviewed and scaled
High-Level Delivery Roadmap
Each bar represents a full product phase. The roadmap shows how the five workstreams overlap instead of running as a sequential waterfall.
Data Needed by Phase
The discussion should confirm what data exists, what can be extracted quickly, and which later phases depend on sources that may not be ready on day one.
Minimum useful starting point.
- Oil, water, gas rates
- Days-on production
- Basic well metadata
- Legacy files for extraction
Turns baseline into controllable scenarios.
- Choke settings
- Uptime and downtime
- Workover / intervention history
- Event chronology
Moves from production behavior to reservoir behavior.
- Pressure data
- Injection history
- Well tests
- Connectivity evidence
Connects engineering constraints to operations.
- Well models
- Equipment limits
- Surface constraints
- SCADA / historian path
Requires validated workflows before AI can assist safely.
- Engineering rules
- Approval constraints
- Workflow history
- Review evidence packs
AI Removes Manual Work Around the Engineering Process
Legacy reports, scans and spreadsheets are converted into structured, source-linked facts.
Wells, dates, units, events and gaps are checked before engineers spend time on interpretation.
Assumptions, confidence and source references are prepared for SME review.
Repeatable phase tasks and review gates become traceable workflows instead of ad hoc manual work.
New incoming data updates the surveillance state without rebuilding the whole interpretation from scratch.
AI is used around the engineering workflow: preparation, checking, evidence and refresh. Engineers still approve interpretation and decisions.
08The First Discussion Should Confirm Where Manual Data Work Blocks Surveillance
We do not need to price the full program in the first meeting. We should confirm the asset scope, available data, manual bottlenecks and the first useful decision gate.
Asset scope
Fields, wells, systems and the surveillance priority you want to address first.
Available data
Production exports, legacy documents, event history, pressure, injection and well-test evidence.
Manual bottlenecks
Where engineers spend time collecting, checking, reconciling or explaining data today.
First decision gate
The first phase output that would be useful enough to plan in detail with your team.