AIQ partners with SKK Migas to bring AI into Indonesia's upstream operations
AIQ has signed a strategic agreement with SKK Migas to deploy AI across select upstream assets in Indonesia. The focus: improve reservoir decision-making, lift productivity, and reduce operating costs in mature fields.
The rollout centers on AIQ's Reservoir Performance Advisor (RPA), part of its Advanced Reservoir 360 (AR360) platform. It blends predictive analytics with automation to speed up model reviews and feed operations teams with real-time insights via cloud dashboards.
What ops teams get
- Faster model assessments and fewer manual review cycles.
- Standardized workflows across assets with real-time visibility.
- According to AIQ, AR360 can deliver up to a 75% efficiency gain in user productivity.
- Better reservoir management for mature fields, with decisions grounded in live data.
Leadership view
Dennis Jol, CEO of AIQ, said the initiative responds directly to Indonesia's mature-field challenges: optimizing production from existing assets while lowering costs, with targeted AI that can extend field life and improve recovery rates.
Dr. Djoko Siswanto, Head of SKK Migas, said the collaboration underscores a push for a smarter, more resilient upstream sector-boosting operational performance and strengthening energy security through advanced AI capabilities.
How to operationalize this (practical checklist)
- Data readiness: Confirm access to reliable well, reservoir, and production data. Define refresh cadence and data quality thresholds.
- Model governance: Set approval gates for model changes, with version control and audit trails.
- Systems integration: Plan connectors to existing SCADA, PI historians, and reservoir simulators. Map data flows to dashboards.
- Change management: Identify end users (petroleum engineers, production ops, planners). Provide short, role-based training and SOP updates.
- KPIs that matter: model review cycle time, deferment reduction, water cut stabilization, incremental barrels vs. base, and opex per barrel.
- Security and access: Enforce least-privilege access, incident logging, and vendor support SLAs.
A practical rollout pattern
- 0-30 days: Data audit, asset selection, KPI baselining, connectivity setup, dashboard scaffolding.
- 30-60 days: Configure RPA scenarios, run shadow evaluations alongside current workflows, validate alerts.
- 60-90 days: Move to production use, document playbooks, set automation thresholds, and review ROI against the baseline.
Wider context
The SKK Migas partnership follows AIQ's recent agreements in Kazakhstan and Colombia, signaling broader adoption of industrial AI to raise efficiency and stabilize production across multiple geographies.
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