Sunya | Energy AI Observatory · Open the interactive dossier
1 AI initiative profile for Azule Energy across 1 department, with evidence grades and source links. Strongest evidence: C · Supplier-reported — Digital well planning linked to AI-enabled rig automation.
192hours before; 31hours after. Supplier-described planning workflow; separate 2,000-meter section result. Case-study publication and measurement dates unknown.
SLB-reported. The 16% drilling-speed gain concerns a different measure; no complete financial return.
Read the case and original sourcesSupplier reports 95% of engineering tasks automated, planning time of 192 versus 31 hours and 75% fewer manual handoffs. A 2,000-meter section had zero reported nonproductive time and 16% higher net rate of penetration than the field benchmark. These are different measures, not a common financial return.
Supplementary notes can update existing programs or describe adjacent work. They are not extra adoption counts.
SLB describes Azule using one digital workflow from well design through rig execution offshore Angola.
Supplier reports 95% of engineering tasks automated, planning time of 192 versus 31 hours and 75% fewer manual handoffs. A 2,000-meter section had zero reported nonproductive time and 16% higher net rate of penetration than the field benchmark. These are different measures, not a common financial return.
Limits: No well identifier, wider well population, benchmark construction, review-effort adjustment, quality comparison or full cost. Execution is described as autonomous, but approval, override and fallback rules are undisclosed. No LLM or AI-only contribution is established; zero NPT for this section is not a long-term reliability rate.
SLB third-quarter 2026 results — selected operating and digital highlights · 2026-10-23
Azule Energy pioneers digital well planning and rig automation in Africa · Unknown