Use Case Intelligence RSS Search use cases Search
SLB Energy West Africa Company newsroom

Eni

Eni wanted to improve drilling efficiency, consistency, and safety in well planning and offshore drilling operations by defining better operating limits and reducing rework, human error, and hazardous rig-floor exposure.

Read the original story Explore all use cases
1,224 meters per day
operational speeds up to 26 std/hour
red zone presence during tripping reduced from an average of three instances to zero
weight-to-weight time decreased by over 50%

Reported impact

Business outcome

Eni improved drilling speed, reduced nonproductive time, increased operational consistency, and improved safety by removing personnel from hazardous rig-floor activity during tripping and pipe-handling operations.

  • Productivity Improvement
  • Risk Reduction

Solution

Eni implemented an SLB digital drilling workflow that combined planning, AI-assisted execution, and automation for offshore well delivery. In the planning stage, DrillPlan coherent well planning and engineering solutions were used to capture drilling constraints for each parameter and iteratively define the operational window or envelope, allowing suboptimal designs that needed re-engineering to be identified earlier. During execution, DrillOps intelligent well delivery and insights solutions used the defined envelopes to recommend and apply optimal drilling parameters and to autonomously manage drilling phases. On the rig floor, DrillPilot equipment sequencing software coordinated multiple machines in an optimized sequence, while Neuro autonomous solutions provided autonomous steering and broader automation support. The deployment was used in Eni's first fully autonomous offshore West Africa operation in Q1 2024. The source describes an execution-oriented control stack rather than a distinct named storage tier.

Data flow

Operational constraints from well planning were captured in DrillPlan and turned into an operational envelope. That envelope fed DrillOps, which recommended and applied drilling parameters and autonomously managed phases. DrillPilot then coordinated drill-floor machine sequences, while Neuro supported autonomous steering during execution on the offshore well.

Solution architecture

4 components · 2 layers
  1. Orchestration
    • DrillPlan Captured parameter constraints and defined the operational envelope during well planning.
    • DrillOps Recommended and implemented optimal drilling parameters and autonomously managed drilling phases.
    • DrillPilot Sequenced multiple drill floor machines in an optimized order to reduce human error and rig-floor exposure.
  2. Uncategorized
    • Neuro Provided autonomous steering and broader automation for well delivery.

Architecture clues

  • First fully autonomous deployment in offshore West Africa in Q1 2024
  • Iterative planning process defined an operational window before execution
  • Service contractor incentive scheme was used to promote operational excellence among crews
  • Source describes machine sequencing and autonomous steering in live drilling operations rather than a standalone analytics environment

Evidence from the source

"Achieved a new global rate of penetration (ROP) record: 1,224 meters per day."
"DrillOps™ intelligent well delivery and insights solutions" were used for "recommending and implementing optimal drilling parameters within defined envelopes and autonomously managing drilling phases."
"DrillPilot™ equipment sequencing software" was used for "sequencing and optimizing operation of drill floor machines."
"DrillPlan™ coherent well planning and engineering solutions" were used for "defining operational envelopes during well planning."
"Neuro™ autonomous solutions" were used for "autonomous steering and overall automation in the well delivery process."
"Red zone presence during tripping reduced from an average of three instances to zero."
"The first quarter of 2024 saw Eni's first fully autonomous deployment using these technologies in offshore West Africa."
"Weight-to-weight time decreased by over 50%."
Transparency Evidence & methodology 95/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    8 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Eni

    Eni customer story

    First-party
    https://www.slb.com/resource-library/case-study-with-navigation/di/eni-using-digital-ai-and-automation-to-drill-beyond-limits
    Source type
    Customer-published story
    Published
    Jul 29, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
SLB Aug 4, 2026 ADNOC Energy United Arab Emirates ADNOC needed to improve performance across a large upstream drilling fleet and operationalize AI at rig scale rather than in isolated pilots. over 120 drilling rigs SLB Jan 31, 2024 Dragon Oil Energy United Arab Emirates Dragon Oil needed to enhance planning and operational performance in a mature offshore field. SLB Jan 31, 2024 OMV Energy global OMV sought to increase efficiency across its global operations using AI-enabled digital capabilities.