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Petoro

Petoro needed to improve mature-field development and well placement decisions on the Norwegian Continental Shelf while dealing with more than 1,000 sidetrack opportunities per field, low-quality data, and no unified collaboration workflow between drilling and reservoir engineering teams.

hundreds more realizations of geological scenarios
in a fraction of the time this work previously took
over 1,000 sidetrack opportunities per field

Reported impact

Business outcome

Petoro improved well location targeting, explored a much wider range of geological development scenarios, and accelerated well placement decisions for mature Norwegian fields.

  • Productivity Improvement
  • Speed to Market

Solution

Petoro used AI embedded in digital solutions from SLB to speed mature-field development and optimize well placement. The source describes an implementation centered on bringing mature-field opportunity data and reservoir context into a shared digital workflow, where AI and analytics evaluate very large sets of sidetrack options that had previously been handled manually. Within that environment, the solution generates probabilistic well rankings to highlight low-performing wells, creates probability maps to improve understanding of overall reservoir performance, and applies analytics-based optimization to target large, connected, highly saturated oil volumes. Reservoir engineers can then generate hundreds more geological scenario realizations in far less time, while drilling and reservoir teams work from a more unified decision process. The source does not name a separate storage product, but it clearly positions SLB’s digital solution layer as the system where data extraction, scenario generation, optimization, and decision support are operationalized.

Data flow

Mature-field opportunity and reservoir data are brought into SLB digital solutions, where AI and analytics process large sets of sidetrack candidates, generate probabilistic well rankings and probability maps, and feed optimized well-location recommendations to reservoir engineering and drilling teams for field-development decisions.

Solution architecture

1 components · 1 layer
  1. Compute
    • digital solutions from SLB Provides the shared digital environment where AI ranks well opportunities, generates reservoir probability views, and supports faster well placement decisions.

Architecture clues

  • Applied to mature fields on the Norwegian Continental Shelf
  • Applies analytics-based optimization to target large connected and highly saturated oil volumes
  • Creates a unified workflow across drilling and reservoir engineering domains
  • Uses probabilistic well ranking to identify low-performing wells
  • Uses probability maps to assess reservoir performance

Evidence from the source

"AI delivered improvements in well location targets for Petoro using probabilistic well ranking to identify low performing wells, probability maps to understand overall reservoir performance, and analytics-based optimization steps which targeted large connected and high saturated oil volumes."
"AI guides us to the best new well location every time."
"Reservoir engineers are generating hundreds more realizations of geological scenarios to explore a far wider spread of possibilities for field development in a fraction of the time this work previously took."
"The innovative workflow aims at enhancing the selection of sidetrack wells opportunities in mature fields by overcoming various challenges, including manually managing a vast volume of over 1,000 sidetrack opportunities per field, grappling with low-quality data, and the absence of a unified collaboration platform between drilling and reservoir engineering domains."
"Using AI embedded in digital solutions from SLB, Petoro reduced mature field development production optimization and vastly accelerated its well placement strategy."
Transparency Evidence & methodology 70/100 reference value · 92% 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 value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • 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
    5 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 specificity8/10
    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. Petoro

    Petoro customer story

    First-party
    https://www.slb.com/resource-library/case-study-with-navigation/di/ai-accelerating-possibilities-for-petoro
    Source type
    Customer-published story
    Published
    Jan 31, 2024
    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.