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Plan decarbonization pathways with confidence

Forecast emissions, model scenarios, and optimize strategies using technical, asset, and business data

Digital sustainability
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Digital sustainability - Plan

Effective sustainability strategies require more than historical data. They depend on the ability to model future scenarios, evaluate trade-offs, and optimize decisions across complex assets, portfolios, and industrial environments.

Many organizations struggle with disconnected planning processes, limited visibility across assets, and difficulty linking sustainability targets with technical, commercial, and implementation constraints.

Our digital sustainability planning solutions bring together emissions data, technical models, and planning workflows to support scenario modeling and decision-making across assets, portfolios, and low-carbon energy projects.

This enables organizations to:

  • Forecast emissions and energy performance.
  • Evaluate trade-offs between emissions, cost, feasibility, timing, and performance.
  • Plan decarbonization strategies at asset and portfolio level.
  • Align sustainability goals with technical, commercial, and implementation constraints.

Core planning capabilities

Emissions forecasting and modeling
Predict future emissions based on scenarios, asset plans, and energy usage.

Scenario analysis and optimization
Evaluate multiple scenarios to identify strategies that balance emissions, performance, cost, and feasibility.

Integrated technical modeling
Use relevant subsurface, wells, facilities, and energy system models to assess the impact of planning decisions.

Portfolio-level planning
Optimize sustainability strategies across multiple assets and regions.

Energy transition planning
Support long-term planning for low-carbon energy solutions including CCUS and geothermal, with initiative prioritization and pathway optimization based on baseline data, cost, timing, and emissions impact.

Evaluate scenarios and optimize outcomes

SLB planning solutions enable organizations to simulate different technical, commercial, and strategic scenarios before implementation.

Examples include:

  • Production vs. emissions trade-offs.
  • Energy efficiency improvements across facilities.
  • Impact of electrification or alternative energy sources.
  • Deployment of carbon capture solutions.

By comparing scenarios, decision-makers can identify strategies that maximize progress on sustainability goals while supporting business and asset performance.

Applications across the energy value chain

  • Emissions forecasting for production planning.
  • Energy transition strategy development.
  • Asset-level decarbonization planning.
  • Portfolio optimization across multiple assets.
  • CCUS planning and feasibility analysis.
  • Geothermal project planning and optimization.
Digital sustainability
Digital sustainability

Business outcomes

  • Improve decision-making with predictive insights.
  • Align sustainability goals with operational performance.
  • Reduce uncertainty in planning and forecasting.
  • Optimize capital allocation for decarbonization initiatives.
  • Accelerate energy transition strategies.
  • Enhance collaboration across technical and business teams.

From disconnected planning to integrated decision-making

Many planning tools operate in isolation, separating sustainability modeling from the technical, asset, and business context needed to operationalize decisions.

We take a more contextual approach by combining emissions data, technical models, and planning workflows to support practical decarbonization decisions.

This means that unlike standalone sustainability tools, our solutions can:

  • Support decisions using relevant technical, asset, emissions, and business data.
  • Generate scenarios that reflect actual asset behavior and planning constraints.
  • Align sustainability goals with production, cost, feasibility, and portfolio priorities.
  • Deliver planning outputs that support practical implementation.

This contextual planning approach helps bridge the gap between sustainability ambition and executable decarbonization pathways.

Built on SLB domain expertise and scalable digital capabilities

Data foundation
Support consistent, governed, and trusted data for emissions forecasting, scenario analysis, and sustainability planning.

Technical and asset context
Use relevant technical, asset, facility, and energy system models where applicable to evaluate options and support better planning decisions.

Planning workflows
Deliver specialized workflows for emissions forecasting, scenario analysis, portfolio planning, and low-carbon project evaluation.

This approach helps scale sustainability planning and reduce siloed decision-making.

Extend your sustainability workflows

Frequently asked questions

What is emissions forecasting?

It involves predicting future emissions based on planning scenarios, asset development plans, and energy usage.

How does scenario modeling support sustainability?

It allows organizations to evaluate different strategies and choose the most effective approach for reducing emissions while balancing cost, feasibility, and performance considerations.

Why is integrated planning important?

It helps ensure sustainability strategies are grounded in realistic technical, commercial, and implementation considerations.

How do digital solutions improve sustainability planning?

They combine relevant data, models, and analytics to provide forecasts, scenario insights, and decision support for sustainability planning.

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Plan smarter, reduce emissions faster

Use predictive modeling and scenario analysis to optimize sustainability strategy, reduce emissions, and support better planning decisions.

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