Volatility in the markets, weather-driven demand fluctuations, and operational uncertainties have increased the need for frequent and scalable scenario planning within the oil and gas industry.
In 2025, Murphy Oil launched a modernization initiative to transform a distributed planning environment into an integrated enterprise analytics ecosystem. The initiative increased scenario simulation capacity by five to 10 times, reduced decision cycle time, improved data integrity, enhanced cross-functional transparency, and established a single source of truth for planning data.
To address structural constraints in the legacy environment, the planning and performance team initiated a data modernization program, designed to be an accessible foundation for forecast data. The initiative consolidated information across the organization, automated manual workflows, and standardized business logic previously maintained in spreadsheets.
The result was a centralized analytical framework, enabling more reliable scenario planning through interactive dashboards with governed data sets. By eliminating inconsistencies and reconciliation bottlenecks, the organization significantly increased its ability to evaluate potential scenarios and support capital allocation decisions.
Modernizing Decision Making
Scenario planning has been a foundational practice in the oil and gas industry, where organizations must regularly evaluate capital allocation options in an environment with constantly changing commodity prices and operational performance.
Murphy’s modernization initiative focused on expanding the scale and accessibility of this capability by developing an integrated analytics ecosystem where data sets and dashboards were standardized and broadly accessible throughout the organization as a singular source of truth. This established a standardized logic and consistent structure across the organization.
Historically, planning workflows relied on spreadsheet-based modeling approaches that remain common across the industry. While effective for analytical modeling, distributed tools can become more difficult to manage as planning complexity increases across assets, teams, and decision cycles.
The modernization initiative addressed this challenge by establishing centralized and governed data architecture. Instead of maintaining independent data sets across teams, the organization implemented a structured data model that supports consistent definitions and standardized planning logic across both offshore and onshore assets.
Modern analytical tools improved automation, scalability, and reliability while maintaining familiar workflows for users. Interactive dashboards allow leadership to explore scenario assumptions dynamically and evaluate the implications of price movements, operational variability, and capital allocation choices.
Research in enterprise decision intelligence suggests organizations that combine centralized data architecture with iterative scenario modeling often outperform peers in both capital efficiency and strategic responsiveness. Murphy’s initiative reflects this broader shift toward scalable, data-driven planning infrastructure.
Planning Infrastructure Evolution
As Murphy’s scenario planning requirements expanded across business units and asset types, the organization identified opportunities to strengthen the underlying planning infrastructure.
Key challenges included:
- Distributed planning data sets managed across multiple teams
- Increasing time spent preparing and validating data for scenario analysis
- Structural differences across offshore and onshore asset data sets
- Independent data set management across planning groups
- Analytical logic embedded across multiple spreadsheet models
These factors did not prevent scenario planning but increased the effort required to prepare and reconcile data before simulations could be executed. Analysts frequently devoted time to data set preparation, validation, and reconciliation before shifting focus to evaluating strategic outcomes.
Given that scenario planning directly informs capital allocation decisions worth millions or billions of dollars, Murphy recognized that improving the scalability and reliability of its planning infrastructure would enhance the organization’s ability to respond quickly to changing market conditions.
Leadership therefore identified an opportunity to strengthen the foundation supporting scenario analysis, enabling more frequent simulations and broader exploration of potential market outcomes.
Development Process
The modernization initiative was designed to establish a controlled, scalable foundation for enterprise scenario planning. The focus was not on redesigning financial models, but on stabilizing the data architecture and formalizing calculation logic to support repeatable, high-frequency simulation.
The first step involved consolidating projection data sets into a governed relational structure. Authoritative data sources were identified, structural inconsistencies were resolved, and standardized definitions were implemented across business units and asset types. This established consistent data governance and version control.
Next, planning logic previously maintained in spreadsheets was centralized into controlled calculation layers. Core transformations, scenario drivers, and mapping structures were formalized within a database environment. Power BI served as the enterprise visualization layer, providing interactive, parameter-driven dashboards that supported scenario comparison and executive review. Excel was retained in a limited, structured role for controlled inputs and exception handling, preserving user familiarity while ensuring core calculations remained centralized.
Key dashboards were created and delivered over the course of the annual planning cycle to support each step of the initiative. Additional time was spent with stakeholders to deliver dashboard visualizations that were aligned with capital allocation workflows, ensuring outputs were decision-oriented rather than purely analytical.
The resulting framework separates logic from presentation, reduces reconciliation effort, and enables scalable scenario iteration while maintaining strong governance and transparency.
Results
The modernization initiative materially expanded Murphy's scenario planning capacity.
Scenario development time was reduced from 5 to 7 days to less than a day, an improvment of five to 10 times in scenario iteration speed. This increase was achieved without a proportional increase in analytical resources, as automation reduced manual data preparation, reconciliation, and formula validation efforts.
Centralizing data sets and calculation logic significantly reduced version-control risk and improved consistency across business units. Decision makers gained access to standardized scenario outputs with automated variance analysis through structured dashboards, enabling direct comparison of price sensitivities, production trade-offs, and downtime impacts.
As a result, capital allocation discussions shifted from reconciling data discrepancies to evaluating strategic implications. The improved simulation cadence enhanced the organization’s ability to quantify upside and downside exposure in volatile market conditions, directly supporting faster and more informed capital planning decisions.
Planning effectiveness is closely tied to iteration frequency and data reliability. The transition from spreadsheet-driven workflows to a centralized analytics framework materially improved both dimensions. By separating data storage, transformation logic, and visualization layers, the organization reduced systemic error risk while increasing transparency in how forecasts were calculated.
Future Opportunities
The framework establishes a scalable foundation for expanding enterprise decision intelligence.
Future development opportunities include
- Expanding automation pipelines to enable more frequent data set refresh cycles.
- Incorporating additional scenario dimensions such as probabilistic modeling and range-based forecasting.
- Expanding included data sets to include a broader range of key performance indicators and benchmark peer performance.
Completing these enhancements will require additional data pipelines and expanded mapping structures. As the platform evolves, it can extend beyond scenario planning to support broader decision intelligence capabilities that connect planning, performance monitoring, and strategic evaluation.
Conclusion
Murphy's modernization of its scenario planning framework demonstrates the value of scaling enterprise decision intelligence through centralized data architecture and automated analytics.
By consolidating fragmented data, standardizing business logic, and implementing a scalable analytics ecosystem, the organization increased simulation throughput by 5–10x while improving transparency and data reliability.
The initiative illustrates a replicable model for enterprise data modernization:
- Governed data foundations
- Automated transformation pipelines
- Standardized logic layers
- Interactive, executive-ready visualization
As volatility continues to shape global energy markets, the ability to evaluate scenarios quickly and consistently is increasingly central to maintaining capital discipline and strategic agility.
Murphy’s experience illustrates how modern analytics infrastructure can strengthen scenario-based decision making and support more responsive capital planning in an uncertain operating environment.
Brandon Rogers, SPE, is a senior strategic planning manager for Murphy Oil with over 14 years of experience across some of North America's most active unconventional plays, including the Eagle Ford Shale, and the Duvernay, and Montney formations. His career spans reservoir engineering, operations, and business development, giving him a full-cycle view of asset value. As a reservoir engineer, Rogers has built a track record of technological innovation in refracturing and enhanced oil recovery, advancing approaches that extend the productive life of mature unconventional wells. He is a coauthor of Applied Petroleum Reservoir Engineering, Third Edition, a widely used reference text in the discipline. Rogers also brings strong operations management and facilities infrastructure experience and has led business development efforts in both traditional oil and gas mergers and aquistions and energy transition opportunities.
Laura Naaykens, SPE, is a managing partner, Canada, at Chirality Research. She has more than 15 years of experience in the energy industry, with a background in production and operational analytics, data science, digital transformation, and project delivery.
Brian Chen has been a data scientist at Chirality Research since 2021, building enterprise applications for various upstream oil and gas operations. As the firm’s lead Power Platform developer, he focuses on data pipeline automation and Power BI analytics, including health, safety, and environment key performance indictors reporting and a policy-enforcing flight reservation system. He holds a Bsc in computer science from Rice University in Houston.