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In-memory computing in Oil & Gas

Explore use cases and perspectives

In-memory computing in the oil & gas industry helps companies accelerate business processes, deliver more business intelligence (BI), and simplify their IT environments. By providing the foundation for all of an organization’s data needs, in-memory computing removes the burden of maintaining separate legacy systems and siloed data; reports and analytics can run live and support decision-making in the new digital economy.

Predictive margin-based operations

Real-time oil & gas data optimizes end-to-end product delivery cost

Analytic techniques leveraging in-memory computing in the oil & gas industry focus on predictive, "what-if" modeling of cost impacts and margin contribution potential based on internal sales parameters, operational activities, and external trends. These techniques align operational cost views with financial forecasts/measurement and illuminate cross-functional efficiency opportunities. Data visualization tools and dashboards provide a view of individual customer, product, and region profitability. Real-time data from ERP and subsystems feeds to two-way, communicative mobile devices that provide consistent access to new information.

Predictive oil & gas analytics improve management transparency into progressive cost structures along the value chain and contribute to the ability to proactively manage operations-related margin erosion. Predictive oil & gas analytics also enable segmentation business rules that optimize "perceived value" of a service (versus cost of service) associated with a specific customer segment. In addition, end-to-end operational performance and profitability dashboards foster improved decision-making.

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Well-level revenue accruals

In-memory computing technology in the oil & gas indsutry allows well-level accruals without impacting system performance

In-memory computing technology in the oil & gas industry increases accrual process speed, granularity, and control, allowing employees to perform accruals at an oil well or at field-level rather than make topside-/summary-level entries that cannot compare directly to actuals. Rapid, real-time analyses of high-volume transactions can identify errors, revise estimates, and drill down into problem areas without impacting overall system performance. In-memory computing can also help to reduce the risk of audits or lawsuits.

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Oil drilling machine

Competitive retail pricing

Oil & gas analytics monitor, analyze, and adjust retail fuel prices to match local competitors

Mobile analytics in the oil & gas industry allow regional offices to monitor competitor pricing and sales volumes within seconds and reduce manual intervention in pricing changes. This in-memory solution integrates both structured (ERP, CRM, pricing policies, supply chain management) and unstructured (third-party equipment intelligence) data sources. Custom dashboards and automated reports allow any end user to understand data analysis with little-to-no training.

Reduced manual intervention in changing fuel prices can better support a company’s strategic pricing initiatives (price and margin analytics, deal negotiation, and price optimization), can improve response time to cost fluctuations and other market developments, reduce pricing errors, and increase access to new pricing opportunities.

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Oil barrels

Advanced market and credit risk analytics

Oil & gas companies can run enterprise-level risk analytics with scalable computing

Cloud and in-memory computing technologies enable oil & gas companies to run market and credit risk calculations at the enterprise level instead of in artificial silos driven by computational scaling issues. Timely single-entity analytics improve visibility into potential risks and aid decision making. Enterprise-level analytics for market and credit risk exposures help to uncover natural offsets in the trading portfolio and avoid misguided transactions measured against inappropriate metrics.

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Static electricity

Vendor risk sensing

Structured and unstructured oil & gas data analytics can reduce supplier-related risks

Using oil & gas data analytics to vet vendors and suppliers can reduce a company’s risk of becoming a victim of fraud, experiencing operational disruption due to supplier failure, and inadvertently conducting business with known Office of Foreign Assets Control (OFAC) offenders. Vendors and suppliers are vetted during onboarding and continuously monitored to detect potential issues. This real-time monitoring gives organizations increased transactional transparency and can help mitigate financial, operational, regulatory, and reputational risks related to third-party activities.

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Man in a tunnel

Going deeper

Pacific Drilling positions itself for even greater business growth by implementing SAP Business Suite powered by SAP HANA.

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