Energy · Global

Oil & gas leader quantifies reservoir uncertainty with ML.

One enterprise analytics platform sharpens reserve prediction and cuts drilling downtime.

// Client overview

Being a leader in the oil and gas sector, the client's operations span exploration, drilling, extracting, refining, and distribution that are part of upstream, midstream, and downstream activities. The organization operates in diverse geographic locations across various fields.

The oil and gas industry operates in a highly complex and unpredictable environment. Discovering and extracting oil and gas reserves is not only complicated but costly too. Geological uncertainties often make it challenging to accurately predict reserves and reservoir performance, and uncertainty quantification is crucial for making informed decisions on exploration, production and investment.

Sector

Energy

Region

Global

Engagement

Cyber transformation

Services

AI & Data Analytics · Data engineering · Machine learning modelling

Technologies

Machine learning · Predictive analytics · Real-time streaming analytics · Enterprise data repository

// Key challenges

Data overload - with the organization inundated with massive data, it was difficult to garner actionable insights, obstructing strategic decision-making.

Reservoir modelling inaccuracy - the existing reservoir models were unable to provide accurate oil reserve predictions, leading to operational inefficiencies.

Data integration - data management and integration had to be streamlined to give decision-makers real-time access to critical data and inputs.

Accurate reservoir predictions - a reliable reservoir modelling system was needed to ensure precise estimation of oil reserves.

Common analytics platform - hardware and software components had to be designed, supplied and installed for descriptive, prescriptive and real-time streaming analytics.

// The solution · highlights

Data integration - Inspira harmonized the diverse data sources within the client's organization, enabling seamless data flow and real-time access for decision-makers.

Implementation of a common enterprise analytics framework across exploration, production and reservoir management, plus cross-functional areas such as finance, HR, sustainability, contracts and materials - a robust umbrella system consolidating existing domain-specific analytical tools.

The platform established direct interfaces with multiple data sources to create a holistic, cross-domain integrated data repository serving everything from machine learning to descriptive, diagnostic, predictive and prescriptive analytics.

Advanced data analytics - by leveraging analytics and machine learning, Inspira created advanced predictive models for reservoir performance, enhancing the accuracy of oil and gas reserve predictions.

Algorithmic expertise - various machine learning algorithms were harnessed, ensuring a comprehensive and effective analytical framework.

Data preprocessing - recognized as a pivotal step, meticulous preprocessing was methodically executed to lay the foundation for accurate results.

Model deployment and optimization - different machine learning models were deployed against the refined data, then fine-tuned with advanced optimization techniques.

Comparative analysis - the models were compared and the one yielding the most exceptional results was chosen for implementation.

Use case 1, data-driven top-down reservoir modelling - interactive maps showing remaining oil saturation, data-driven field plans with optimal placement of infill oil wells and water injection wells, and rigorous sensitivity analysis on the developed models.

Visualization modules - a well module giving asset-level detail on each well, and a reservoir module dashboard displaying key reservoir metrics and properties across the field.

Use case 2, drilling data analytics - ROP optimization to streamline the rate of penetration, and stuck-pipe detection to alert and prevent pipe blockages before they halt operations.

// Outcome & benefits

Enriched reservoir modelling - the new models provided more accurate predictions, resulting in more accurate estimation of oil and gas reserves and better-informed decision-making.

Cost reduction - significant cost savings by optimizing drilling and exploration operations against more accurate reservoir predictions.

Competitive edge - data-driven decision-making let the client anticipate industry challenges and remain a domain leader.

Built-in analytical capabilities - the platform shipped with a comprehensive suite of analytical tools, with no additional installation or configuration needed.

Data source flexibility - connectors to various data sources are easily configured, giving decision-makers real-time access to critical information.

// By the numbers · ROI

Rate of penetration (ROP) increase measured after ML-driven optimization

Downtime reduction from fewer drilling disruptions

Early warning of stuck-pipe events before operations halt

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