The Client

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. 

Background and Key Challenges/Requirements:

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.  Uncertainty quantification is very crucial for making informed decisions regarding exploration, production, and investments. The Client was facing the following challenges,

Data Overload: With the organizations inundated with massive data, it was difficult to garner actionable insights, thereby obstructing strategic decision-making.

Reservoir Modelling Inaccuracy:  The existing reservoir models were unable to provide accurate oil reserve predictions leading to operational inefficiencies.

Goals:

The client wanted to improve its uncertainty quantification and get empowered with data-driven insights for strategic decision-making.

Data Integration:  It became important to streamline data management and integration to provide decision-makers with real-time access to critical data and inputs.

Accurate Reservoir Predictions:  It was critical to develop a reliable reservoir modeling system to ensure precise estimation of oil reserves.

Common Analytics Platform:  There was a need to design, supply, and install hardware and software components for implementing Descriptive Analytics and Prescriptive Analytics along with Real-time Streaming Analytics.

The Solution: Inspira Enterprise Approach:

Inspira, the global Cybersecurity, Data Analytics, and AI services provider was entrusted with the task of transforming the Client’s operations.

Data Integration:  Inspira harmonized the diverse data sources within the Client’s organization enabling seamless data flow and real-time access to data and information for the benefit of decision-makers.

Implementation of a Common Enterprise Analytics Framework:  This was done for functional areas of Exploration, Production, and Reservoir Management along with cross-functional ones such as Finance, HR, Sustainability, Contracts, and Materials across the Client’s

organization.  This platform served as a robust umbrella system, consolidating existing domain-specific analytical tools.  It established direct interfaces with multiple data sources throughout the Client’s organization creating a holistic, cross-domain integrated data repository that catered to a wide spectrum of analytics needs ranging from Machine Learning (ML) to descriptive, diagnostic, predictive, and prescriptive analytics.

Advanced Data Analytics:  By leveraging data analytics and machine learning techniques, Inspira created advanced predictive models for reservoir performance, enhancing the accuracy of oil and gas reserve predictions.

The multifaced approach that was leveraged to achieve the goals included,

1. Algorithmic Expertise: Various machine learning algorithms were harnessed, ensuring a comprehensive and effective analytical framework.
2. Data Preprocessing: Recognized as a pivotal step, meticulous data preprocessing was methodically executed to lay the foundation for accurate results.
3. Model Deployment: Different machine learning models were deployed to analyze the refined data.
4. Optimization Strategies: Advanced optimization techniques were employed to fine-tune model accuracy.
5. Comparative Analysis: In the final phase, a comparative analysis of the models was conducted, and the model yielding the most exceptional results was chosen for implementation.

Key Highlights:

1. Built-In Analytical Capabilities: From its inception, this revolutionary system has come fully equipped with a comprehensive suite of analytical capabilities. Users can seamlessly access an array of analytical tools without the need for additional installations or configurations.
2. Data Source Flexibility: The platform’s versatility allows for easy configuration of connectors to various data sources, streamlining the process of data retrieval from these sources. This ensures that decision-makers have real-time access to critical information.

Use Case-1

Data-Driven Top-Down Reservoir Modelling

KPIs

• Interactive Maps: Interactive maps showcase remaining oil saturation, enhancing decision-making processes.
• Optimized Field Planning: Suggested data-driven field plans with the optimal placement of infill oil wells and water injection wells, strategically located in the sub-surface for enhanced efficiency by ranking opportunities for Exploration.
• Sensitivity Analysis: Rigorous sensitivity analysis is conducted on developed models, ensuring robust and adaptable reservoir management strategies. 

Visualization Modules

• Well Module: Visual Dashboard that shows Detailed information on an asset level about the wells. Each widget displays information about the well which enhances faster business decisions.
• Reservoir Module: The reservoir module contains a visual dashboard that displays key metrics related to the Reservoir. The properties of the reservoir are displayed on the reservoir module which gives overall information about the field for pro-active business decisions.

Use Case-2

Drilling Data Analytics

Machine Learning analytics is set to transform critical aspects of our operations, focusing on two key deliverables:

1. ROP Optimization: Streamlining the Rate of Penetration (ROP) for more efficient drilling processes.

KPIs

• Rate of Penetration (ROP) Increase: Measure the percentage increase in ROP after implementing machine learning-driven optimization techniques.
• Downtime Reduction: Track the reduction in downtime due to fewer drilling disruptions or issues.

1. Stuck Pipe Detection: Enhancing detection mechanisms to alert and prevent pipe blockages.

KPIs

• Early Warning Success: Assess the effectiveness of the machine learning model in providing early warnings before a pipe becomes stuck.
• Operational Continuity: Monitor the ability to maintain continuous drilling operations without disruptions due to stuck pipes.

Benefits of the Solution:

This forward-looking approach underpinned the commitment to operational excellence and harnessed the full potential of machine learning to drive efficiency, precision, and innovation in the business.  This yielded several benefits to the client.

• Enriched Reservoir Modelling: These new reservoir models provided more accurate predictions, resulting in more accurate estimation of oil and gas reserves, which led to informed Decision-Making.
• Cost Reduction: The Client could achieve significant cost savings, by optimizing, drilling and exploration operations based on more accurate reservoir predictions.
• Competitive Edge: The Client could stay ahead of the curve with data-driven decision-making where they could anticipate industry challenges and remain a domain leader.