Economic Oil Well Production Decision Modeling for a Multinational Energy Company

Challenge

The multinational oil and gas client faced complex decision-making challenges around bringing oil wells online for production. Critical considerations included:

• Economic Factors: Oil price forecasting, operational costs, and financial returns (NPV & IRR).
• Reservoir and Well Performance: Reservoir quality, production rates, decline curves, and recovery potential.
• Infrastructure and Logistics: Accessibility, capacity constraints, and proximity to existing infrastructure.
• Regulatory and Environmental Compliance: Permitting, regulatory approvals, and environmental impact.
• Operational Risk Management: Geological, technological risks, and workforce availability.
• Strategic Considerations: Alignment with production targets, budget constraints, and portfolio optimization.

Previously, the client relied on disparate, decentralized spreadsheets, causing repetitive work, inefficiencies, and limited capacity for comprehensive sensitivity analysis.

Solution

The Mahusai Global team delivered a sophisticated, scalable, cloud-based economic modeling platform designed for robust, real-time analysis and collaboration:

• Integrated Economic Models: Consolidated dozens of complex offline spreadsheets into a single, centralized application, offering seamless data integration and management.
• Advanced Forecasting and Analytics: Incorporated predictive analytics, scenario modeling, and sensitivity analysis features to dynamically forecast oil price fluctuations, production rates, and operational costs.
• Real-time Collaboration: Facilitated simultaneous access and real-time collaboration capabilities, allowing geographically dispersed teams to align decisions rapidly and efficiently.
• Interactive Dashboards and Reports: Implemented intuitive, interactive visualization dashboards and reporting tools, enabling stakeholders to easily interpret and analyze complex financial and operational data.
• Compliance and Audit Trails: Provided built-in auditing functionalities and traceable documentation, significantly enhancing regulatory compliance and transparency.
• AI-driven Recommendations: Integrated machine learning models to generate predictive insights and actionable recommendations, enhancing decision accuracy and strategic alignment.

Technology

• Frontend: React, Electron
• Backend Framework: Django REST
• Programming & Analytics: Python, JupyterHub
• Cloud Infrastructure: Microsoft Azure

Results

The implementation of this economic modeling solution delivered immediate, impactful improvements:

• Increased decision-making speed and accuracy by eliminating redundant spreadsheet-based processes.
• Enhanced collaboration among teams, enabling faster and better-informed strategic decisions.
• Improved compliance, transparency, and ease of reporting, reducing operational risks.

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