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.
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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.
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Technology

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