A leading global oil & gas services provider sought to accelerate well drilling planning by making better use of extensive historical drilling data. Engineers relied on manual analysis to identify relevant offset wells and determine optimal drilling parameters, making planning time-intensive and dependent on specialist expertise.
Coforge developed an AI-powered advisory system that brings together historical well data, AI/ML, engineering knowledge, physics-based modeling, and human-guided decision-making. The solution automates historical data evaluation and offset well selection, provides drilling parameter recommendations, and creates an explainable evidence trail—helping reduce well planning cycles from weeks to hours.
Transformation Timeline
Drag
The Challenge
Well engineers needed to analyze extensive historical drilling information when identifying offset wells and developing plans for new wells. However, critical evidence was fragmented across multiple data platforms, Daily Drilling Reports (DDR), and telemetry systems, making analysis manual, time-consuming, and highly dependent on specialist knowledge.
Formation, BHA, bit, mud, trajectory, ROP, shock & vibration, and failure information were also not consistently compared at the same level of detail. This made it difficult to trace recommendations and exclusions back to source evidence, engineering rules, thresholds, and data quality.
With drilling costs reaching up to US$500K per day, longer planning cycles, and variable drilling decisions increased exposure to operational risk and non-productive time. The organization needed a governed and explainable approach to convert historical drilling evidence into actionable recommendations while systematically retaining knowledge for future wells.
Our Approach
AI-Powered Well Planning
Coforge developed an AI-powered advisory system that automates the evaluation of historical drilling data and helps engineers identify relevant offset wells. Offset selection considers geological, mechanical, operational, and performance characteristics, while similarity scoring helps identify historical wells most relevant to the proposed drilling scenario.
The system generates recommendations for critical drilling parameters, including Weight on Bit (WOB) and RPM, while maintaining human-in-the-loop decision-making so engineers retain control over final planning decisions.
Unified Historical Drilling Intelligence
Coforge curated historical information spanning wells, sections, formations, depths, bits, mud types, drilling incidents, and other drilling parameters.
High- and low-frequency drilling data from multiple systems were integrated to enable more comprehensive analysis and to establish a reusable historical knowledge foundation for future well planning.
Knowledge Graph & Explainable AI
A graph-based knowledge architecture was established to separate reusable enterprise drilling knowledge from scenario-specific decision intelligence.
Recommendations were connected to underlying offset wells, engineering rules, thresholds, data quality, and lineage, enabling engineers to trace recommendations back to their supporting evidence. Graph traversal also created the foundation for natural-language interaction with historical drilling knowledge through LLM-powered experiences.
Agentic Decision Intelligence
Coforge implemented a multi-agent architecture that combines engineers' inputs with historical drilling data to produce intermediate analyses and final recommendations.
An Advisor Agent interprets engineering intent and applies governed policies, while the Knowledge Graph performs evidence-based traversal before the LLM generates a grounded response. This helps keep AI-generated recommendations explainable, auditable, and grounded in engineering evidence.
Partner / Technology Ecosystem
The AI-powered advisory system brings together Azure AI Foundry, Databricks, Neo4j Knowledge Graph, domain-oriented AI agents, AI/ML, physics-based modeling, Generative AI/LLM, and data engineering to create a governed, explainable, well-planned environment.

Impact to Date
50%
Weeks → Hours
Deeper Historical Intelligence
Business Impact
- Accelerated well drilling planning through automated historical data evaluation and AI-powered recommendations
- Improved offset well selection using geological, mechanical, operational, and performance characteristics
- Enabled more consistent use of historical drilling evidence across new well-planning scenarios
- Improved decision traceability by connecting recommendations to source evidence, engineering rules, thresholds, and data lineage
- Preserved drilling knowledge in a reusable foundation to support future well planning
- Enabled human-guided, explainable AI recommendations while retaining engineering oversight
