SPE GCS 2026 ML Challenge - Building
an Agentic AI System for Operational
Intelligence
Introduction
Drilling a well for oil and gas is a complex engineering activity. During drilling, large amounts of
data are generated. This includes numerical measurements such as depth and rate of
penetration, as well as written daily reports prepared by engineers at the rig site.
Engineers must combine these different types of information to understand what is happening,
detect problems, evaluate performance, and decide what actions to take next.
In this challenge, your task is to build an intelligent AI agent that can read drilling data and
reports, reason about them, and answer operational questions in a clear and evidence based
way.
The goal is not only to predict values. The goal is to explain what happened, why it happened,
and what are the potential next steps.
Aim of the Challenge
The aim of this challenge is to design an AI system that can combine structured data, written
reports, and domain knowledge to generate operational insights.
Your system should be able to:
• Understand drilling operations
• Identify drilling phases and activities
• Analyze performance and efficiency
• Evaluate drilling configurations
• Explain operational issues
• Provide decision support
The focus is on reasoning, clarity, and evidence based conclusions.
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Data That Will Be Provided
Participants will receive extracted data from the public Equinor Volve Field dataset through a
shared repository.
The provided data will include:
1. Well metadata
This includes basic information about wells such as well name, sections drilled, and
configuration information.
2. Drilling data samples
This includes structured time based or depth based measurements such as:
• Depth
• Rate of penetration
• Rotation speed
• Torque
• Pump pressure
• Flow rate
• Hookload or weight on bit
3. Daily drilling reports
These are written reports prepared by engineers. They describe what activities were performed
during the day, what problems occurred, and what actions were taken.
4. Volve documentation
This includes supporting documents that explain the dataset and provide background
information.
The data will be provided in raw form. There will be no predefined drilling phase labels, no event
tags, and no performance ratings. Participants must interpret and structure the data
themselves.
Open Knowledge Sources You May Use
Participants are encouraged to use publicly available reference material as a knowledge base.
This material is not curated or simplified. It must be retrieved and interpreted by your system.
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Examples of public knowledge sources include:
• Schlumberger Oilfield Glossary
This explains drilling terminology such as rate of penetration, tripping, circulation, and
non-productive time.
• SPE PetroWiki
This contains articles explaining drilling concepts, tools, and operational practices.
• IADC drilling terminology documents
These explain standard drilling acronyms and definitions.
• General engineering references related to drilling and well construction.
You may use these sources to help your system understand domain terms and concepts.
What Your System Must Do
Your system should function as an intelligent agent. It should be able to answer operational
questions using both numerical data and written reports.
The types of questions will cover multiple levels of reasoning.
Drilling Phase Identification & Validation
• Identify and label the major drilling phases for <Well Name> over the selected interval,
including the evidence used for each phase.
• Detect significant operational or phase transitions, noting when they occurred and why
they matter.
• Assess how well the inferred drilling phases align with the daily drilling reports.
• Identify periods where the operational state is ambiguous and explain the sources of
uncertainty.
Time & Efficiency Analysis
• Distinguish between productive and non-productive drilling time, and justify the criteria
used.
• Define drilling efficiency for <Well Name> and evaluate how it changes over time.
• Compare overall drilling efficiency between <Well Name> and at least one other well.
• Evaluate whether higher drilling speed was associated with stable operations or
increased operational risk.
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Section & ROP Performance
• Determine which hole section appears easiest to drill and which appears most
challenging, with supporting evidence.
• Analyze how rate of penetration varies across sections and describe notable trends.
• Identify periods of exceptional drilling performance and explain why they stand out.
Configuration & BHA Effectiveness
• Identify the most effective drilling configuration or BHA run and explain the context.
• Assess whether changes in configuration coincide with changes in performance.
• Evaluate configuration effectiveness by hole section.
• Identify configurations that appear robust across operating conditions, as well as those
that underperformed and potential reasons why.
• Assess how daily drilling reports support or contradict conclusions about configuration
effectiveness.
Operational Issues & Root Causes
• Identify key operational issues encountered while drilling <Well Name>.
• Propose likely contributing factors or root causes.
• Analyze whether these issues persisted, resolved, or recurred over time.
• Highlight areas where drilling data and daily reports provide conflicting interpretations.
Synthesis & Recommendations
• Compare the drilling phase distribution of <Well Name> with another well <Well
Name1> and explain key differences.
• Describe remaining uncertainties in the analysis and their potential impact.
• Determine which operational team(s) should be notified based on the findings, and why.
• Produce a concise operational handover summary for the next shift.
• Extract key lessons learned that could apply to future wells.
• Based on observed trends, describe expected performance in a similar section of
another well.
• Recommend a drilling configuration for similar conditions.
• Identify what additional data would most improve confidence in the conclusions.
Expected Output Format
For each question, your system should provide:
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• A clear answer
• Evidence from drilling data
• Evidence from daily reports
• Explanation of reasoning
• Statement of assumptions
• Confidence level or uncertainty
Answers should be understandable to an engineer reviewing your work.
Design Criteria
You may use:
• Open source libraries
• Local language models
• Free tier cloud models
• Statistical analysis methods
• Machine learning models
• Retrieval augmented generation systems
• Tool based agents
You are not required to use any proprietary software.
Your system design should prioritize:
• Transparency
• Traceability of evidence
• Clear reasoning
• Reproducibility
Complexity alone will not be rewarded.
Evaluation Criteria
Evaluation will be based on a structured question set.
Solutions will be assessed based on:
• Quality of reasoning
• Correct and relevant use of evidence
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• Consistency across answers
• Clarity of assumptions
• Handling of uncertainty
• Practical relevance of insights
There is no single correct answer for the questions. Different approaches are acceptable if they
are well justified and supported by evidence.
The evaluation emphasizes reasoning quality rather than matching a specific numeric answer.
Summary
This challenge asks you to build more than a predictive model. It asks you to design an AI system
that can read data, understand context, reason through engineering problems, and
communicate conclusions clearly.
The objective is to explore how intelligent systems can assist real world operational decision
making using raw data and public domain knowledge.
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