Exploring Workflow Approaches for Core Tops Curation

Introduction: Why Workflow Design Matters for Core Tops Curation

Core tops curation — the process of selecting, describing, and archiving representative intervals from drill cores — is a foundational step in subsurface analysis. Yet, many organizations treat it as an afterthought, relying on ad-hoc methods that lead to inconsistent data, lost context, and costly reinterpretations. Based on patterns observed across numerous projects, we argue that investing in a deliberate workflow approach is not a luxury but a necessity. A well-defined workflow ensures that every core top is captured with consistent metadata, that quality control is systematic, and that the resulting dataset is ready for integration with logs, seismic, and reservoir models.

Consider a typical scenario: a geologist logs a core manually on paper, then transcribes notes into a spreadsheet. Another team member digitizes photos. Months later, when a third party needs to re-examine a specific interval, the original description is missing or ambiguous. Such fragmentation is common and costly. This guide explores three primary workflow approaches — manual, semi-automated, and automated — and provides a framework for choosing and implementing the right one for your team. We focus on conceptual comparisons and process design, avoiding vendor-specific recommendations. The goal is to equip you with the criteria to evaluate your own curation pipeline and identify areas for improvement.

Core Concepts: Understanding the Curation Pipeline

To design an effective workflow, we must first understand the core tops curation pipeline as a sequence of stages: acquisition, description, classification, archival, and retrieval. Each stage presents choices that affect data quality and team efficiency.

Acquisition

This stage involves physically selecting core intervals for curation. Decisions include sampling density (every foot or only at key boundaries), preservation method (wrapping, resin-coating), and documentation (photo scales, depth markers). A manual workflow might rely on the geologist's judgment alone, while an automated system could use core scanner imagery to flag anomalies.

Description

Here, the curator records lithology, sedimentary structures, bioturbation index, and other features. Consistency is a major challenge. In manual workflows, descriptions vary between geologists. Semi-automated approaches use standard templates or picklists to enforce uniformity. Full automation uses machine learning to classify textures from images, but requires validation.

Classification

Core tops are grouped into rock types or facies. This step often ties to a company's internal classification scheme. The workflow must ensure that each top is assigned a code that maps to a standard database. Discrepancies here propagate into petrophysical models.

Archival

Physical core tops need to be stored in a way that allows retrieval. Digital archival includes photos, descriptions, and location metadata. A robust workflow includes a chain-of-custody log to track sample movement.

Retrieval

Finally, when a user needs a specific top, the workflow must support rapid search and physical access. This stage often fails when metadata is incomplete or the storage system is disorganized.

Understanding these stages helps us evaluate where different workflow approaches add value or create bottlenecks. Many teams find that the most impactful improvements come from standardizing description and classification, as these are the most subjective stages.

Comparing Three Workflow Approaches: Manual, Semi-Automated, and Automated

Each curation approach has distinct trade-offs in terms of cost, consistency, and scalability. Below, we compare three common approaches across key criteria.

As the table shows, no approach is universally superior. Manual workflows offer maximum flexibility for small projects or complex geology. Semi-automated approaches balance consistency and cost, making them suitable for most routine projects. Automated workflows excel in high-volume, standardized environments but require significant upfront investment and expert oversight.

When to Choose Manual

Manual curation is appropriate when core volumes are low (e.g., less than 500 feet per project), when geology is highly variable and requires expert judgment, or when the team is small and cannot invest in software. The risk is that inconsistent descriptions can lead to misinterpretations later.

When to Choose Semi-Automated

This is the most common choice for mid-sized projects (500–5,000 feet). Tools like digital core logging software with picklists and photo integration reduce data entry errors while allowing the geologist to override automated suggestions. Teams often start here and later add automation for specific tasks.

When to Choose Automated

Large-scale projects (over 5,000 feet) within organizations that have standardized rock-type classifications benefit most from automation. Example: a major operator with a consistent facies scheme can use hyperspectral imaging to classify core intervals, with geologists validating a random 10% sample. However, automation struggles with highly heterogeneous formations or when the classification scheme changes mid-project.

Step-by-Step Guide to Designing Your Curation Workflow

Designing a curation workflow that fits your team's constraints requires a structured approach. Follow these steps to move from ad-hoc practices to a repeatable process.

Step 1: Audit Your Current Workflow

Map out every step from core arrival to final archival. Interview team members to identify pain points. Common issues include missing depth tags, inconsistent lithology codes, and photos that don't match core intervals. Document these as requirements for your new workflow.

Step 2: Define Data Standards

Agree on a single set of classification codes, measurement units, and metadata fields. This is often the hardest step because it requires consensus. Use an industry standard like the AAPG's Core Description Guidelines as a starting point, but adapt to your local formations. Without standards, even the best workflow will produce chaotic data.

Step 3: Choose Your Approach

Based on the audit, decide which stages benefit most from automation. For example, if photo capture and depth matching are the main bottlenecks, invest in a core scanner with automated depth registration. If description consistency is the issue, implement a picklist-based logging tool. Use the comparison table from the previous section to guide your decision.

Step 4: Implement and Train

Roll out the new workflow in phases. Start with a pilot project to test the tools and procedures. Provide hands-on training and create a quick-reference guide. Emphasize that the workflow is a tool, not a straitjacket — geologists should still use judgment when automated suggestions seem wrong.

Step 5: Build Quality Control into the Process

Designate a curation lead who reviews a subset of new entries. Use automated checks for missing fields, depth gaps, and code mismatches. Schedule periodic audits to ensure compliance. QC should happen before the data enters the corporate database, not after.

Step 6: Establish a Feedback Loop

After each project, hold a brief retrospective. What worked? What was confusing? Update the workflow and standards accordingly. Curation is not a one-time design; it evolves with your team's experience and technology.

This step-by-step process helps you avoid the common mistake of buying software before understanding your needs. By starting with an audit, you ensure that technology serves your workflow, not the other way around.

Real-World Examples: How Teams Optimized Their Curation

Hypothetical scenarios based on common industry patterns can illustrate how different choices play out in practice.

Example 1: A Small Consultancy Goes Semi-Automated

A 5-person geotechnical firm handling a mining project with 1,200 feet of core initially used manual logging on paper forms. After noticing that two geologists described the same interval differently, they adopted a tablet-based logging app with predefined rock-type picklists and depth-matched photo capture. The new workflow reduced description time by 30% and eliminated mismatches in facies codes. However, they kept manual descriptions for intervals with complex structures because the app's templates were too rigid. This hybrid approach gave them consistency where it mattered most while preserving flexibility.

Example 2: An Energy Company Scales with Automation

A mid-sized operator drilling 15 wells per year with over 20,000 feet of cumulative core faced a bottleneck in description. They implemented a core scanning line that produced high-resolution images and hyperspectral data. A machine learning model classified intervals into predefined facies, and geologists validated a 15% sample. Within six months, the turnaround for core descriptions dropped from 4 weeks to 1 week. However, the model struggled with a new formation that contained igneous textures not in the training set, requiring a manual re-log of those intervals. The lesson: automation works best for stable, well-characterized lithologies.

Example 3: A Research Institute Prioritizes Flexibility

A university lab curating cores for multiple research projects chose a fully manual workflow with detailed photography and open-ended description fields. Their volumes were low (under 500 feet per year), but each project required different classification schemes. Automated tools would have forced a standardization that conflicted with research needs. Instead, they invested in proper archival — temperature-controlled storage and a searchable metadata database — ensuring that physical tops could be retrieved years later. This approach prioritized preservation over processing speed.

These examples show that the best workflow depends on volume, consistency requirements, and organizational culture. The key is to match the approach to the specific constraints of your work.

Common Questions and Concerns

Teams often raise similar questions when evaluating workflow changes. Here are answers to the most frequent concerns.

Will automation replace the geologist's role?

No. Automation handles repetitive classification and data entry, but expert judgment is still needed for anomalous intervals, complex sedimentary structures, and quality control. Geologists shift from manual logging to interpretation and validation — a higher-value role.

How do we convince management to invest in a new workflow?

Focus on the cost of poor curation: reinterpretation time, lost data, and misinformed decisions. Use a pilot project to demonstrate before/after metrics like description time or error rate. Emphasize that a standardized workflow improves data trust across the organization.

What if our team is geographically distributed?

Cloud-based curation platforms allow remote teams to log cores using shared standards. However, ensure that physical cores are handled consistently by providing detailed SOPs and periodic training. Virtual audits of photos and descriptions can help maintain quality.

How do we handle legacy core tops?

Prioritize new curation first, then plan a phased back-curation of older cores. Use a simplified version of your workflow for legacy data, focusing on critical metadata like depth and basic lithology. Avoid trying to fully reconcile old and new data — accept that older records have higher uncertainty.

What is the biggest mistake teams make?

The most common mistake is skipping the audit and jumping straight to tool selection. Teams buy software without understanding their pain points, leading to a workflow that automates the wrong steps. Start with people and processes, then choose tools.

Conclusion: Building a Sustainable Curation Practice

Core tops curation is not merely a clerical task; it is a scientific process that safeguards the value of physical samples. The workflow approach you choose — manual, semi-automated, or automated — should align with your team's size, project volumes, and geology. There is no one-size-fits-all solution, but the principles are universal: standardize data, embed quality control, and iterate based on feedback.

We recommend starting with a thorough audit of your current process, identifying the stages where inconsistency or inefficiency costs the most. Then, pilot a new workflow on a single project before rolling it out broadly. Invest in training and documentation to ensure adoption. Finally, treat your curation workflow as a living system that evolves with technology and team experience.

By taking these steps, you will build a curation practice that produces reliable, auditable data — data that supports better interpretations, reduces risk, and ultimately saves time and money. Remember that the goal is not perfection, but continuous improvement. Every step toward a more structured workflow is a step toward higher quality subsurface understanding.