What coreAI Delivers

After proper training—or by utilizing a pre-trained model—coreAI can rapidly generate high-quality geological interpretations, including:

• Facies logs along the wellbore: Identifying and classifying different rock facies based on core image analysis.
• Net-to-gross ratio curves for reservoir characterization: Determining the proportion of reservoir rock capable of storing and transmitting hydrocarbons.
• Vshale logs, indicating shale volume distributions: Assessing the amount of shale present, which impacts reservoir quality and fluid flow.
• Rock property logs at user-specified sampling intervals: Providing insights into porosity, permeability, and lithological variations for better reservoir modeling.

By automating these processes, coreAI empowers geologists and reservoir engineers to make data-driven decisions with greater accuracy and efficiency. The ability to generate reliable subsurface models faster and at lower costs has a profound impact on field development strategies and overall project economics.

How coreAI Works: A Step-by-Step Workflow

The coreAI workflow is designed to be intuitive and user-friendly, making it accessible even to those without extensive AI expertise. It follows a structured approach:

1) Data Conditioning of Core Photos

• Before analysis, raw core images undergo preprocessing to enhance their quality.
• Noise and non-geological features (such as drill marks, shadows, and artifacts) are filtered out.
• AI and ML algorithms refine and standardize the data for optimal interpretation.
• Image segmentation techniques are applied to delineate rock features, including grain boundaries, lamination, and fractures.

2) Training AI Models for Core Interpretation

• AI models are trained using expert-annotated core data, focusing on facies, depositional environments, and bedding structures.
• The system learns from a limited number of manually interpreted cores, improving its ability to recognize patterns and geological features.
• Deep learning algorithms analyze image textures, colors, and structural features to differentiate between various lithologies.

3) Quality Control through Blind Testing

• To ensure model accuracy, the trained AI is tested on unseen core samples.
• This step helps validate the reliability of the model before applying it to a broader dataset.
• Cross-validation techniques ensure that the AI performs consistently across different geological settings.

4) Predicting Facies and Rock Properties in Cored Intervals

• Once validated, the AI models analyze all core photos within the available well intervals.
• The system generates facies logs, property logs, and other key geological interpretations.
• Advanced algorithms detect features such as grain size variations, cementation patterns, and porosity distribution.

5) Integrating Core Data with Conventional Well Logs

• ML algorithms are applied to correlate conventional well logs (e.g., gamma ray, neutron porosity) with core-derived logs from Step 4.
• This integration improves the prediction of rock properties across wells with incomplete core data.
• AI-driven interpolation techniques allow for more accurate reservoir characterization in data-limited scenarios.

6) Predicting Facies and Properties in Wells without Core Data

• After training on core-photo-derived logs, the ML models are applied to wells that lack physical core samples.
• This enables the generation of facies and property logs for uncored wells using only conventional well log data.
• The ability to extrapolate core-based interpretations to non-cored wells enhances field-wide reservoir understanding and reduces exploration risks.

Why Choose coreAI?

1) Cost-Effective and Scalable

• Traditional core interpretation is expensive and time-intensive, often requiring expert geological input.
• coreAI automates this process, significantly reducing costs while scaling analysis across multiple wells and reservoirs.
• The reduction in laboratory expenditures allows operators to allocate resources more efficiently.

2) Enhanced Accuracy and Consistency

• By eliminating human bias and subjectivity, coreAI ensures consistent and reproducible results.
• The AI-driven approach enhances the precision of reservoir models, leading to more reliable predictions.
• Unlike manual interpretations that may vary between geologists, AI ensures a standardized workflow across projects.

3) Faster Decision-Making for Hydrocarbon Extraction

• With coreAI, geologists and reservoir engineers can access high-quality rock property predictions in a fraction of the time required by traditional methods.
• This accelerated workflow helps operators make informed decisions for hydrocarbon extraction, ultimately optimizing production strategies.
• The rapid turnaround time enables real-time decision-making during drilling and completion operations.

4) Integration with Existing Geological Workflows

• coreAI seamlessly integrates with subsurfaceAI’s other packages, such as SBED and ReservoirStudio, in the same visualization environment and share the same database. coreAI results can be easily utilized as input to other industry-standard software platforms used in geological and petrophysical analysis.
• The compatibility with various data formats ensures easy adoption without disrupting existing workflows.
• Users can customize model parameters to align with specific reservoir characteristics.

Conclusion

The integration of AI and ML into core interpretation represents a game-changing advancement for the oil and gas industry. coreAI provides a revolutionary solution by automating core analysis, reducing costs, and delivering accurate geological insights. By bridging the gap between traditional core analysis and modern AI-driven workflows, coreAI empowers operators to build better reservoir models, enhance hydrocarbon recovery, and improve overall decision-making.

With coreAI, the future of subsurface analysis is not just automated—it’s smarter, faster, and more efficient than ever before. The ability to harness AI for core interpretation represents a paradigm shift in how the industry approaches reservoir characterization, enabling companies to maximize their assets while minimizing costs and operational risks. As the energy sector continues to embrace digital transformation, coreAI stands at the forefront of innovation, providing a powerful tool for the next generation of geoscientists and engineers.

The era of AI-driven geological interpretation has arrived, and with coreAI, operators can unlock unprecedented insights into their reservoirs, driving efficiency, productivity, and sustainability in upstream operations.