— Dr. Nick Peters, Former Global Business Development Director - BGP International; AI Consultant

Executive Summary

Artificial intelligence is now woven into the exploration narrative of every major operator, but the public framing often oversells what AI can do and undersells where it truly creates value. AI does not independently establish a commercially producable reservoir. It does not replace geoscientists. And it does not understand geology, physics, or stratigraphy in any meaningful sense.

Unconstrained machine learning amplifies subjective human biases and struggles to generalize across basins (e.g., from the mature Gulf of Mexico to the Guiana Shield).

Commercial value can improve through human-reviewed workflows and Physics-Informed Neural Networks (PINNs).

What AI does is compress time. It accelerates seismic reprocessing, multiplies the number of geological scenarios that can be tested, and sharpens the decision-making cycle that leads to Final Investment Decision (FID). When deployed with physics constraints and expert oversight, AI becomes a force multiplier for exploration teams and a genuine lever for capital efficiency.

The New Exploration Narrative

Major operators have highlighted AI-driven seismic reprocessing as a contributor to identifying new opportunities in the Guyana–Suriname basin. That statement landed with weight because Guyana is one of the most consequential deepwater provinces of the past decade. But the deeper story is not about AI "finding" oil. It is about operators shifting from manual interpretation to computational acceleration.

For investors underwriting multi-billion-dollar frontier campaigns, the critical question is how to interpret these claims. Does AI fundamentally expand subsurface visibility, or does it simply accelerate workflows that already exist?

The technical literature supports a narrower conclusion. AI is a pattern engine—fast, powerful, and transformative—but it is not a geological oracle. Understanding that distinction is essential for evaluating technical risk in modern exploration portfolios.

What AI Actually Means in Seismic Interpretation

Marine seismic exploration sends acoustic waves into the earth and records the returning echoes to map subsurface structure. Processing this data has always been computationally intense. Historically, removing random marine noise and picking out complex fault networks required months or years of manual effort.

When operators talk about AI-driven reprocessing, they are referring to pattern recognition and acceleration. Convolutional Neural Networks (CNNs) are trained to identify the visual signatures of faults, stratigraphic boundaries, and other features within seismic volumes. Research shows that advanced image-processing and machine-learning techniques can assist in mapping complex structures such as salt bodies and turbidite channels.

But speed is not the same as understanding. AI can accelerate selected interpretation tasks, but its outputs still require independent geological and geophysical validation.

Figure 1: Traditional vs. AI Accelerated Seismic Processing timelines

Where AI Breaks Down

Most commercial machine learning models treat seismic volumes as generic images. They do not understand wave propagation, depositional systems, or geological plausibility. When applied without constraints, they fail in three predictable—and expensive—ways.

Manufactured Structure from Noise

Marine seismic data contains noise and ambiguity. A generic fault-picking model will often interpret acquisition footprint, multiples, or migration artifacts as real structure because it lacks understanding of physics. The output may look confident, but it can frequently produce fractured horizons and impossible fault geometries. Trusting those picks can steer a well toward a target that never existed.

· Automating Human Bias

Machine learning models learn whatever they are trained on—including decades of subjective manual interpretation. Fault networks, stratigraphic boundaries, and geobodies are interpreted differently by different geoscientists, and the variation can be substantial. Training algorithms on these interpretations does not remove bias; it amplifies it. The model becomes a fast, confident version of the interpreter who labeled the training set.

· Failure to Generalize Across Basins

A model trained on clean, well-behaved data from a mature basin will not generalize to a frontier basin with different rock physics, noise characteristics, or stratigraphic styles. It will struggle with subtle stratigraphic traps (responsible for many major discoveries in Guyana) mixed depositional systems, and low-contrast impedance boundaries. In regions where exploration success depends on recognizing these subtleties, a poorly calibrated model may miss or misclassify relevant subsurface features.

Figure 2: Seismic Pitfalls and Mitigation Strategies

Potential commercial value includes faster processing, consistent screening and improved analyst productivity; whether any prospect would have been missed can not be established from the sighted evidence. Reducing cycle time may improve project Net Present Value (NPV) provided it materially advances spend or production cash flows, and additional AI costs do not offset the gain. By compressing the time from seismic acquisition to Final Investment Decision (FID), operators may reduce some carrying costs but a shorter workflow does not necessarily advance regulatory approvals FID, or first oil.

Figure 3: Impact of AI-Driven Timelines on Exploration Capital Efficiency

How Operators Use AI to Empower Geoscientists

A defensible use of AI supports geoscientists rather than treating model outputs as autonomous geological conclusions. They are augmenting geoscientists with computational tools that accelerate interpretation while preserving geological judgment.

Two corrections define the most robust workflows:

· Physics-Informed Neural Networks (PINNs)

Physics-informed neural networks can incorporate governing-equation constraints in selected tasks. They do not guarantee geologically correct structures or remove the need for independent validation.

· Human-in-the-Loop (HITL) Workflows

One possible workflow is AI-generated preliminary interpretation followed by expert validation, error logging, and, where feasible, model updates.

Figure 4: The Modern Human-In-the-Loop (HITL) Workflow

The Key Takeaway

For Guyana, the rise of AI‑driven seismic interpretation presents both opportunity and oversight risk. Operators now have tools that can accelerate prospect maturation, but those same tools can misinterpret noise as structure or fail when applied to frontier geology without proper calibration. This puts the government in a position where it must evaluate claims built on models it cannot independently audit. The solution is not to match the technical sophistication of the operators, but to demand process transparency: requiring companies to demonstrate how their AI models were trained, what basins the training data came from, how physics constraints were applied, and how expert geoscientists validated the outputs. Governments can also mandate multi‑expert blind reviews, basin‑specific calibration tests, and disclosure of uncertainty ranges before accepting AI‑derived subsurface interpretations. In practice, Guyana does not need to “challenge the AI”; it needs to challenge the workflow. A proportionate technical assurance framework that focuses on validation, provenance, and geological plausibility will allow Guyana to benefit from AI-accelerated exploration without being exposed to its blind spots.

References

Karniadakis, G. E., et al. (2021). "Physics-informed machine learning." Nature Reviews Physics, 3, 422–440.

Alcalde, J., et al. (2019). "Quantification of uncertainty in 3-D seismic interpretation." Solid Earth, 10, 1049–1061.

Wu, X., et al. (2018). "Image processing and machine learning in seismic structural interpretation." The Leading Edge, 37(6), 451-458.

Canales, L. L. (1984). "Random noise reduction." SEG Technical Program Expanded Abstracts.

Anjos, C., et al. (2021). "Impacts of seismic resolution on fault interpretation." Tectonophysics, 814, 229008.

Disclaimer: The insights presented in this article are drawn from decades of general professional experience in applied geophysics, predictive modeling, and publicly available industry literature. They do not reflect the internal operations, specific technologies, or proprietary data of any specific operator, service company, or former employer.

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