AI in Oil & Gas Exploration
A neural network built by RN-KrasnoyarskNIPIneft, a Rosneft subsidiary, cuts one of the first stages of seismic data processing tenfold — from up to 80 hours of manual work down to about 7. In petrophysics, machine-learning models are finding links between facies and reservoir properties that classic attribute analysis misses, while analog-field search and geological risk assessment increasingly rely on Bayesian networks and explainable models instead of one geologist's judgment call. Here's where AI in exploration already saves weeks of work — and where it still needs a human check, not blind trust.
AI in exploration isn't a replacement for geologists — it's an accelerator for routine stages: seismic interpretation, analog-field search and reserves estimation that used to take weeks now take hours. Labeled training data is often scarce, and drilling a single well costs tens of millions of rubles — so every model result still needs checking, not blind trust.
How Does AI Pick Out Horizons and Faults in Seismic Data?
Manual seismic interpretation is the most labor-intensive stage of exploration: a geologist painstakingly traces horizons and faults across a volume of data that, on a single project, can span hundreds of square kilometers and tens of thousands of seismograms. Neural networks take over exactly that routine part of the work — not the whole interpretation, but its longest, most repetitive slice.
«Specialists at RN-KrasnoyarskNIPIneft, a Rosneft subsidiary, built a neural-network-based algorithm that cuts the time needed for one of the initial stages of seismic data processing tenfold.» — Neftegaz.RU — Rosneft rolled out neural networks in seismic data processing (in Russian)
This isn't a one-off pilot — it's a working tool on real assets:
- A single project covers roughly 300 km² or 30,000 seismograms
- Manual processing takes up to 80 hours; the algorithm takes up to 7, with no specialist involved
- The prototype was tested on real data from two of the company's license areas in Eastern Siberia
The same logic applies to fault picking, where an interpreter's mistake is especially costly — it affects both the field's structural model and the seal-integrity assessment for CO₂ storage. Eliis and Chevron combined Chevron's proprietary automated fault-detection models with Eliis' PaleoScan automated seismic interpretation platform — according to the companies, the time needed to build a structural interpretation drops by orders of magnitude as a result.
«In the field of geosciences, Eliis views artificial-intelligence outcomes not as ultimate end-products but more as enabling technologies that have to be seamlessly integrated into a wider scientific workflow that includes the ability to perform quality control and refinement to ensure that geoscientists remain in control throughout the entire interpretive process.» — Francois Laferriere, Eliis — JPT (SPE)
What Do Models Find in Well Logs and Core Samples?
Well logs and core samples only give data at wellbore points, but what's needed is a continuous picture of the reservoir between them. The classic approach is interpolation plus seismic attribute analysis; where the geology is heterogeneous, both methods hit a ceiling on accuracy.
«Using machine learning algorithms, including Kolmogorov neural networks, improved the forecasting of facies distribution and reservoir properties. The study's results showed high correlation between the predictions and the actual data, confirmed by drilling new wells.» — Neftegaz.RU — Integrating a facies model and machine learning to forecast reservoir properties (in Russian)
The case in point: the geologically complex PK19-20 producing formations of the Pokur suite in Western Siberia. Specialists at the Tyumen Petroleum Research Center used core and well-section data to identify more than 20 facies zones, grouped them into five key macrofacies, and linked them to seismic data through Kolmogorov neural networks — an architecture with complex nonlinear connections between neurons that traditional seismic attribute analysis doesn't pick up. After the model was built, 17 new wells were drilled: for gas-saturated thickness, the facies model matched actual results better than the standard approach, which only models reservoir versus non-reservoir rock.
How Does AI Select Analog Fields?
Exploratory drilling is the most reliable way to learn a field's parameters — and also the slowest and most expensive. Where drilling is premature or doesn't make sense yet, missing data is estimated from analogs — nearby or similar fields with comparable geology.
«Analog fields are fields that resemble the target field in their geological characteristics.» — ITMO University — Analog field search based on Bayesian network clustering (in Russian)
The difficulty is that a field's parameters form a multi-dimensional distribution mixing discrete and continuous variables at once, and most existing solutions rely either on a geologist's expert judgment or on simple clustering algorithms that lack precision. One proposed approach clusters Bayesian networks built on the parameters of the fields closest to the target: a distance metric first finds the N nearest analogs, a Bayesian network is trained on that shortlist, and the result is used to reconstruct missing parameters and estimate reserves probabilistically rather than as a single point figure — the same P90/P50/P10 principle behind probabilistic resource-base estimation in general.
Can You Trust AI's Assessment of Geological Risk?
Geological risk assessment — prospect risking — translates geological and geophysical data into a probability of success: is this the place to drill? A model can automate that assessment, but adoption is moving slower than it could, and accuracy isn't the reason why.
«Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go through a long process to arrive at the probability of success of specific outcomes. AI has the capability to automate the process but its adoption has been limited thus far owing to a lack of transparency in the way complicated, black box models generate decisions.» — Ahmad Mustafa, Ghassan AlRegib — arXiv
The problem is broader than one method. Seismic interpretation, well logging and analog selection share the same bottleneck: labeled data is scarce — a drilled well costs tens of millions and stays a rare confirmation, not just another row in a dataset — and an opaque result leaves a geologist nothing to check it against. Platform developers are reaching the same conclusion from a different angle — not through explaining a single model, but through making the entire calculation traceable.
«The companies envision platforms with transparent audit trails showing the datasets, assumptions and logic behind each recommendation. The goal is to provide users with verifiable outputs rather than a "black box" decision-making process.» — Offshore Magazine — Geoteric and SINTEF on agentic AI for subsurface workflows
The practical takeaway is the same for all three tasks — seismic, logging and analogs: trust in a number starts not with how accurate the model is, but with being able to see what data and what logic produced it.
How Does AVP AI Evaluate a Block's Geology Before Drilling?
AVP AI — an AI platform for oil and gas asset evaluation — solves the same problem as the tools above, but not as a separate step: it's the foundation for everything calculated afterward. The platform pulls a block's geology from open industry data, selects analog fields, and estimates reserves within a probabilistic P90/P50/P10 range.
From there, those numbers don't stand alone — agents that calculate the production profile and economics build on top of them. A coordinator and eight specialised agents — search, geology, reservoir simulation, surface facilities, economics, wells, business case and data standardisation — together run 34 calculation tools. Every step is logged: what data was used, from which source, and what formula produced the result — so the reserves range can be checked, not just trusted, exactly as the sources above recommend.
For what the agents calculate beyond geology, see agentic systems in oil and gas and AI in oil and gas. With a long shortlist of blocks, it's worth running them through license area screening first rather than calculating each one's geology from scratch by hand. For how this differs from a report generated on a fixed schedule, see automating asset valuation.
Frequently Asked Questions on AI in Oil & Gas Exploration
Wherever there's a clear volume of routine manual work to compare against. At RN-KrasnoyarskNIPIneft, a neural network sped up one of the initial stages of seismic processing tenfold — 7 hours of algorithm time instead of 80 hours of manual work, with no specialist involved. In petrophysics, a model built on Kolmogorov neural networks forecast facies distribution and reservoir properties more accurately — the forecast was confirmed by drilling 17 new wells.
Analog fields are blocks that resemble the target field in their geological characteristics, letting you estimate missing parameters from their data instead of paying for expensive exploratory drilling. One approach proposed at ITMO University clusters Bayesian networks built on the parameters of nearby fields — a method that captures the multi-dimensional, uneven nature of geological data more accurately than simple clustering algorithms.
Only if it's clear what data and what logic the model used to reach its decision. Research on arXiv finds that it's the opacity of complex models, not their accuracy, that limits AI adoption in geological risk assessment. Developers of industrial seismic and reservoir-modeling platforms reach the same conclusion from a different angle — they describe the goal as a verifiable result with traceable logic, not a "black box" you have to take on faith.
The platform pulls a block's geology from open industry data, selects analog fields, and estimates reserves within a probabilistic P90/P50/P10 range — the starting point for calculating the production profile and economics. A coordinator and eight specialised agents together run 34 calculation tools, and every step is logged: what data was used and what formula produced the result, so the reserves range can be checked rather than just trusted.
Sources
- Industry media Neftegaz.RU ↗ Rosneft rolled out neural networks in seismic data processing (in Russian)
- Research JPT (SPE) ↗ Eliis and Chevron Agree To Collaborate on AI in Seismic Interpretation
- Industry media Neftegaz.RU ↗ Integrating a facies model and machine learning to forecast reservoir properties (in Russian)
- Research ITMO University ↗ Analog field search based on Bayesian network clustering (in Russian)
- Research arXiv ↗ Explainable Machine Learning for Hydrocarbon Prospect Risking
- Industry media Offshore Magazine ↗ IMAGE 2026: Geoteric and SINTEF aim to connect seismic interpretation and reservoir simulation through agentic AI
We will assess the geology of one of your blocks with AI — free of charge
Give us coordinates or a license number — the platform will select analogs, estimate P90/P50/P10 reserves and cut some of the uncertainty before you drill.