Digital Petroleum

We help to automate analysis of oil and gas fields

Get more info on  our products and services
develops tools enabling efficient decision making at exploration and development of oil and gas fields
Machine learning, hybrid modelling of physical and technological processes, math methods of optimization and AI planning
We use
you reduce costs of technological operations on oil and gas  fields and conduct an objective estimation of the potential of the oil and gas reserves
With our tools
We reside in Skolkovo. Skoltech is one of our founders.
Geological uncertainties
Expensive geophysical surveys
Expensive and time consuming core studies
Challenges you may face with
Automation of geomodelling
Forecasting of reservoir potential over a wide range of features
Digital twin of lab core studies
Our developments to tackle the challenges
Cost intensive decisions on field development
Development and drilling
Expensive drilling and completions operations
High cost and   uncertainty of IOR/EOR projects
Challenges you may face with
Production forecast with real-time history matching
Multi objective cost optimization
Objective data-driven recommending systems
Our developments to tackle the challenges
Cost intensive decisions on well treatment campaigns
Pumps failures
Not-optimal performance of surface infrastructure
Challenges you may face with
Predictive models based on well treatment history and physical feaures of near wellbore zone
Predictive maintanance
System engineering and   continous optimization
Our developments to tackle the challenges
Our advantages
Reduction of costs of technological processes
Cost efficiency
Speeding-up conventional math modelling approaches
Time efficiency
Predicting the cases outside of historical envelope
Forecast efficiency
Suppressing risks at working with geological uncertainties
Risk-handling efficciency
We develop tools for tickling the   particular challenges of our partners from the oil and gas industry
How to work with us
R&D on demand
We help developing the strategic directions for  digital technologies at various segments of oil ang gas
We license our own software solutions  deliver the support services
Software Licensing
If you have a particular challenge for us or you want to know more of our developments, please email to
Contradictory information on   reservoir potential
Sample projects
Reservoirs data assimilation and machine learning for  recovery factor prediction
Data-driven estimate of recovery factor
XGB-based tool for recovery factor prediction with and estimate of prediction uncertainty
Cost-intensive well treatment jobs are unclear in terms of the potential outcome. Physics-based models have a very uncertain input and generate a very uncertain output.
Develop a model based on a real history of well treatment jobs at a particular geological location
Well treatment efficiency prediction
Data-driven tool for prediction of well treatment efficiency (XGB with a fine tuning) at various job designs, types of completions and features of the near wellbore geology
Expensive well logging or/and incomplete well logging data
Machine learning to forecast the well logging data at the areas with poor coverage of geophysical data
Well logs reconstruction
Data-driven tool for reconstruction of the well logging data
Become our partner
Our team
Dmitry Koroteev
Denis Orlov
BD, AI-Driven Field Development
Leyla Ismailova
BD, Automated Geomodelling
Ksenia Antipova
BD, AI-Driven Drilling
Ekaterina Muravleva
Expert, Mathematical Modelling
Yury Meshalkin
R&D Engineer, Hybrid Modelling
Ivan Makhotin
R&D Engineer, Artificial Intelligence
Nikita Klyuchnnikov
Consultant, Artificial Intelligence
Alexey Zaytsev
Expert, Artificial Intelligence
Mohammad Ebadi
R&D Engineer, Applied Math
Evgeny Baraboshkin
R&D Engineer, Geology and Machine Learning
Anna Gubanova
R&D Engineer, Field Development Modelling
Pavel Temirchev
R&D Engineer, Surrogate Modelling
Gazprom Neft
Our team has performed projects for
Skolkovo Institute of Science and Technology,
Skolkovo Innovation Center
Nobel Street, 3, Moscow, 121205, Russia
© 2019 Digital Petroleum
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