Research output per year
Research output per year
Personal profile
Profile Summary
Research Engineer in Data Science and GeoEnergy focused on converting theoretical concepts into real-world impact. Specializes in applied data science and machine learning to deliver robust solutions for subsurface, energy, and environmental challenges. Executes the full R&D lifecycle – from research and feasibility analysis to prototyping and production-grade Python development.
Research interests
Field of study: Machine Learning for GeoEnergy and Environment applications; Petroleum Data Science.
Methodologies: Classical ML, Deep Learning, Computer Vision, Image Processing; Pressure Transient Analysis, Well Testing.
Data: Multivariate Time-Series, High-Dimensional Tabular Data, Geospatial & Remote Sensing Data, Subsurface Imaging & Seismic Data.
Roles & Responsibilities
Research Engineer at AutoWell
R&D Lead at AI Methane Tracker
Research Grants and Projects
UKRI ICURe discover grant for AI Methane Tracker
Global Innovation Challenge 2024-2025 for AI Methane Tracker
Research Group Contact Details
Research lab: GeoDataScience group
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy -
SDG 8 Decent Work and Economic Growth -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 11 Sustainable Cities and Communities -
SDG 13 Climate Action -
SDG 15 Life on Land
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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A Breakthrough in Early Detection and Monitoring of Induced Fracture with Automated Interpretation of Step-Rate Tests
Mugisha, J., Shchipanov, A., Øverland, A. M., Starikov, V. & Muradov, K., 23 Jun 2026, SPE Europe Energy Conference and Exhibition 2026. Society of Petroleum Engineers, SPE-233202-MSResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Combining Data-Driven Physics-Informed Methods to Automate Permanent Monitoring of Well Performance
Shchipanov, A., Cui, B., Starikov, V., Muradov, K., Zhang, N., Demyanov, V. & Berenblyum, R., 9 Mar 2026, p. 1-5. 5 p.Research output: Contribution to conference › Paper › peer-review
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Deep Learning Ensemble for Methane Emissions Detection in Satellite Imagery
Starikov, V., Demyanov, V. & Soobhany, A. R., Mar 2025, p. 1-5. 5 p.Research output: Contribution to conference › Paper › peer-review
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A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements
Shchipanov, A., Cui, B., Starikov, V., Muradov, K., Khrulenko, A., Zhang, N. & Demyanov, V., 17 Apr 2024, SPE Norway Subsurface Conference 2024. Society of Petroleum Engineers, SPE-218470-MSResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution
6 Link opens in a new tab Citations (Scopus) -
Feature Extraction and Pattern Recognition in Time-lapse Pressure Transient Responses
Starikov, V., Shchipanov, A., Demyanov, V. & Muradov, K., Nov 2024, In: Geoenergy Science and Engineering. 242, 213160.Research output: Contribution to journal › Article › peer-review
Open AccessFile7 Link opens in a new tab Citations (Scopus)179 Downloads (Pure)