233 citations · 483 across the 14 of their papers we have counts for
15 papers
Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents
Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova +1
We present an approach for interpreting a black-box alarming system for forecasting accidents and anomalies during the drilling of oil and gas wells. The interpretation methodology…
Core Box Image Recognition and its Improvement with a New Augmentation Technique
E. E. Baraboshkin, A. E. Demidov, D. M. Orlov +1
Most methods for automated full-bore rock core image analysis (description, colour, properties distribution, etc.) are based on separate core column analyses. The core is usually i…
On a workflow for efficient computation of the permeability of tight sandstones
Vladislav Pimanov, Vladislav Lukoshkin, Pavel Toktaliev +7
The paper presents a workflow for fast pore-scale simulation of single-phase flow in tight reservoirs typically characterized by low, multiscale porosity. Multiscale porosity impli…
Forecasting the abnormal events at well drilling with machine learning
Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova +1
We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning algorithm uses the data from the drilling telemetry representin…
Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation
Ivan Makhotin, Denis Orlov, Dmitry Koroteev +3
Well known oil recovery factor estimation techniques such as analogy, volumetric calculations, material balance, decline curve analysis, hydrodynamic simulations have certain limit…
Deep Convolutions for In-Depth Automated Rock Typing
E. E. Baraboshkin, L. S. Ismailova, D. M. Orlov +5
The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a metho…