The increase in Arctic marine activity due to rapid warming and significant sea ice loss necessitates highly reliable, short-term sea ice forecasts to ensure maritime safety and operational efficiency. In this work, we present a novel data-driven approach for sea ice condition forecasting in the Gulf of Ob, leveraging sequences of radar images from Sentinel-1, weather observations, and GLORYS forecasts. Our approach integrates advanced video prediction models, originally developed for vision tasks, with domain-specific data preprocessing and augmentation techniques tailored to the unique challenges of Arctic sea ice dynamics. Central to our methodology is the use of uncertainty quantification to assess the reliability of predictions, ensuring robust decision-making in safety-critical applications. Furthermore, we propose a confidence-based model mixture mechanism that enhances forecast accuracy and model robustness, crucial for reliable operations in volatile Arctic environments. Our results demonstrate substantial improvements over baseline approaches, underscoring the importance of uncertainty quantification and specialized data handling for effective and safe operations and reliable forecasting.

Russia has claimed that US sanctions levied against the Arctic LNG 2 project undermine global energy security. The Russian foreign ministry's spokeswoman hit out on Wednesday at Washington's "unacceptable" move to clamp down on the massive Arctic LNG 2. The sanctions are just the latest measure implemented as the West seeks to limit Moscow's financial ability to wage war in Ukraine. The remarks came after Washington announced sanctions against the new liquefied natural gas plant that is under development on the Gydan Peninsula in the Arctic last month. "We consider such actions unacceptable, especially in relation to such large international commercial projects as Arctic LNG 2, which affect the energy balance of many states," said foreign ministry spokesperson Maria Zakharova. "The situation around Arctic LNG 2 once again confirms the destructive role for global economic security played by Washington, which speaks of the need to maintain this security but in fact, by pursuing its own selfish interests, tries to oust competitors and destroy global energy security."

This paper presents a successful attempt to overcome the uncertainties in seismicstratigraphic interpretation of the complex rock section with good accuracy for the early stage of field maturity. The deliverable included the model of restored regression relationship between the values of natural radioactivity of rocks and seismic wave field attributes with an acceptable prediction quality. Acceptable quality of the forecast is confirmed both by model validation with complete removal of some data from the learning process, and by the data obtained following the results of a new well drilled 150 meters away from the well from the learning sample. The regression relationship between the natural radioactivity of rocks and effective porosity of reservoirs was restored based on well tops data and log interpretation data - transition to reservoir properties of the target was carried out. The result was achieved with help of process stack consisting of machine learning methods, methods of enriching the source data with synthetic data, algorithms of creating new features using the function for regression model reconstruction as the target one, measurements of natural radioactivity of rocks, including for horizontal segments of wells. Two approaches were developed to enriching the source sample (geophysical data augmentations): spindle method and with help of Conditional Generative Adversarial Nets architecture (CGAN).

During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20 m between the bit and high-fidelity rock type sensors. The only way to detect the lithotype changes in time is the usage of Measurements While Drilling (MWD) data. However, there are no mathematical modeling approaches that reconstruct the rock type based on MWD data with high accuracy. In this article, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock type. We propose the approach that combines traditional machine learning based on the solution of the rock type classification problem with change detection procedures rarely used before in Oil & Gas industry. The data come from a newly developed oilfield in the North of Western Siberia. The results suggest that we can detect a significant part of changes in rock type reducing the change detection delay from 20 to 2.6 m and the number of false positive alarms from 71 to 7 per well.

In order to bridge the gap of more than 15m between the drilling bit and high-fidelity rock type sensors during the directional drilling, we present a novel approach for identifying rock type at the drilling bit. The approach is based on application of machine learning techniques for Measurements While Drilling (MWD) data. We demonstrate capabilities of the developed approach for distinguishing between the rock types corresponding to (1) a target oil bearing interval of a reservoir and (2) a non-productive shale layer and compare it to more traditional physics-driven approaches. The dataset includes MWD data and lithology mapping along multiple wellbores obtained by processing of Logging While Drilling (LWD) measurements from a massive drilling effort on one of the major newly developed oilfield in the North of Western Siberia. We compare various machine-learning algorithms, examine extra features coming from physical modeling of drilling mechanics, and show that the classification error can be reduced from 13.5% to 9%.