Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning.

Despite advances in minimally invasive surgery, urolithiasis still recurs within the first five years after the first episode in more than 50% of patients. Researchers continue to search for new crystallisation inhibitors, metaphylaxis strategies and laser sources for stone destruction. Therefore, to achieve these goals, it is necessary to study kidney stones not only as the result of an isolated process of pathological biomineralisation in the human body, but also as a biogenic mineral or rock that obeys universal patterns and has certain properties. Consequently, kidney stones need to be studied using methods that are widely used in the geological sciences for the study of minerals, such as computed microtomography and petrological analysis. In this review, the properties of kidney stones studied using various research methods used in geosciences are discussed. These properties are also considered as new biomarkers of urolithiasis. This review discusses how new data from multimodal stone analysis can be used to develop personalised metaphylaxis and treatment strategies for all types of urolithiasis, including the most common idiopathic calcium-oxalate urolithiasis.

Structural correlation of reflection horizons remains one of the most labour-intensive operations in 3D seismic interpretation, particularly within the clinoform Neocomian sections of West Siberia, where target reflections show variable dynamics, pinch-outs and are complicated by faulting. Because horizon tracking is inherently sequential, we formulate it as a Markov decision process and solve it with reinforcement learning. Observing a local seismic context and the history of previously picked samples, an agent chooses, at each step, the vertical shift of the horizon on the adjacent trace; the reward function encodes geophysical criteria – waveform similarity to the reference reflection, phase consistency, lateral smoothness and fault tolerance. The method is implemented as a two-stage scheme: a policy first correlates the horizon over a grid of reference sections with a user-defined spacing, building a reliable network of anchors, and then densifies the result into a continuous surface across the whole volume. The agent is trained in a synthetic environment produced by forward full-wavefield modelling, with imitation pretraining on reference horizon trajectories; one or two intersecting reference sections suffice as input, and additional interpretation is introduced in complex zones. Tested on Achimov deposits of a field in Northern West Siberia, the approach agrees with manual correlation within 10 ms over conformable intervals. Limitations and directions for development are discussed.