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Fiber-optic cables have recently gained popularity for use as Distributed Acoustic Sensing (DAS) arrays for borehole microseismic monitoring due to their physical robustness as well as high spatial and temporal resolutions. As a result, the sensors record large amounts of data, making it very difficult to process in real-/semi-real-time using the conventional processing routines. We present a novel approach, based on deep learning, for handling the large amounts of DAS data in real-/semi-real-time. The proposed neural network was trained on synthetic microseismic data contaminated with real-ambient noise from field data and was validated using field DAS microseismic data obtained from a hydraulic fracturing operation. The results indicate that the trained network is capable of detecting and locating microseismic events from DAS data and simultaneously update the velocity model to a high degree of precision. The mean absolute errors in the event locations and the velocity model parameters are 2.04, 0.72, 2.76, 4.19 and 0.97 percent for distance (x), depth (z), P-wave velocity, S-wave velocity and density, respectively. In addition to automation and computational efficiency, deep learning reduces human expert data handling during processing, thus preserving data integrity leading to more accurate and reproducible results.

The thermal interaction of a gas production well with ice-rich permafrost that bears relict gas hydrates is simulated in Ansys Fluent using the enthalpy formulation of the Stefan problem. The model admits phase changes of pore ice and hydrate (ice melting and gas hydrate dissociation) upon permafrost thawing. The solution is derived from the energy conservation within the modeling domain by solving a quasilinear thermal conductivity equation. The calculations are determined for a well completion with three casing strings and the heat insulation of a gas lifting pipe down to a depth of 55 m. The thermal parameters of permafrost are selected according to laboratory and field measurements from the Bovanenkovo gas-condensate field in the Yamal Peninsula. The modeling results refer to the Bovanenkovo field area and include the size of the thawing zone around wells, with regard to free methane release as a result of gas hydrate dissociation in degrading permafrost. The radius of thawing around a gas well with noninsulated lifting pipes operating for 30 years may reach 10 m or more, while in the case of insulated lifting pipes, no thawing is expected. As predicted by the modeling for the Bovanenkovo field, methane emission upon the dissociation of gas hydrates caused by permafrost thawing around producing gas wells may reach 400,000–500,000 m3 over 30 years.

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.

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