Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization

Abstract

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.

Hydraulic Fracturing Shear/Tensile/Compressive Crack Investigation Using Microseismic Data

“…By injecting high-pressure fluid into the target reservoir, shear/tensile/compressive cracks and the corresponding HF networks are generated, expanded, or connected with natural fractures, helping to increase the porosity and permeability of underground reservoirs and then improve the productivity of unconventional oil and gas resources. Through microseismic monitoring, signals of the HF cracks can be captured and analyzed, providing information about the geomechanical and petrophysical changes during reservoir development [4,5]. Study of the microseismic focal mechanism (shear/tensile/compressive crack) is of great importance for characterizing fracture geometry, calculating the stimulated reservoir volume, understanding in situ stress state, and evaluating the HF effects throughout the process [6,7].…”

Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation

“…Recently, deep learning (DL) has shown excellent capabilities for nonlinear mapping function approximation in computer vision, especially in the tasks of reconstructing models and high-resolution images [15,16]. The development of DL has also brought new opportunities to seismic and microseismic data processing and inversion [17], such as signal denoising [18], signal identification and classification [19,20], first-arrival picking [21][22][23], source location [24], and velocity model building and calibration [25]. Using seismic waveforms as the feature input and velocity models as the labels, the trained models with the nonlinear mapping capability of neural networks can effectively predict velocity models from seismic waveforms.…”