Abstract: A method is described for automatically detecting shear-wave (S-wave) microseismic reflections using distributed acoustic sensing (DAS). The method includes obtaining raw DAS data; performing passive seismic event detection and phase picking; extracting a passive seismic event based on the passive seismic event phase picks to generate a seismic S-wave event gather; reducing noise in the raw DAS data; using the passive seismic S-wave event gather and the passive seismic event phases to identify an apex of a passive seismic S-wave event in the denoised DAS dataset and dividing it into two portions based on the apex; dip filtering the two portions to remove the direct arrival of the passive seismic S-wave events to generate a dip-filtered gather; and generating an S-wave microseismic reflection gather based on the dip-filtered gather.

Abstract: A subsurface electrical device monitoring system includes a controller configured to: obtain measurements made by a first sensor device measuring a temperature adjacent to a subsurface electrical device and a second sensor device measuring a power parameter associated with the subsurface electrical device; correlate the measurements made by the first and second sensor devices by time; generate a baseline of performance of the electrical device over periods of time within which the subsurface electrical device starts; obtain subsequent measurements made by the first sensor device and the second sensor device; correlate the subsequent measurements made by the first and second sensor devices by time; compare the subsequent measurements made by the first sensor device against expected values derived from the baseline; and determine that a problem is developing with the subsurface electrical device when a difference between a subsequent measurement and an expected value exceeds a threshold value.

Abstract: A method is described for monitoring strain in a borehole, that may include receiving fiber optic sensing data recorded in the borehole; identifying fingerprint signatures in the fiber optic sensing data caused by known markers; and using the fingerprint signatures to monitor changes in the borehole. The known markers may include downhole jewelry or lithology along the borehole.

Abstract: A method is described for automatically detecting shear-wave (S-wave) microseismic reflections using distributed acoustic sensing (DAS). The method includes obtaining raw DAS data; performing passive seismic event detection and phase picking; extracting a passive seismic event based on the passive seismic event phase picks to generate a seismic S-wave event gather; reducing noise in the raw DAS data; using the passive seismic S-wave event gather and the passive seismic event phases to identify an apex of a passive seismic S-wave event in the denoised DAS dataset and dividing it into two portions based on the apex; dip filtering the two portions to remove the direct arrival of the passive seismic S-wave events to generate a dip-filtered gather; and generating an S-wave microseismic reflection gather based on the dip-filtered gather.

Abstract: A method is described for determining well integrity including deploying seismic sensors in a wellbore; attaching a vibrational source to well hardware; using the vibrational source to generate seismic signal that moves through the wellbore; creating a seismic dataset by recording the seismic signal at the seismic sensors; and processing the seismic dataset to make a processed seismic image. The processed seismic image may be displayed on a graphical display in order to identify well integrity problems such as wellbore deformation, well casing damage, or problems with cement.

Abstract: Systems and methods are provided for correcting distributed acoustic sensing (DAS) data. The system can receive a seismic dataset with a plurality of initial seismic phase picks and a plurality of traces, and cross-correlate each of the plurality of initial seismic phase picks using the plurality of traces as reference traces. Each initial seismic phase pick can receive a set of corrected phase picks. The system can calculate a probability density function for each set of corrected phase picks. The system can select a peak of each probability density functions as accurate seismic phase picks. These accurate seismic phase picks can be used for event location in the DAS data.

Abstract: A method is described for predicting and preventing wellbore interactions at wells that are near the injection well. The method includes receiving fiber optics data; performing object detection by detecting object-like events in the fiber optic data; and sending instructions to a hydraulic fracturing system based on the object detection. The method is executed by a computer system.

Abstract: A method is described for predicting and preventing wellbore interactions at wells that are near the injection well. The method includes receiving fiber optics data; performing object detection by detecting object-like events in the fiber optic data; and sending instructions to a hydraulic fracturing system based on the object detection. The method is executed by a computer system.

Abstract: The present disclosure includes a method for suppressing 4D noise. The method includes calculating a first similarity map based on the similarity of one of one or more first 3D images and a second 3D image. The first and second 3D images are derived from first and second surveys, respectively. The method also includes calculating a second similarity map based on the similarity of one of the one or more first 3D images and a third 3D image, which is derived from the second survey. The method also includes calculating a third similarity map based on the similarity of first and second 4D images, which are based on differences between the 3D images. The method also includes generating a composite 4D image based at least on the first, second, and third similarity maps. The present disclosure may also include associated systems and apparatus.

Abstract: The present disclosure includes a method for suppressing 4D noise. The method includes calculating a first similarity map based on the similarity of one of one or more first 3D images and a second 3D image. The first and second 3D images are derived from first and second surveys, respectively. The method also includes calculating a second similarity map based on the similarity of one of the one or more first 3D images and a third 3D image, which is derived from the second survey. The method also includes calculating a third similarity map based on the similarity of first and second 4D images, which are based on differences between the 3D images. The method also includes generating a composite 4D image based at least on the first, second, and third similarity maps. The present disclosure may also include associated systems and apparatus.