Abstract: Systems and methods for operating a distributed acoustic sensing (DAS) system are disclosed that process the DAS signal by downsampling the received signal and stacking the channels, generating a plurality of sliding windows of the processed signal, analyzing the windows to either identify a microseismic event or determine that the window contains only noise, then discarding all noise windows. A convolutional neural network is used to determine an onset time and a peak channel of each microseismic event and to reduce the dimensionality of the data in time and space around the onset time. A convolutional neural network is used to identify a first arrival pick within the truncated window of all recorded phases, which are then used to determine the physical location of the source fracture.

Abstract: In some embodiments, a system for producing fluid from a well can include an electrical submersible pump (ESP) disposed in a wellbore of the well and configured to pump the fluid. The system may further include a distributed acoustic sensing (DAS) system, for example having an interrogator unit and a fiber optic cable extending downhole in the wellbore. An end of the fiber optic cable can be disposed downhole relative to the ESP. In embodiments, the system may further include a controller configured to receive data from the DAS system, process the data to detect a slug, determine a parameter of the detected slug, and alter operation of the ESP in response to determining that the parameter exceeds a threshold.

Abstract: A system for controlling intelligent completion valves (ICVs) used in a hydrocarbon well operation is disclosed. The ICVs can be located downhole in a wellbore, and an outflow of pressurized injection fluid from the wellbore may be used to drive hydrocarbons toward one or more offset producing wells. A trained machine-learning model can be generated by training a machine-learning model on training data comprising acoustic signal data generated by a pressurized injection fluid flowing through the ICVs at various flow rates and ICV positions. When applied to new acoustic sensing system sensor data associated with an ICV of multiple ICVs in the well, the trained machine-learning model can generate a result indicating a predicted flow rate of the pressurized injection fluid through the ICV. The result may be output and used to control the flow rate of pressurized injection fluid through the ICVs.

Abstract: A system for operating intelligent completion valves (ICVs) used in a hydrocarbon well operation is disclosed. The ICVs can be positionable downhole in a wellbore. A trained machine-learning model can be generated by training a machine-learning model on training data comprising acoustic signal data generated by a pressurized injection fluid flowing through the ICVs at various flow rates and different ICV positions. When new acoustic sensing system sensor data associated with an ICV of multiple ICVs in the wellbore is applied to the trained machine-learning model, the trained machine-learning model can generate a result determining that the amplitude spike is attributable to a change in the position of the intelligent completion valve. The system may also determine a magnitude of the change in the position of the intelligent completion valve. The result may be output and used to control the ICV.

Abstract: In some embodiments, a system for producing fluid from a well can include an electrical submersible pump (ESP) disposed in a wellbore of the well and configured to pump the fluid. The system may further include a distributed acoustic sensing (DAS) system, for example having an interrogator unit and a fiber optic cable extending downhole in the wellbore. An end of the fiber optic cable can be disposed downhole relative to the ESP. In embodiments, the system may further include a controller configured to receive data from the DAS system, process the data to detect a slug, determine a parameter of the detected slug, and alter operation of the ESP in response to determining that the parameter exceeds a threshold.

Abstract: Systems and methods for operating a distributed acoustic sensing (DAS) system are disclosed that process the DAS signal by downsampling the received signal and stacking the channels, generating a plurality of sliding windows of the processed signal, analyzing the windows to either identify a microseismic event or determine that the window contains only noise, then discarding all noise windows. A convolutional neural network is used to determine an onset time and a peak channel of each microseismic event and to reduce the dimensionality of the data in time and space around the onset time. A convolutional neural network is used to identify a first arrival pick within the truncated window of all recorded phases, which are then used to determine the physical location of the source fracture.

Abstract: The present disclosure provides techniques for presenting a reward impression. A computing system can receive, from a client device, a request to view the reward impression having a first time slot and a second time slot. The computing system can calculate a first conversion rate associated with a first content item being presented in the first time slot and a second conversion rate associated with a second content item being presented in the second time slot. The computing system can select, using the one or more machine-learned models based on the first conversion rate and the second conversion rate, the first content item and the second content item from a plurality of content items. The computing system can cause the presentation of the first content item in the first time slot and the second content item in the second time slot of the reward impression.

Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for machine learning analysis of low-frequency signal data in fracturing operations. The present technology can receive strain data associated with a monitoring well that is proximate to a treatment well. The strain data can comprise information representing a fracturing operation associated with the treatment well. Further, the present technology can convert the strain data into image data where a color scale corresponds to a degree of strain observed by a fiber optic cable deployed in the monitoring well. As follows, the present technology can provide the image data to a machine-learning model, which is configured to identify one or more features in the image data.

Abstract: An asymmetric fluidic oscillator can generate acoustic signals in a wellbore. The asymmetric fluidic oscillator can include an inlet housing defining an inlet channel, a feedback system, and an outlet housing defining an outlet channel. The inlet channel can be sized to receive fluid from the wellbore. The feedback system can be coupled to the inlet channel to oscillate the fluid from the wellbore. The outlet channel can be coupled to the feedback system and can be sized to receive the oscillated fluid from the feedback system. The outlet channel can include an asymmetric feature to generate acoustic signals detectable in the wellbore.

Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for machine learning analysis of low-frequency signal data in fracturing operations. The present technology can receive strain data associated with a monitoring well that is proximate to a treatment well. The strain data can comprise information representing a fracturing operation associated with the treatment well. Further, the present technology can convert the strain data into image data where a color scale corresponds to a degree of strain observed by a fiber optic cable deployed in the monitoring well. As follows, the present technology can provide the image data to a machine-learning model, which is configured to identify one or more features in the image data.

Abstract: An asymmetric fluidic oscillator can generate acoustic signals in a wellbore. The asymmetric fluidic oscillator can include an inlet housing defining an inlet channel, a feedback system, and an outlet housing defining an outlet channel. The inlet channel can be sized to receive fluid from the wellbore. The feedback system can be coupled to the inlet channel to oscillate the fluid from the wellbore. The outlet channel can be coupled to the feedback system and can be sized to receive the oscillated fluid from the feedback system. The outlet channel can include an asymmetric feature to generate acoustic signals detectable in the wellbore.