G01V2210/30

Methods and systems for processing borehole dispersive waves with a physics-based machine learning analysis

Systems and methods are provided for determining a formation body wave slowness from an acoustic wave. Waveform data is determined by logging tool measuring the acoustic wave. Wave features are determined from the waveform data and a model is applied to the wave features to determine data-driven scale factors The data-driven scale factors can be used to determine a body wave slowness within a surrounding borehole environment and the body wave slowness can be used to determine formation characteristics of the borehole environment.

Determining distance to bed boundary uncertainty for borehole drilling

A system and method for determining an uncertainty of a distance to bed boundary (DTBB) inversion of a geologic formation. The system or method includes receiving logging data from a borehole tool, performing a first DTBB inversion using the logging data to calculate first DTBB solutions, adding quantified noise to the logging data to produce an adjusted signal, performing a second DTBB inversion using the adjusted signal to calculate second DTBB solutions, comparing the first DTBB solutions to the second DTBB solutions to determine an uncertainty of the first DTBB solutions based on a relationship of the quantified noise and the difference between the first DTBB solutions and the second DTBB solutions.

Microseismic Sensitivity Analysis and Scenario Modelling
20170371051 · 2017-12-28 ·

Systems, methods, and computer-readable media for designing a microseismic monitoring project. The method includes receiving data representing the microseismic monitoring project for at least one subterranean volume, the data including data representing a plurality of factors associated with a design of the microseismic monitoring project. The method also includes conducting a sensitivity analysis to determine a relative sensitivity between at least two of the plurality of factors, and determining whether to update a modelling scenario for the microseismic monitoring project based on the relative sensitivity.

FORMATION MEASUREMENTS USING DOWNHOLE NOISE SOURCES

A method of performing measurements of an earth formation includes disposing at least a first receiver and a second receiver in one or more monitoring boreholes in a formation, and injecting fluid into the formation from an injection borehole, wherein injecting includes operating a fluid control device to generate seismic and/or acoustic noise having an identifiable characteristic. The method also includes detecting seismic and/or acoustic signals at the first receiver and detecting seismic and/or acoustic signals at a second receiver, the seismic and/or acoustic signals corresponding to the seismic and/or acoustic noise, calculating an estimate of a Green's function between the first receiver and the second receiver by processing seismic and/or acoustic waves detected by the first receiver and the second receiver to at least partially reconstruct the Green's function, and estimating variations in a velocity of a region of the formation by determining variations in the reconstructed Green's function.

DEVICE AND METHOD FOR WEIGHTED SPARSE INVERSION FOR SEISMIC PROCESSING
20170248716 · 2017-08-31 · ·

Computing device, computer instructions and method for processing input seismic data d. The method includes receiving the input seismic data d recorded in a data domain, solving a linear inversion problem constrained by input seismic data d to obtain a model domain and its energy, wherein the linear inversion problem is dependent on sparseness weights that are simultaneously a function of both time and frequency, reverse transforming the model domain energy to the data domain, and generating an image of a surveyed subsurface based on the reverse transformed model domain energy.

NOISE REMOVAL FOR DISTRIBUTED ACOUSTIC SENSING DATA

An example method includes at least partially positioning within a wellbore an optical fiber of a distributed acoustic sensing (DAS) data collection system. Seismic data from the DAS data collection system may be received. The seismic data may include seismic traces associated with a plurality of depths in the wellbore. A quality factor may be determined for each seismic trace. One or more seismic traces may be removed from the seismic data based, at least in part, on the determined quality factors.

Microseismic monitoring with fiber-optic noise mapping

The combination of one or more 3-component microseismic sensors deployed into a wellbore adjacent a microseismic event and a linear array of distributed fiber optic acoustic sensors deployed uphole thereof provides two sets of data for establishing noise-free signals for locating the microseismic event in the formation about the wellbore. The distributed fiber optic signals monitor noise transmitted along coiled tubing used to pump a completion operation or as a result of the fluid flowing through the casing or coiled tubing, or along wireline used to deploy the microseismic sensors. The noise is mapped and extrapolated for estimating noise at the 3-component sensors. The estimated noise is removed from the 3-component sensor data for producing clean signals representing the location of the microseismic events.

REAL TIME IDENTIFICATION OF EXTRANEOUS NOISE IN SEISMIC SURVEYS
20220066060 · 2022-03-03 ·

A system to detect and control noise in seismic surveys is provided. The system receives, responsive to a seismic wave generated by a source, seismic data detected by a sensor component of a seismic data acquisition unit. The system generates, for windows of the seismic data, Hough tensors for seismic data transforms in multiple dimensions. The system detects, based on a comparison of an eigenvector and eigenvalue of a canonical matrix of the Hough tensors with a historical eigenvector and eigenvalue of a historical canonical matrix of historical Hough tensors of historical seismic data, a first presence of noise in the seismic data. The first presence of noise can correspond to a noisy spectra pattern in a seismic data transform of the seismic data. The system provides, responsive to detection of the first presence of noise in the seismic data, a notification to adjust a characteristic of the seismic survey.

METHODS AND SYSTEMS FOR PROCESSING BOREHOLE DISPERSIVE WAVES WITH A PHYSICS-BASED MACHINE LEARNING ANALYSIS

Systems and methods are provided for determining a formation body wave slowness from an acoustic wave. Waveform data is determined by logging tool measuring the acoustic wave. Wave features are determined from the waveform data and a model is applied to the wave features to determine data-driven scale factors The data-driven scale factors can be used to determine a body wave slowness within a surrounding borehole environment and the body wave slowness can be used to determine formation characteristics of the borehole environment.

DETERMINING DISTANCE TO BED BOUNDARY UNCERTAINTY FOR BOREHOLE DRILLING

A system and method for determining an uncertainty of a distance to bed boundary (DTBB) inversion of a geologic formation. The system or method includes receiving logging data from a borehole tool, performing a first DTBB inversion using the logging data to calculate first DTBB solutions, adding quantified noise to the logging data to produce an adjusted signal, performing a second DTBB inversion using the adjusted signal to calculate second DTBB solutions, comparing the first DTBB solutions to the second DTBB solutions to determine an uncertainty of the first DTBB solutions based on a relationship of the quantified noise and the difference between the first DTBB solutions and the second DTBB solutions.