Abstract: Sonic logging data including a sonic waveform associated with a plurality of shot gathers is accessed. A transformation operator is applied to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator. A machine learning process is performed using the transformed sonic image to determine a sonic slowness associated with the sonic logging data. The sonic slowness is provided as an output.

Abstract: Sonic logging data including a sonic waveform associated with a plurality of shot gathers is accessed. A transformation operator is applied to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator. A machine learning process is performed using the transformed sonic image to determine a sonic slowness associated with the sonic logging data. The sonic slowness is provided as an output.

Abstract: A method for estimating sonic slowness comprising: obtaining (700) a plurality of sonic waveforms are received by a plurality of receivers of a logging tool after emission of a source sonic wave by a transmitter, obtaining (710) slowness models of the subterranean formation, a slowness model being defined by a at least one cell of constant slowness for at least one wave energy mode, computing (720), for each slowness model, a set of candidate travel times, a candidate travel time of a set of candidate travel times being computed for a wave energy mode and a position of a receiver of the plurality of receivers, computing (730) a relevance indicator for each set of candidate travel times based on the recorded sonic waveforms; searching (740) a match between the sets of candidate travel times and the recorded sonic waveforms by searching a relevance indicator which is optimum, computing (750) a sonic slowness estimate for the subterranean formation from a set of candidate travel times for which the relevance ind

Abstract: A method for processing microseismic data, comprises: receiving the microseismic data acquired by one or more multicomponent sensors; convolving the microseismic data with an operator that is applied to all of the components of the microseismic data; and applying a multicomponent filter operator to the convolved microseismic data. The microseismic data may result from human activity or be entirely natural. The filtering preserves the polarity of the received data while improving the signal-to-noise ratio of the filtered data.

Abstract: A method for estimating sonic slowness comprising: obtaining (700) a plurality of sonic waveforms are received by a plurality of receivers of a logging tool after emission of a source sonic wave by a transmitter, obtaining (710) slowness models of the subterranean formation, a slowness model being defined by a at least one cell of constant slowness for at least one wave energy mode, computing (720), for each slowness model, a set of candidate travel times, a candidate travel time of a set of candidate travel times being computed for a wave energy mode and a position of a receiver of the plurality of receivers, computing (730) a relevance indicator for each set of candidate travel times based on the recorded sonic waveforms; searching (740) a match between the sets of candidate travel times and the recorded sonic waveforms by searching a relevance indicator which is optimum, computing (750) a sonic slowness estimate for the subterranean formation from a set of candidate travel times for which the relevance ind

Abstract: This invention provides a method for characterizing natural fracture networks or other textural networks in an Earth formation when using microseismic monitoring of a hydraulic fracturing job. The method comprises receiving (120) microseismic data from a hydraulic fracturing event, identifying a data subset (153) comprising components of the microseismic data associated with the one or more hydraulic fractures; and obtaining a remainder dataset (156) of the microseismic data by removing the subset from the microseismic data. One approach for identifying the data subset, after removing high uncertainty microseismic events, is to create a Voronoi diagram of a plurality of cells each associated with one of the microseismic events, determine a density for each cell, create a connectivity matrix of the high density cells and identify event clusters in the connectivity matrix which are aligned with a main growing direction of the hydraulic fracture.

Abstract: A method for processing microseismic data, comprises: receiving the microseismic data acquired by one or more multicomponent sensors; convolving the microseismic data with an operator that is applied to all of the components of the microseismic data; and applying a multicomponent filter operator to the convolved microseismic data. The microseismic data may result from human activity or be entirely natural. The filtering preserves the polarity of the received data whilst improving the signal-to-noise ratio of the filtered data.

Abstract: Methods and systems for the detection and localization of microseismic events are proposed which operate in real-time. Hypocenters in three spatial dimensions are provided along with an estimate of the event origin time. Sensor positions may be distributed in 3D space, and are not confined to linear arrays in vertical wells. A location of the event is approximated and a grid search, based on the approximate location of the event, is used to derive a residual function over a finer sampling followed by a gradient search of the residual function to optimize the location of the event.

Abstract: Methods and systems for the detection and localization of microseismic events are proposed which operate in real-time. Hypocenters in three spatial dimensions are provided along with an estimate of the event origin time. Sensor positions may be distributed in 3D space, and are not confined to linear arrays in vertical wells. A location of the event is approximated and a grid search, based on the approximate location of the event, is used to derive a residual function over a finer sampling followed by a gradient search of the residual function to optimize the location of the event.

Abstract: A method for the detection and localization of microseismic events is proposed which operates in real-time. It provides hypocenters in three spatial dimensions along with an estimate of the event origin time. Sensor positions may be distributed in 3D space, and are not confined to linear arrays in vertical wells. The method combines and improves two existing approaches. For detection and localization purposes the method makes use of the generalized beam-forming and forward modeling properties defined in the “CMM” algorithm. For location refinement, the method uses a stabilized version of the generalized “Geiger” approach.

Abstract: A method for characterizing fracture planes generated during a hydraulic fracturing process, comprises receiving microseismic data from the hydraulic fracturing process and processing a microseismic event cloud from the received microseismic data. This is followed by determining at least one reservoir geometry from the microseismic event cloud. The determination of geometry may consist of determining multiple candidate geometries and probability of each. In some forms of the invention the method may comprise postulating a set of candidate geometries with differing numbers of fracture planes, determining the most probable locations of the postulated fracture planes in each member of the set of candidate geometries and also determining relative probabilities of the candidate geometries in the postulated set. Determining a location of a fracture plane may comprise calculating a number density for each microseismic event, dependent on distance from some possible location of a fracture plane or fracture network.

Abstract: This invention provides a method for characterizing natural fracture networks or other textural networks in an Earth formation when using microseismic monitoring of a hydraulic fracturing job. The method comprises receiving (120) microseismic data from a hydraulic fracturing event, identifying a data subset (153) comprising components of the microseismic data associated with the one or more hydraulic fractures; and obtaining a remainder dataset (156) of the microseismic data by removing the subset from the microseismic data. One approach for identifying the data subset, after removing high uncertainty microseismic events, is to create a Voronoi diagram of a plurality of cells each associated with one of the microseismic events, determine a density for each cell, create a connectivity matrix of the high density cells and identify event clusters in the connectivity matrix which are aligned with a main growing direction of the hydraulic fracture.

Abstract: Methods and systems for the detection and localization of microseismic events are proposed which operate in real-time. Hypocenters in three spatial dimensions are provided along with an estimate of the event origin time. Sensor positions may be distributed in 3D space, and are not confined to linear arrays in vertical wells. A location of the event is approximated and a grid search, based on the approximate location of the event, is used to derive a residual function over a finer sampling followed by a gradient search of the residual function to optimize the location of the event.

Abstract: A method for the detection and localization of microseismic events is proposed which operates in real-time. It provides hypocenters in three spatial dimensions along with an estimate of the event origin time. Sensor positions may be distributed in 3D space, and are not confined to linear arrays in vertical wells. The method combines and improves two existing approaches. For detection and localization purposes the method makes use of the generalized beam-forming and forward modeling properties defined in the “CMM” algorithm. For location refinement, the method uses a stabilized version of the generalized “Geiger” approach.