The present disclosure provides a method of processing data obtained from distributed optical fiber sensors to detect acoustic energy generated by a poroelastic effect of fractures in a structure, such as a rock formation. The sensing fiber of an optical fiber distributed sensing system may be deployed in the vicinity of the region where fracturing is occurring, for example, along a well that is offset from a treatment well undergoing hydraulic fracturing. The DAS data obtained from along the sensing fiber is processed to measure changes in the low-frequency strain caused by the poroelastic effects in the rock as the fractures open and close. This measured strain rate data is iteratively processed at each instant time to identify fracture opening features (characterised as compression-tension-compression) that are correlated with fracture closing features (characterised as tension-compression-tension) as a function of depth, to thereby identify and locate fracture hits in the vicinity of the sensing fiber.

The present disclosure provides a method of processing data obtained from distributed optical fiber sensors to detect acoustic energy generated by a poroelastic effect of fractures in a structure, such as a rock formation. The sensing fiber of an optical fiber distributed sensing system may be deployed in the vicinity of the region where fracturing is occurring, for example, along a well that is offset from a treatment well undergoing hydraulic fracturing. The DAS data obtained from along the sensing fiber is processed to measure changes in the low-frequency strain caused by the poroelastic effects in the rock as the fractures open and close. This measured strain rate data is iteratively processed at each instant time to identify fracture opening features (characterised as compression-tension-compression) that are correlated with fracture closing features (characterised as tension-compression-tension) as a function of depth, to thereby identify and locate fracture hits in the vicinity of the sensing fiber.

Systems and methods of the present disclosure relate to calibration of resistivity logging tool. A method to calibrate a resistivity logging tool comprises disposing the resistivity logging tool into a formation; acquiring a signal at each logging point with the resistivity logging tool; assuming a formation model for a first set of continuous logging points in the formation; inverting all of the signals for unknown model parameters of the formation model, wherein the formation model is the same for all of the continuous logging points in the first set; assigning at least one calibration coefficient to each type of signal, wherein the calibration coefficients are the same for the first set; and building an unknown vector that includes the unknown model parameters and the calibration coefficients, to calibrate the resistivity logging tool.

Systems and methods of the present disclosure relate to calibration of resistivity logging tool. A method to calibrate a resistivity logging tool comprises disposing the resistivity logging tool into a formation; acquiring a signal at each logging point with the resistivity logging tool; assuming a formation model for a first set of continuous logging points in the formation; inverting all of the signals for unknown model parameters of the formation model, wherein the formation model is the same for all of the continuous logging points in the first set; assigning at least one calibration coefficient to each type of signal, wherein the calibration coefficients are the same for the first set; and building an unknown vector that includes the unknown model parameters and the calibration coefficients, to calibrate the resistivity logging tool.

A method is disclosed for radiaiiy profiling shear velocities of flexural wave modes in a formation. The method includes establishing sensitivity kernels with two non-dimensionalized parameters and using said sensitivity kernels to perform an inversion for radial shear wave velocity profiles. This method may be used for LWD, MWD, or wireline logging operations.

A method is disclosed for radiaiiy profiling shear velocities of flexural wave modes in a formation. The method includes establishing sensitivity kernels with two non-dimensionalized parameters and using said sensitivity kernels to perform an inversion for radial shear wave velocity profiles. This method may be used for LWD, MWD, or wireline logging operations.

A method and a system for well log data quality control is disclosed. The method includes obtaining a well log data regarding a geological region of interest, verifying an integrity and a quality of the well log data, determining the quality of the well log data based on a quality score of the well log data and making a determination regarding the access to the databases based on the quality of data. Additionally, the method includes performing the statistical analysis and the classification of well log data, a predictive and a prescriptive analysis of trends and predictions of the well log data, and generating an action plan for datasets with unsatisfactory quality scores.

A method for identifying a material behind a pipe string. The method may comprise disposing an acoustic logging tool into a wellbore, insonifying a pipe string within the wellbore with the acoustic logging tool, recording sonic or ultrasonic data. The method may further comprise inputting the sonic or ultrasonic data into trained a machine learning model and identifying the material behind the pipe string using the trained machine learning model.

A method for identifying a material behind a pipe string. The method may comprise disposing an acoustic logging tool into a wellbore, insonifying a pipe string within the wellbore with the acoustic logging tool, recording sonic or ultrasonic data. The method may further comprise inputting the sonic or ultrasonic data into trained a machine learning model and identifying the material behind the pipe string using the trained machine learning model.

A method can include accessing a trained machine model as trained to analyze digital seismic data of a region with respect to a structural feature of a geologic region; analyzing at least a portion of the digital seismic data using the trained machine model to generate results; and outputting the results as indicators of spatial locations of the structural feature of the geologic region.