Abstract: A system and method of obtaining a high SNR seismic while-drilling data and a robust velocity profile of a geological site having a main well and at least one uphole located in the vicinity of the main well, the seismic profile being obtained from seismic waves generated by a drilling device located at the main well. The method comprises deploying at least one distributed acoustic fiber optic cable vertically in the at least one uphole, at least a portion of the fiber optic cable being positioned at a depth exceeding a predetermined depth below the surface, receiving seismic data at recording station positioned on the at least one fiber optic cable at at least the predetermined depth, generating, at a processor a high SNR seismic while-drilling signal; yielding a reliable velocity profile from the seismic data received, and determining a presence of near surface hazards from the generated high SNR while drilling seismic data.

Abstract: A system, a method, and a computer program for modelling a subsurface region of the earth for hydrocarbon exploration, development, or production, including receiving a seismic prestack dataset, determining one or more multiparameter attributes on a sparse grid based on the seismic prestack dataset, associating the one or more multiparameter attributes with color image pixels, encoding the associated one or more multiparameter attributes to generate a low-resolution colored image, inpainting the low-resolution colored image by a deep neural network to build a high-resolution colored image, and decoloring the high-resolution image.

Abstract: Methods for determination of elastic properties of geological formations using machine learning include extracting a first feature vector from data acquired during drilling. The data includes at least drilling parameters. The first feature vector is indicative of a drilling environment classification. A machine learning classification algorithm determines the drilling environment classification based on the first feature vector. A machine learning regression algorithm is selected from multiple machine learning regression algorithms based on the drilling classification. A second feature vector is extracted from the data acquired during drilling based on the drilling classification and the selected machine learning regression algorithm. The second feature vector is indicative of elastic properties of a geological formation. The selected machine learning regression algorithm determines the elastic properties of the geological formation based on the second feature vector.

Abstract: A method of evaluating processing imprint on seismic signals includes receiving a first and a second seismic dataset of a reservoir. A first and a second synthetic dataset are generated, where the second synthetic dataset is generated by multiplying at least a portion of data in the first synthetic dataset by a scaling factor. A first and a second combined dataset are generated by adding the respective seismic dataset and the respective synthetic dataset. A first and a second processed dataset are generated by applying a seismic processing step on the first and the second combined dataset, respectively. A difference factor between the first and the second processed dataset is calculated. Based on the difference factor and the scaling factor, it is determined whether the seismic processing step is able to preserve signal amplitude changes between the first and the second seismic dataset.

Abstract: Provided in some embodiments is a well torpedo system that includes a torpedo adapted to be advanced in a wellbore of a subterranean well. The torpedo including an integrated spool adapted to hold a fiber-optic (FO) umbilical including a FO line adapted to couple to a surface component, and an engine adapted to combust solid propellant to generate thrust to propel advancement of the torpedo in the wellbore.

Abstract: The disclosure provides systems and methods to enhance pre-stack data for seismic data analysis by: sorting the reflection seismic data acquired from cross-spread gathers into sets of data sections; performing data enhancement on the sets of data sections to generate enhanced traces by: (i) applying forward normal-moveout (NMO) corrections such that arrival times of primary reflection events become more flat, (ii) estimating beamforming parameters including a nonlinear traveltime surface and a summation aperture, (iii) generating enhanced traces that combine contributions from original traces in the sets of data sections, and (iv) applying inverse NMO corrections to the enhanced traces such that temporal rearrangements due to the forward NMO corrections are undone.

Abstract: Provided in some embodiments is a method of distributed acoustic sensing in a subterranean well. The method including advancing a torpedo into a first portion of a wellbore of a subterranean well (the torpedo including a distributed acoustic sensing (DAS) fiber-optic (FO) umbilical that is physically coupled to a surface component and adapted to unspool from the torpedo as the torpedo advances in the wellbore, and an engine adapted to generate thrust to propel the torpedo), and activating the engine to generate thrust to propel advancement of the torpedo within a second portion of the wellbore such that at least some of the DAS FO umbilical is disposed in the second portion of the wellbore.

Abstract: Provided in some embodiments is a well torpedo system that includes a torpedo adapted to be advanced in a wellbore of a subterranean well. The torpedo including an integrated spool adapted to hold a fiber-optic (FO) umbilical including a FO line adapted to couple to a surface component, and an engine adapted to combust solid propellant to generate thrust to propel advancement of the torpedo in the wellbore.

Abstract: A system, a method, and a computer program for modelling a subsurface region of the earth for hydrocarbon exploration, development, or production, including receiving a seismic prestack dataset, determining one or more multiparameter attributes on a sparse grid based on the seismic prestack dataset, associating the one or more multiparameter attributes with color image pixels, encoding the associated one or more multiparameter attributes to generate a low-resolution colored image, inpainting the low-resolution colored image by a deep neural network to build a high-resolution colored image, and decoloring the high-resolution image.

Abstract: A geologic survey system includes a plurality of acoustic sources spaced at intervals over a target area of a terranean surface. Each of the plurality of acoustic sensors is configured to generate a seismic energy wave. The system also includes a plurality of acoustic sensors positioned in a plurality of boreholes formed in a geologic formation, where the boreholes have a depth sufficient to reach a geologic datum. The system also includes a control system communicably coupled to the plurality of acoustic sensors and configured to perform operations including receiving, from the plurality of acoustic sensors, data associated with reflected acoustic signals generated by the plurality of acoustic sources and received by the plurality of acoustic sensors; determining, based on the received data, a subsurface topology of the geologic formation; and generating a subsurface model of the geologic formation based on the determined subsurface topology.

Abstract: A borehole is traversed through from an upper end of the borehole to substantially a lower end of the borehole. A cable attached to the everting liner is carried into the borehole. A tether attached to the everting liner is carried into the borehole. The tether is attached to an end of the everting liner that is configured to be at the downhole end of the borehole after traversing the borehole.

Abstract: A method of evaluating processing imprint on seismic signals includes receiving a first and a second seismic dataset of a reservoir. A first and a second synthetic dataset are generated, where the second synthetic dataset is generated by multiplying at least a portion of data in the first synthetic dataset by a scaling factor. A first and a second combined dataset are generated by adding the respective seismic dataset and the respective synthetic dataset. A first and a second processed dataset are generated by applying a seismic processing step on the first and the second combined dataset, respectively. A difference factor between the first and the second processed dataset is calculated. Based on the difference factor and the scaling factor, it is determined whether the seismic processing step is able to preserve signal amplitude changes between the first and the second seismic dataset.

Abstract: A geologic survey system includes a plurality of acoustic sources spaced at intervals over a target area of a terranean surface. Each of the plurality of acoustic sensors is configured to generate a seismic energy wave. The system also includes a plurality of acoustic sensors positioned in a plurality of boreholes formed in a geologic formation, where the boreholes have a depth sufficient to reach a geologic datum. The system also includes a control system communicably coupled to the plurality of acoustic sensors and configured to perform operations including receiving, from the plurality of acoustic sensors, data associated with reflected acoustic signals generated by the plurality of acoustic sources and received by the plurality of acoustic sensors; determining, based on the received data, a subsurface topology of the geologic formation; and generating a subsurface model of the geologic formation based on the determined subsurface topology.

Abstract: A geologic survey system includes a plurality of acoustic sources spaced at intervals over a target area of a terranean surface. Each of the plurality of acoustic sensors is configured to generate a seismic energy wave. The system also includes a plurality of acoustic sensors positioned in a plurality of boreholes formed in a geologic formation, where the boreholes have a depth sufficient to reach a geologic datum. The system also includes a control system communicably coupled to the plurality of acoustic sensors and configured to perform operations including receiving, from the plurality of acoustic sensors, data associated with reflected acoustic signals generated by the plurality of acoustic sources and received by the plurality of acoustic sensors; determining, based on the received data, a subsurface topology of the geologic formation; and generating a subsurface model of the geologic formation based on the determined subsurface topology.

Abstract: A geologic survey system includes a plurality of acoustic sources spaced at intervals over a target area of a terranean surface. Each of the plurality of acoustic sensors is configured to generate a seismic energy wave. The system also includes a plurality of acoustic sensors positioned in a plurality of boreholes formed in a geologic formation, where the boreholes have a depth sufficient to reach a geologic datum. The system also includes a control system communicably coupled to the plurality of acoustic sensors and configured to perform operations including receiving, from the plurality of acoustic sensors, data associated with reflected acoustic signals generated by the plurality of acoustic sources and received by the plurality of acoustic sensors; determining, based on the received data, a subsurface topology of the geologic formation; and generating a subsurface model of the geologic formation based on the determined subsurface topology.

Abstract: The detailed analysis of cross well seismic data for a gas reservoir in Texas revealed two newly detected seismic wave effects, recorded approximately 2000 feet above the reservoir. A tube-wave (150) is initiated in a source well (110) by a source (111), travels in the source well (110), is coupled to a geological feature (140), propagates (151) through the geological feature (140), is coupled back to a tube-wave (152) at a receiver well (120), and is and received by receiver(s) (121) in either the same (110) or a different receiving well (120). The tube-wave has been shown to be extremely sensitive to changes in reservoir characteristics. Tube-waves appear to couple most effectively to reservoirs where the well casing is perforated, allowing direct fluid contact from the interior of a well case to the reservoir.

Abstract: A seismic velocity profile in a region of interest in a subsurface formation is determined using at least the following steps. (a) Activating a seismic source at a location n, thereby exciting a wave in the subsurface formation. (b) Recording a wave signal trace unm(t) against time t, at a seismic receiver m. (c) Recording a wave signal trace unk(t) against time t at a seismic receiver k. (d) Cross correlating the wave signal traces unm(t) and unk(t) to obtain uconvnmnk(t). (e) Repeating these steps, for different locations n; (f) Summing uconvnmnk(t) over all locations n, to obtain a signal trace uvsmk(t) which corresponds to the signal received by the seismic receiver k from the virtual source at the position of seismic receiver m; (g) Deriving the seismic velocity based on the time of first arrival of the wave in uvsmk(t) and the predetermined distance between the seismic receiver m and the seismic receiver k.

Abstract: A method and system is described for estimating stress characteristics from seismic data. The method includes receiving seismic data acquired over a region, receiving properties of rock at a location within the region, and estimating one or more stress characteristics for a sub-region by combining the seismic data and the rock properties using a relationship between the stress characteristics in the sub-region and elastic stiffness and/or sonic velocity in the sub-region. The relationship is based on a non-linear elasticity theory.

Abstract: A method and system is described for estimating stress characteristics from seismic data. The method includes receiving seismic data acquired over a region, receiving properties of rock at a location within the region, and estimating one or more stress characteristics for a sub-region by combining the seismic data and the rock properties using a relationship between the stress characteristics in the sub-region and elastic stiffness and/or sonic velocity in the sub-region. The relationship is based on a non-linear elasticity theory.