Computing device, computer instructions and method for estimating a broadband wavelet associated with a given seismic data set. The method includes receiving broadband seismic data; constructing and populating a misfit function; calculating the broadband wavelet based on the misfit function and the broadband seismic data; and estimating physical reservoir properties of a surveyed subsurface based on the broadband wavelet. The broadband wavelet is constrained, through the misfit function, by (1) an amplitude only long wavelet, and (2) an amplitude and phase short wavelet. The amplitude and phase short wavelet is shorter in time than the amplitude only long wavelet.

A method for characterizing a sand-pack or gravel-pack in a subsurface formation includes inducing a pressure change to induce tube waves in fluid in a well drilled through the subsurface formation. At a location proximate to a wellhead at least one of pressure and a time derivative of pressure in the well is measured for a selected length of time. At least one of a physical parameter and a change in the physical parameter with respect to time, of the sand-pack or gravel-pack, is determined using the measured pressure and/or the time derivative of pressure.

Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.

Systems and methods of placing wells in a hydrocarbon field based on seismic attributes and quality indicators associated with a subterranean formation of the hydrocarbon field can include receiving seismic attributes representing the subterranean formation and seismic data quality indicators. A cutoff is generated for each seismic attribute and seismic data quality indicator. A weight is assigned to each seismic attribute and seismic data quality indicator. The weighted seismic attributes and data quality indicators are aggregated for each location in the hydrocarbon field. A risk ranking is assigned based on the weighted seismic attributes and data quality indicators associated with each location in the hydrocarbon field based on the cutoffs. A map is generated with each location on the surface of the subterranean formation color-coded based on its assigned risk ranking.

A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.

The present disclosure relates to a method of processing seismic signals comprising: receiving a set of seismic signals, applying a wavelet transformation to the set of signals and generating transformed signals across a plurality of scales. Then for each scale determining coherence information indicative of the transformed signals and generating a comparison matrix comparing the transformed signals, then outputting seismic attribute information based on combined coherence information.

The present disclosure belongs to the field of capture, utilization, and storage of carbon dioxide, particularly relates to a method and system for intelligently identifying a carbon storage box based on a GAN network, and aims at solving the problem that the analysis accuracy of a fault zone area in the prior art is insufficient. The method comprises the steps: delineating seismic waveform data of a stable sedimentary area through a GAN network, and removing seismic waveform data points in the fault zone area; obtaining a stable sedimentary background seismic waveform data invertomer; obtaining a three-dimensional wave impedance prediction data volume; making a difference to obtain an abnormal wave impedance data volume; retaining abnormal wave impedance data of fault-karst in the three-dimensional variance attribute volume to obtain a fault-karst wave impedance data volume; and then obtaining a carbon storage box interpretation model.

A device may include a processor that may recover the signals misallocated in the deblending process of seismic data acquired with simultaneous sources. The processor may update the primary signal estimate based at least in part on a separation operation that separates coherence signals from noise signals in an output associated with the residual determined to be remaining energy for separation. The processor may be incorporated into the iterative primary signal estimate of the deblending process or be applied towards preexisting deblending output. In response to satisfying an end condition, the processor may transmit a deblended output that includes the weak coherence signals recovered from the misallocation or error in the primary signal estimate. The processor may also transmit the deblended output for use in generating a seismic image. The seismic image may represent hydrocarbons in a subsurface region of Earth or subsurface drilling hazards.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems for direct gas reservoir detection using frequency amplitude. One computer-implemented method includes spectrally decomposing seismic data associated with a target area into a plurality of mono-frequency volumes. Further, the method includes based on a low-frequency volume of the plurality of volumes, generating a low frequency map of the target area. Yet further, the method includes based on a high-frequency volume of the plurality of volumes, generating a high frequency map of the target area. Additionally, the method includes dividing the low frequency map by the high frequency map to generate a frequency ratio map. The method also includes using the frequency ratio map to identify a subsurface gas reservoir in the target area.

A method, computing system, and non-transitory computer-readable medium, of which the method includes receiving offset well data collected while drilling one or more offset wells, generating a machine learning model configured to predict drilling risks from drilling measurements or inferences, based on the offset well data, receiving drilling parameters for a new well, determining that the drilling parameters are within an engineering design window, generating a drilling risk profile for the new well using the machine learning model, and adjusting one or more of the drilling parameters for the new well, after determining the drilling parameters are within the engineering design window, and after determining the drilling risk profile, based on the drilling risk profile.