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.

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.

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.

Methods and systems for performing log quality control on well data of non-key wells is provided. A method of identifying elastic facies in non-key wells as part of Log Quality Control (LQC) includes selecting one or more key wells, building a reference model of elastic facies using the well log data of the selected one or more key wells, propagating the reference model to well data of one or more non-key wells, benchmarking the well data of the non-key wells with the reference model, and calibrating the well data of the non-key wells with the reference model.

Seismic systems and processes for making and using same. In some examples, the seismic system can include an acoustic tool configured for placement in a wellbore penetrating a subterranean formation. The acoustic tool can include an acoustic transmitter unit for producing acoustic energy waveforms in the subterranean formation such that the interaction of the acoustic energy waveforms with structures in the formation can be an event that generates event signals. A receiver unit can include acoustic receivers distributed azimuthally around a central axis of the acoustic tool for sensing the event signals. The acoustic tool can determine event signal amplitudes of the event signals at each of the acoustic receivers, can determine which of the acoustic receivers has a maximum amplitude of the event signal amplitudes, and can interpolate between the maximum amplitude acoustic receiver and one or more adjacent acoustic receivers to find an azimuthal direction of the event.

Innovative aspects of the subject matter described in this specification may be embodied in methods that include obtaining a seismic data image. A first plane-wave destruction filter dip estimation is applied to the seismic data image to generate an initial dip model. A second plane-wave destruction filter dip estimation is applied to the seismic data image using the initial dip model to generate a pattern-guided dip estimation. The pattern-guided dip estimation is stored in a data store.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to generate a custom seismic surface and volume attribute. In one aspect, a method includes receiving a seismic cube and a seismic surface, and the seismic cube includes traces recorded at receivers deployed to collect seismic data. The seismic surface is picked on the seismic cube. Seismic wavelets are extracted with a selected length from the seismic cube along an intersection with the seismic surface for each spatial coordinate associated with the seismic surface. A reference wavelet is determined. A surface attribute map is generated based on comparing each of the seismic wavelets to the reference wavelet. A productivity of the seismic surface is evaluated using the surface attribute map.

The present disclosure is related to absolute strength and absolute sensitivity in seismic data. Seismic data from a marine survey can be received. The seismic data can be calibrated based on an absolute strength of a seismic source and an absolute sensitivity of a seismic receiver. The calibrated seismic data can be processed.

A computer-implemented method is described for a manner of geologic analysis using time-lapse seismic data. The method includes steps of receiving a first seismic attribute volume inverted from a seismic dataset recorded at a first time, a second digital seismic attribute volume inverted from a seismic dataset recorded at a second time, and a range of geological and geophysical parameters possible in the subsurface volume of interest; identifying a layer and area of interest; computing an attribute difference volume from the seismic attribute volumes; performing probabilistic attribute analysis of at least two of the first digital seismic attribute volume, the second digital seismic attribute volume, and the attribute difference volume using the range of geological and geophysical parameters; estimating time-lapse reservoir properties based on the probabilistic attribute analysis; and outputting visual information depicting the time-lapse reservoir properties via a user interface.

Systems and methods for refining estimated effects of parameters on amplitudes are disclosed. Exemplary implementations may: (a) obtain ranges of parameter values for individual parameters within a subsurface region of interest; (b) generate a first model of the subsurface region of interest; (c) calculate a synthetic seismogram from the first model to determine corresponding amplitudes; (d) store results of applying the synthetic seismogram; (e) repeat steps (b)-(d) for multiple additional models; (f) obtain a subsurface distribution; (g) apply the subsurface distribution to the multiple models and the corresponding amplitudes; (h) generate a representation; and (i) display the representation.