Hybrid seismic inversion methods and apparatuses perform wave equation inversion and stochastic inversion to generate one or more final models for the reservoir characterization of the survey region. A method may include retrieving seismic data using seismic data recording sensors; storing the seismic data in the database; retrieving well data using the well born sensor in the wellbore; storing the seismic data in the database; storing geology integration information and one or more background models in the database; retrieving the seismic data and processing the seismic data to mitigate the seismic data for a seismic hybrid inversion; and performing the seismic hybrid inversion including performing wave equation inversion and stochastic inversion to generate the one or more final models for the reservoir characterization of the survey region.

A global objective function is initialized to an initial value. A particular model simulation process is executed using prepared input data. A mismatch value is computed by using a local function to compare an output of the particular model simulation process to corresponding input data for the particular model simulation process. Model objects associated with the particular model simulation process are sent to another model simulation process. An optimization process is executed to predict new values for input data to reduce the computed mismatch value.

Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

A system for estimating a rock property away from a well may include one or more hardware processors configured to access acquired three-dimensional (3D) seismic data that includes seismic traces from a 3D seismic survey of an area of interest. The system may also include a multi-head Convolutional Neural Network (CNN) model. The multi-head CNN model may include a plurality of kernels of various sizes for determining spatial and temporal relationships of the captured 3D seismic data at different resolutions. The multi-head CNN model may be trained to generate an estimated rock property value of a formation zone included in the area of interest, away from the well. The one or more hardware processors are further configured to update a drilling program for a production system based on the estimated rock property value. The drilling program may be executed on a computing device of the production system.

Systems, methods, and devices for wireless communication that support mechanisms for identifying hyper frame number (HFN) desynchronization conditions and/or for triggering HFN resynchronization in a wireless communication system. In aspects, an HFN desynchronization condition is identified based on Ethernet frame validation. For example, aspects of the present disclosure provide mechanisms for validating and Ethernet frame. An HFN desynchronization condition is identified or detected when an Ethernet frame is determined to be corrupt based on the Ethernet frame validation in accordance with aspects herein. In some aspects, such as in Ethernet header compression (EHC) protocol implementations, an HFN desynchronization condition may be identified based on a determination that a deciphered context identification (CID) is not a valid CID (e.g., is not a CID in a set of valid CIDs).

Systems and methods are disclosed that include generating reservoir property profiles corresponding to reservoir properties for pseudo wells based on reservoir data, generating seismic attributes for the pseudo wells, and training a machine learning model by comparing the reservoir property profiles against the seismic attributes. In this manner, the machine learning model may be used to predict reservoir properties for use with seismic exploration above a region of a subsurface that contains structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.

A method includes receiving a first transition probability matrix (TPM) of a subsurface region, wherein the TPM defines, for a given lithology at a current depth sample (or micro-layer), a probability of particular lithologies at a next depth sample (or micro-layer), receiving seismic data for the subsurface region, utilizing the first TPM and the seismic data to generate first pseudo wells, calculating a second TPM from the first pseudo wells, determining whether the second TPM is consistent with the first TPM, and utilizing the first pseudo wells to characterize a reservoir in the subsurface region when the second TPM is determined to be consistent with the first TPM.

A method can include accessing data associated with a well and one or more offset wells; based on at least a portion of the data, generating a set of distributions via parametric estimation, where the distributions are associated with a well-related activity and time; analyzing individual distributions in the set of distributions with respect to at least a portion of the data to pass or fail each of the individual distributions; and, for one or more passed individual distributions, outputting one of the passed individual distributions for the well.

A system to obtain information about a subsurface formation, in some embodiments, comprises an array of acoustic transmitters in a first well; a distributed acoustic sensing (DAS) fiber in a second well; and processing logic, in communication with the array of acoustic transmitters and the DAS fiber, that activates the array of acoustic transmitters and the DAS fiber so as to use the Doppler effect to obtain information about the subsurface formation.

One embodiment includes receiving distributed acoustic sensing (DAS) data for responses associated with seismic excitations in an area of interest. The area of interest includes a sea surface, the water column, a seafloor, and a subseafloor. The seismic excitations are generated by at least one seismic source in the area of interest. The responses are detected by at least one fiber optic sensing apparatus configured for DAS that is in the water column, on the seafloor, in a wellbore drilled through the seafloor and into the subseafloor, or any combination thereof. The embodiment includes determining a function of speed of sound in water using the DAS data, and correcting a digital seismic image associated with the area of interest using the function of speed of sound in water to generate a corrected digital seismic image.