Abstract: A correlated sparse nodes and mini-streamers system for collecting seismic data includes plural nodes distributed on the ocean bottom, and a mini-streamer spread that includes plural mini-streamers. The plural nodes and the mini-streamer spread are configured to simultaneously collect seismic data from a surveyed subsurface, and wherein a length of the mini-streamers is equal to or less than three times an inline distance between adjacent nodes of the plural nodes.

Abstract: Permeability values are estimated based on well logs using regression algorithms, such as gradient boosting and random forest. The training data is selected from well logs for which core-analysis-based permeability values are available. The estimated permeability values are used to plan hydrocarbon production. The well logs used to build the depth blended model may include total porosity, gamma ray, volume of calcite, density, resistivity, and neutron logs. Selecting the training data may include grouping the well logs according to regions expected to have similar characteristics, choosing a subset of the well logs corresponding to wells expected to provide stable models according to pre-determined criteria, and/or identifying training zones on the chosen well logs according to one or more rules. Validation and consistency checks may also be performed.

Abstract: Limitations in accuracy and computing power requirements impeding conventional Kirchhoff migration and reverse time migration are overcome by using the wave-equation Kirchhoff, WEK, technique with Kirchhoff migration. WEK technique includes forward-propagating a low-frequency wavefield from a shot location among pre-defined source locations, calculating an arrival traveltime of a maximum amplitude of the low-frequency wavefield, and applying Kirchhoff migration using the arrival traveltime and the maximum amplitude.

Abstract: Computing device, computer instructions and method for processing input seismic data d. The method includes receiving the input seismic data d recorded in a first domain by seismic receivers that are towed in water, the input seismic data d including pressure data and/or and particle motion data; generating a model p in a second domain to describe the input data d; processing the model p to generate an output particle motion dataset; and generating an image of the surveyed subsurface based on the output particle motion dataset.

Abstract: Methods and devices according to various embodiments perform full-wave inversion jointly for datasets acquired at different times over the same underground formation using a time-lag cost function with target regularization terms. This approach improves the 4D signal within reservoirs and suppresses 4D noise outside.

Abstract: One method interpolates simulated seismic data of a coarse spatial sampling to a finer spatial sampling using a neural network. The neural network is previously trained using a set of simulated seismic data with the finer spatial sampling and a subset thereof with the coarse spatial sampling. The data is simulated using an image of the explored underground formation generated using real seismic data. The seismic dataset resulting from simulation and interpolation is used for denoising the seismic data acquired over the underground formation. Another method demigrates seismic data at a sparse density and then increases density by interpolating traces using a neural network.

Abstract: A permutation that optimizes correspondence between the seismic data and the simulated data is computed using a graph space optimal transport formulation-based misfit. The seismic data or simulated data are transformed into auxiliary data by applying a portion of time shifts computed from the optimal permutation before updating the structural model of the explored underground formation. The full-waveform inversion minimization of the distance between auxiliary data and the seismic data or simulated data to which partial time shifts have not been applied, may be embedded in a Kantorovich-Rubinstein norm.

Abstract: A method for seismic exploration uses visco-acoustic FWI to model velocity and quality factor Q for an explored subsurface formation. The method employs frequency-dependent velocity to reduce cross-talk between Q and velocity and may be used for both isotropic and anisotropic media.

Abstract: A method for estimating in-situ porosity based on cutting images employs a neural network trained with labeled images, the labels indicating wireline porosity values. The method may be used to obtain porosity values along a vertical, deviated or horizontal well, where wireline logging data is not available or unreliable. The method uses machine learning. Training and validating the neural network may be ongoing processes in the sense that any new labeled image that becomes available can be added to the training set and the neural network being retrained to enhance its predictive performance.

Abstract: Methods and devices for seismic exploration of an underground structure apply W2-based full-wave inversion to transformed synthetic and seismic data. Data transformation ensures that the synthetic and seismic data are positive definite and have the same mass using an adaptive normalization. This approach yields superior results particularly when the underground structure includes salt bodies.

Abstract: Methods and systems for processing seismic data are presented. Primary wave (P) seismic data (PP data) and shear wave (P) seismic data (PS data) are jointly inverted as part of a nonlinear tomography process which adheres to one or more co-depthing constraints.

Abstract: Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.

Abstract: A non-blended dataset related to a same surveyed area as a blended dataset is used to deblend the blended dataset. The non-blended dataset may be used to calculate a model dataset emulating the blended dataset, or may be transformed in a model domain and used to derive sparseness weights, model domain masking, scaling or shaping functions used to deblend the blended dataset.

Abstract: A multi-sensor electromagnetic (EM) system and method for measuring gradients of EM signals. The multi-sensor EM system includes a frame; a transmitter device attached to the frame and configured to generate a primary EM field; a receiver device attached to the frame and configured to record a secondary EM field generated by the earth after being excited by the primary EM field; and a gradient sensor device attached to the frame and configured to record a gradient of the secondary EM field.

Abstract: Seismic exploration of an underground formation uses seismic excitations to probe the formation's properties such as reflectivity that can be imaged using reverse time migration. Using an equal area spherical binning at reflection points improves and simplifies RTM imaging together with adaptability to the data acquisition geometry, while overcoming drawbacks of conventional cylindrical binning.

Abstract: Methods and apparatuses for seismic data processing perform a least-squares reverse time migration method in which surface-attribute-independent coefficients for the surface attribute gathers are demigrated to reduce the computational cost.

Abstract: A reflection full waveform inversion method updates separately a density model and a velocity model of a surveyed subsurface formation. The method includes generating a model-based dataset corresponding to the seismic dataset using a velocity model and a density model to calculate an objective function measuring the difference between the seismic dataset and the model-based dataset. A high-wavenumber component of the objective function's gradient is used to update the density model of the surveyed subsurface formation. The model-based dataset is then regenerated using the velocity model and the updated density model, to calculate an updated objective function. The velocity model of the surveyed subsurface formation is then updated using a low-wavenumber component of the updated objective function's gradient. A structural image of the subsurface formation is generated using the updated velocity model.

Abstract: Seismic data is deblended by performing, for each receiver, a first inversion and a second inversion in a transform domain. The first inversion is formulated to minimize a number of non-zero coefficients of the first inversion result. A sub-domain of the transform domain is defined by vectors of a transform domain basis for which the first inversion has yielded the non-zero coefficients. The second inversion is performed in this sub-domain. The solution of the second inversion is used to extract deblended seismic datasets corresponding to each of the distinct signals, from the seismic data.

Abstract: Well completion is accomplished by obtaining a sample of geological material from the subsurface and generating primary data for the sample of geological material. The primary data include textural data, chemical data and mineralogical data. The primary data are used to derive secondary data for the sample of geological material, and the primary data and the secondary data are used to generate tertiary data for the sample of geological material. The tertiary data are a quantification of physical characteristics of the sample of geological material. The primary data, secondary data and tertiary data are used to determine a location of a stage along a well and an arrangement of perforation clusters in the stage.

Abstract: An exploration method starts from cuttings associated with sampling intervals and well data for a well in a subsurface formation. The cuttings are prepared and analyzed to extract textural and chemical/mineralogical data for plural fragments in each sample that is made of the cuttings in one sampling interval. The method then includes matching lithotypes of rock defined according to the textural and chemical/mineralogical data for each fragment with segments of the well data in the corresponding sampling interval to obtain correspondences between the lithotypes and depth ranges. The correspondences between the lithotypes and the depth ranges may be used as constraints for seismic data inversion.