The present invention relates to a method of prediction of hydrocarbon accumulation in a geological region comprising the following steps of: a. Generation of a geological basin model; b. Generation of a geomechanical model; c. Generation of an integrated model; d. Generation of a strain map based on the information obtained in steps a to c; e. Prediction of hydrocarbon accumulation from the strain maps.

Enhanced seismic velocity models are generated using a geomechanical model. A tomographic velocity model is generated based on raw seismic data. One or more initial seismic images are generated based at least partially on the tomographic velocity model. Geomechanical data and the initial seismic images are used to generate a geomechanical model. The geomechanical model produces geomechanical outputs that are used to generate a geomechanical velocity model. A second tomographic velocity model is generated based on the first tomographic velocity model and the geomechanical velocity model.

Systems and methods are disclosed for generating subsurface data as a function of position and time. Exemplary implementations may include obtaining a first initial subsurface model and a first set of subsurface parameters, obtaining training subsurface property data and a first training subsurface dataset, generating a first conditioned subsurface model, and storing the first conditioned subsurface model.

Systems and methods are disclosed for generating subsurface property data as a function of position and time. Exemplary implementations may include obtaining a first initial subsurface property model and a first set of subsurface property parameters, obtaining training input data and a first training subsurface property dataset, generating a first conditioned subsurface property model, and storing the first conditioned subsurface property model.

Systems and methods for quantitative seismic integrated modelling (QSIM) are disclosed for integrating the one, two and three-dimensional (1D, 2D, 3D) data from different geoscience domains within a framework in order to produce hi-resolution geocellular models that simulate realistic sub-surface reservoir properties. The QSIM systems and methods accurately leverage the seismically derived reservoir rock properties, integrating the geophysical, geological and engineering information through an optimum rock physics models and takes in consideration all the empirically constrained templates to correct, validate and quality check all the input data.

A method and apparatus for generating an image of a subsurface region including obtaining geophysical data/properties for the subsurface region; resampling the geophysical data/properties to generate a resampled data set; iteratively (a) inverting the resampled data set with an initial prior model to generate a new model; and (b) updating the new model based on learned information to generate an updated prior model; substituting the initial prior model in each iteration with the updated prior model from an immediately-preceding iteration; and determining an end point for the iteration. A final updated model may thereby be obtained, which may be used in managing hydrocarbons. Inversion may be based upon linear physics for the first one or more iterations, while subsequent iterations may be based upon non-linear physics.

A method may include obtaining an initial velocity model regarding a subterranean formation of interest. The method may further include generating various seismic migration gathers with different cross-correlation lag values based on a migration-velocity analysis and the initial velocity model. The method may further include selecting a predetermined cross-correlation lag value automatically using the seismic migration gathers and based on a predetermined criterion. The method may further include determining various velocity boundaries within the initial velocity model using a trained model, wherein the trained model is trained by human-picked boundary data and augmented boundary data. The method may further include updating, by the computer processor, the initial velocity model using the velocity boundaries, the automatically-selected cross-correlation lag value, and the migration-velocity analysis to produce an updated velocity model. The method may further include generating an image of the subterranean formation of interest using the updated velocity model.

Apparatus (10) and methods for combining time reversal and elastic nonlinearity of formation materials for qualtitatively probing for over-pressured regions down hole in advance of a well drilling bit, to determine the distance to the over-pressured region, and for accurately measuring pore pressure downhole in a formation, are described. Classical and reciprocal time reversal methods may be utilized to achieve these measurements.

The invention describes a procedure for determining the shale brittleness index from data obtained in the well by at least three well-logging tools measuring corresponding parameters. Three tools, namely sonic, density and deep resistivity, are selected. The time interval signals from the sonic tool are converted to the P-wave velocity. The product of signals obtained from the sonic and density tools (P-wave velocity×Bulk density=Acoustic impedance (AI)) responds in the same direction to a variation of the volume of water and organic matter (OM) volume of the rocks, whereas the third tool (Deep Resistivity) reacts very differently in response to a change of one or other of these same components, in a three-pole diagram, with rock matrix, OM and water as the three components onto an Acoustic Impedance vs resistivity ratio function plane. The resistivity ratio function is the square root of the ratio between the water resistivity and the measured formation resistivity. The position of the curved matrix-water line with OM=0 fraction by volume is fixed connecting the rock matrix point with that of the water point. The slope of the matrix-water curve is controlled by the tortuosity factor ‘a’ that is selected for a formation zone considering the pore structure, grain size and level of compaction. The data points to be analysed can be calibrated accordingly by iterating the resistivity of water (Rw) and occasionally the tortuosity factor (a) parameter to obtain the Rw value. In a graph where the parameters used depend, for example, on the sonic velocity in the rock, the rock bulk density and on the electric resistivity of the formations, the iso quartz/calcite-content lines are denoted as iso-brittleness line as with an increase in quartz/calcite content, both organic content and porosity decrease, resulting in an increase in brittleness. These iso-brittleness lines form a set of parallel curved lines intersecting the matrix-water reference curved line. Brittleness is derived from that graph corresponding to each pair of values of the parameters measured in the well.

The invention describes a procedure for determining the subsurface total organic content (TOC) from data obtained in the well by at least three well-logging tools measuring corresponding parameters. Three tools, namely sonic, density and deep resistivity are selected. The time interval signals from the sonic tool are converted to the P-wave velocity. The product of signals obtained from the sonic and density tools (P-wave velocity×Bulk density=Acoustic Impedance (AI)) responds in the same direction to a variation of the volume of water and organic matter (OM) volume of the rocks, whereas the third tool (Deep Resistivity) reacts very differently in response to a change of one or other of these same components, in a three-pole diagram, with rock matrix, OM and water as the three components onto an Acoustic Impedance vs resistivity ratio function plane. The resistivity ratio function is the square root of the ratio between the water resistivity and the measured formation resistivity. The position of the curved line with OM=0% by volume is fixed connecting the rock matrix pole with that of water pole. The slope of the matrix-water curve is controlled by the tortuosity factor ‘a’ that is a function of the rock pore structure, grain size and level of compaction. Iso-OM curves run parallel to this 0% OM reference curve. The data points to be analysed can be calibrated accordingly by changing the resistivity of water (Rw) and the tortuosity factor (a) parameters. In a graph where the parameters used depend, for example, on the sonic velocity in the rock, the rock bulk density and on the electric resistivity of the formations, the iso-OM lines form a set of parallel curved lines. The OM is derived from there corresponding to each pair of values of the parameters measured in the well. The obtained organic matter volume is converted to Total organic carbon (TOC) in gram percentage using a conventional relation.