A computer-implemented method for determining a velocity image of a domain of the subsurface structural geology in an oil and gas reservoir comprising an iterative method that combines a migration step and a population step propagating values from regions of the image wherein the velocity is known, or the velocity is according to information being deemed as correct. In this field of the technology this kind of data is also known as hard data. The iterative process uses a seismic image in depth of an initial guess as initial velocity model in a conjunction with tensor properties to generate a new velocity model based on criteria of minimum residual moveout on a specific point(s) or well control location(s). At these locations data information (velocity) is considered as well-known a-prior information to be propagated around the neighborhood guided taking into account the geological structure.

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.

Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. A lithology porosity machine-learning model may be trained and calibrating using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.

A method for evaluating brittleness of a deep shale reservoir and a computer readable storage medium. The method includes determining a Rickman brittleness index of the deep shale reservoir and determining an effective pressure of the deep shale reservoir according to a pore pressure and an overlying formation pressure of the deep shale reservoir. The Rickman brittleness index is adjusted to obtain the brittleness index of the deep shale reservoir according to an exponential relationship of the brittleness index with the effective pressure of the deep shale reservoir. Inherent properties, such as rock brittle mineral content and the like, are better indicated by the Rickamn brittleness index, and then the brittleness index of the deep shale reservoir is obtained by utilizing the exponential relationship of the brittleness index with the effective pressure of the deep shale reservoir, to realize reasonable evaluation for the brittleness of the deep shale reservoir.

An information processing system having a processor and a memory device coupled to the processor, wherein the memory device includes a set of instruction that, when executed by the processor, cause the processor to receive a multi-dimensional grid of acoustic or elastic impedances determined from seismic survey data associated with a subterranean formation, receive elastic property data that describes elastic property characteristics used to sort pseudo-components, and wherein the respective pseudo-components are formed of a combination of two or more lithologies. The instructions, when executed by the processor, further cause the processor to define select design variables using the impedance arrays, perform optimization operations for optimizing select design variables by applying the elastic property data as a part of a constitutive relation, and output a distribution of the pseudo-components to characterize volumetric concentrations of spatially grouped lithologies in a control volume of the subterranean formation.

The invention discloses a dolomite reservoir prediction method and system based on well and seismic combination, and storage medium. The method steps include: obtaining the dolomite index characteristic curve through well log sensitivity analysis, and distinguishing the dolomite and limestone according to the difference in their response range; after the artificial intelligence deep learning is performed on the dolomite index characteristic curve of the drilling area, the dolomite index characteristic curve of the virtual drilling area is obtained; according to the dolomite index characteristic curve of the drilling area and the virtual drilling area, the post-stack seismic data is used for inversion to obtain the distribution and development status of the dolomite reservoir in the test area. The invention effectively distinguishes the dolomite and limestone through the dolomite index characteristic curve, and accurately predicts the distribution and development status of the dolomite reservoir in the test area with less wells.

Methods, apparatuses, and computer-readable media utilize data stacking to facilitate identification and/or correction of data interpretation conducted for a subsurface formation. Related data sets, such as well logs, may be displayed along with markers representing a common entity in the related data sets, such as formation features in a surface formation, and a visualization of stacked data may be generated and centered on the markers to highlight mis-alignment of any of the markers.

A seismic rock physics inversion method based on a large area tight reservoir includes steps: building a multi-scale rock physics model; analyzing fluid sensitivities of rock physics parameters in two scales of acoustic logging and ultrasonic wave, and sifting the rock physics parameters that are most sensitive to a porosity and a gas saturation in a plurality of observation scales; building a single-well rock physics template, preferably a standard template; considering lateral variations and heterogeneity of reservoir geological features, fine-tuning input parameters of the rock physics template according to gas testing situations of all wells in a large work area, optimizing the whole work area and building a three-dimensional work area rock physics template data volume, and combining the data volume with pre-stack seismic inversion to calculate a porosity and a saturation of a target layer; and smoothing a result and finally outputting a reservoir parameter inversion data volume.

A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups.

A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.