Aspects of the disclosure provide for a method using clusters of sonic peaks from a logging tool to generate a log of an acoustic property of the formation as a function of depth.

A reverse time migration imaging method for cased-hole based on ultrasonic pitch-catch measurement, including: calculating a theoretical dispersion curve; expanding original Lamb data of two receivers into array waveform data based on phase-shift interpolation; establishing a two-dimensional migration velocity model including density, P-wave velocity and S-wave velocity of a target area; generating and storing a forward propagating ultrasonic wavefield for each time step; reversing a time axis; generating and storing a reversely propagating ultrasonic Lamb wavefield for the two receivers after phase-shift interpolation; calculating envelopes of the forward propagating ultrasonic Lamb wavefield and the reversely propagating ultrasonic Lamb wavefield; applying a zero-lag cross-correlation imaging condition to obtain reverse time migration imaging results; and applying Laplace filtering to suppress low-frequency imaging noises in the imaging results.

A reverse time migration imaging method for cased-hole based on ultrasonic pitch-catch measurement, including: calculating a theoretical dispersion curve; expanding original Lamb data of two receivers into array waveform data based on phase-shift interpolation; establishing a two-dimensional migration velocity model including density, P-wave velocity and S-wave velocity of a target area; generating and storing a forward propagating ultrasonic wavefield for each time step; reversing a time axis; generating and storing a reversely propagating ultrasonic Lamb wavefield for the two receivers after phase-shift interpolation; calculating envelopes of the forward propagating ultrasonic Lamb wavefield and the reversely propagating ultrasonic Lamb wavefield; applying a zero-lag cross-correlation imaging condition to obtain reverse time migration imaging results; and applying Laplace filtering to suppress low-frequency imaging noises in the imaging results.

A computer-implemented method, and system implementing the method, are disclosed for computing a final model of elastic properties, using nonlinear direct prestack seismic inversion for Poisson impedance. User inputs and earth-model data is obtained over points of incidence of a survey region, at various angles of incidence. Various models are then computed that serve for lithology identification and fluid discrimination and take part in preliminary seismic exploration and reservoir characterization. Therefore, further refinement of these models is required due to changes in burial depths, compaction and overburden pressure, as they provide limitations for reservoirs on porous media. The further refinement using nonlinear direct prestack seismic model is performed on a system computer, which produces a final model of elastic properties. This model can then be applied for lithology prediction and fluid detection to identify potential targets of oil and gas exploration and estimating spots in unconventional shale gas applications.

A computer-implemented method, and system implementing the method, are disclosed for computing a final model of elastic properties, using nonlinear direct prestack seismic inversion for Poisson impedance. User inputs and earth-model data is obtained over points of incidence of a survey region, at various angles of incidence. Various models are then computed that serve for lithology identification and fluid discrimination and take part in preliminary seismic exploration and reservoir characterization. Therefore, further refinement of these models is required due to changes in burial depths, compaction and overburden pressure, as they provide limitations for reservoirs on porous media. The further refinement using nonlinear direct prestack seismic model is performed on a system computer, which produces a final model of elastic properties. This model can then be applied for lithology prediction and fluid detection to identify potential targets of oil and gas exploration and estimating spots in unconventional shale gas applications.

A system includes a housing configured to be secured to the casing string. The housing has a ring shape defining a central orifice for passage of the fluid and an interior surface facing the central orifice. A reflectometer is mounted on the interior surface and is configured to emit a microwave signal into the fluid in the central orifice, receive a reflected microwave signal from the central orifice, and determine a microwave reflection parameter. An acoustic transceiver is also mounted on the interior surface and is configured to emit an acoustic signal into the fluid in the central orifice, receive a reflected acoustic signal from the central orifice, and determine an acoustic reflection parameter. A processor is configured to determine the property of the fluid from the microwave reflection parameter and the acoustic reflection parameter.

Disclosed is a method of quantitatively evaluating structural disturbance characteristics of present in-situ geo-stress in deep shale gas reservoirs, including: measuring geomechanics key parameters of key wells in different tectonic zones within a study area; performing interpretations of single-well profile rock mechanics and continuity of the in-situ geo-stress in magnitude and direction; establishing a geological model; performing anisotropic sequential Gaussian stochastic simulation to obtain three-dimensional (3D) heterogeneous rock mechanics parameter field distribution; performing prediction of distribution of geo-stress states in the study area, and calculating a stress structural index and stress disturbance factor of the target layer and a rotation degree of a maximum horizontal principal stress; and performing quantitative evaluation on an in-situ geo-stress structural disturbance and mapping.

A computer-implemented method for augmented inversion and uncertainty quantification for characterizing geophysical bodies is disclosed. The method includes machine-learning-augmented inversion that also facilitates the characterization of uncertainties in geophysical bodies. The method may further estimate wavelets without a well-log calibration, thereby enabling a pre-discovery exploration phase when well log data is unavailable. The machine learning component incorporates a priori knowledge about the subsurface and physics, such as distributions of expected rock types and rock properties, geological structures, and wavelets, through learning from examples. The methodology also allows for conditioning the characterization with the information extracted a priori about the geobodies, such as probabilities of rock types, using other analysis tools. Thus, the conditioning strategy may make the inversion more robust even when a priori distributions are not well balanced. Using the method, a scenario testing workflow may evaluate different candidate subsurface models, facilitating the management of uncertainty in decision-making processes.

A computer-implemented method for augmented inversion and uncertainty quantification for characterizing geophysical bodies is disclosed. The method includes machine-learning-augmented inversion that also facilitates the characterization of uncertainties in geophysical bodies. The method may further estimate wavelets without a well-log calibration, thereby enabling a pre-discovery exploration phase when well log data is unavailable. The machine learning component incorporates a priori knowledge about the subsurface and physics, such as distributions of expected rock types and rock properties, geological structures, and wavelets, through learning from examples. The methodology also allows for conditioning the characterization with the information extracted a priori about the geobodies, such as probabilities of rock types, using other analysis tools. Thus, the conditioning strategy may make the inversion more robust even when a priori distributions are not well balanced. Using the method, a scenario testing workflow may evaluate different candidate subsurface models, facilitating the management of uncertainty in decision-making processes.

A computer system and method for determining subterranean rock composition is described in which user input data is received having a plurality of parameters defining a desired subterranean rock composition from a wellbore. Data associated with at least one geologic environment is received, which data contains data acquired from at least one wellbore. An analytical analysis is then conducted by a computer processor utilizing the user input data and the received geologic environment data to determine a match between the user desired subterranean rock composition and the received geologic environment data. Output graphic data is then determined and generated, based at least in part on the analytical analysis, on a computer graphical display consisting of a two-dimensional (2D) graphical representation indicating a region of the geologic environment having a match between the user desired subterranean rock composition and the received geologic environment data.