Chevron U.s.a. Inc.

Prasenjit Roy

Reynaldo Cardona

David C Bartel

Anandaroop Ray

Kabilan Krishnamurthy

Xinfa Zhu

Christopher Hugh Skelt

Joseph Paul Stefani

James R Magill

Yang Zhang

Dileep K Tiwary

Donna J Venable

William Savran

Jacob G Tesfaye

James Magill

Bassel Mouloud Almoughraby

Yong Hua

Kun Fu

Ashley Elizabeth Shuler

Jacob Tesfaye

Well data (e.g., well log) may be divided into multiple segments, and different samplings of data in the individual segments may be performed to increase the amount of data that is used to train a seismic inversion model. Synthetic well data may be generated from real well data to increase the amount of well data from which sampling is performed.

Training data for machine learning seismic inversion

A method is described for inverting seismic data including obtaining well logs representative of subsurface volumes of interest; generating an amplitude variation with angle (AVA) database from the well logs by seismic modeling, wherein the seismic modeling is performed a plurality of times for all combinations of fluid substitutions of brine, oil, and gas and low porosity, mid-porosity, and high porosity; generating a trained AVA model using the AVA database; obtaining a seismic dataset; calibrating the seismic dataset; computing seismic attributes for the calibrated seismic dataset using statistics for AVA classification; and generating direct hydrocarbon indicators as a function of position in the subsurface volume of interest by applying the trained AVA model to the seismic attributes. The method is executed by a computer system.

System and method for seismic inversion

A method is described for seismic amplitude analysis including receiving a seismic dataset representative of a subsurface volume of interest wherein the seismic dataset includes an angle or angle stack dimension; select a plurality of sets of sub-cubes in the seismic dataset wherein each set of sub-cubes includes a plurality of the angles or the angle stacks; compute standard score statistics for each of the plurality of sub-cubes; identify amplitude variation with angle (AVA) anomalies based on the standard score statistics for each of the set of sub-cubes; classify the AVA anomalies to generate classified AVA anomalies; and displaying, on a user interface, the classified AVA anomalies as a graphical display. The method is executed by a computer system.

System and method for seismic amplitude analysis

Systems and methods for refining estimated effects of parameters on amplitudes are disclosed. Exemplary implementations may: (a) obtain ranges of parameter values for individual parameters within a subsurface region of interest; (b) generate a first model of the subsurface region of interest; (c) calculate a synthetic seismogram from the first model to determine corresponding amplitudes; (d) store results of applying the synthetic seismogram; (e) repeat steps (b)-(d) for multiple additional models; (f) obtain a subsurface distribution; (g) apply the subsurface distribution to the multiple models and the corresponding amplitudes; (h) generate a representation; and (i) display the representation.

Systems and methods for refining estimated effects of parameters on amplitudes

A method is described for creating a petro-elastic model including receiving, at a computer processor, well log data; computing in situ rock properties; calculating dry frame moduli for a range of porosities based on a rock physics model such as a modified critical porosity model; performing fluid substitutions and computing elastic properties by statistical sampling for the range of porosities; creating a petro-elastic model in a seismic domain; and identifying and producing target hydrocarbon reservoirs based on the petro-elastic model. The method may be executed by a computer system.

System and method for petro-elastic modeling

A method is described for porosity mapping of low porosity subsurface reservoirs using seismic data and well logs. The method may compute dry-frame rock properties using fluid substitution modeling based on the well log data; perform porosity expansion of the dry-frame rock properties based on rock physics to generate expanded rock properties; generate synthetic seismic data based on the expanded rock properties; calibrate the seismic dataset with the synthetic seismic data; generate an estimated porosity map based on the calibrated seismic dataset and the synthetic seismic data; and identify geologic features based on the estimated porosity map. The method may be executed by a computer system.

System and method for porosity estimation in low-porosity subsurface reservoirs

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Katy, TX, United States of America

Prasenjit Roy