US 12,585,037 B2 · Method and system for augmented inversion and uncertainty quantification for characterizing geophysical bodies

Abstract

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.

Claims24

Dependent claims

A computer-implemented method of performing geophysical inversion comprising:

performing a training step independent of measured data for a subsurface region to generate a machine-learned model, the training step using direct probabilistic modeling of geologic priors of at least one aspect of rock types, facies, or fluid types thereby tailoring the machine-learned model to the at least one aspect of rock types, facies, or fluid types;
accessing the measured data for the subsurface region;
solving an inversion problem by:
(i) inputting the measured data and an initial solution using the machine-learned model tailored in the at least one aspect to rock types, facies, or fluid types;
(ii) generating an inversion solution based on the measured data and the initial solution tailored in the at least one aspect to the rock types, the facies or the fluid types;
(iii) determining whether to continue iterating;
(iv) responsive to determining to continue iterating, iterating back to (ii) to generate the inversion solution that is updated and based on the measured data and the initial solution;
and
responsive to determining not to continue iterating, drilling a well or extracting hydrocarbons from the well using the inversion solution tailored in the at least one aspect to the rock types, the facies or the fluid types.

6 more elements

A computer-implemented method of machine learning-augmented geophysical inversion comprising:

accessing measured data for a subsurface region;
accessing prior subsurface data;
accessing conditioning data;
forming an augmented forward model independent of the measured data for the subsurface region based on:
a machine-learning model representing the prior subsurface data conditioned with the conditioning data, and
a physics model mapping output of the machine-learning model to seismic data thereby interlinking a direct physical relationship of at least one of rock types, facies, or fluid types with the seismic data;
initializing, using the augmented forward model, a plausible model by sampling latent space of a priori network model;
solving an inversion problem by:
(i) inputting the measured data and the plausible model using the augmented forward model tailored in the at least one aspect to rock types, facies, or fluid types;
(ii) generating, based on the measured data and the plausible model, one or more solutions which are consistent with the seismic data, the prior subsurface data and the conditioning data;
(iii) determining whether to continue iterating;
(iv) responsive to determining to continue iterating, iterating back to (ii) to generate the one or more solutions that is updated and based on the measured data and the plausible model;
and
responsive to determining not to continue iterating, drilling a well or using the well to extract hydrocarbons using the one or more solutions.

11 more elements

A computer-implemented method of machine learning-augmented geophysical inversion comprising:

obtaining measured data for a subsurface region;
obtaining prior subsurface data;
performing a training step independent of the measured data for the subsurface region using direct probabilistic modeling of geologic priors of at least one aspect of rock types, facies, or fluid types to generate an augmented forward model based on machine-learning with the prior subsurface data conditional on scenarios of at least one of geologic systems, rock types, facies, or fluid types;
solving an inversion problem by:
(i) inputting the measured data and different rock distributions as different initial solutions using the augmented forward model, wherein different conditions of at least one of rock types, facies, or fluid types are used as inputs to the augmented forward model in order for the augmented forward model to generate the different rock distributions;
(ii) generating multiple scenario solutions based on the measured data and the different initial solutions;
(iii) determining whether to continue iterating;
(iv) responsive to determining to continue iterating, iterating back to (ii) to generate the multiple scenario solutions that are updated and based on the measured data and the different initial solutions;
responsive to determining not to continue iterating, testing at least one of the multiple scenario solutions;
and
drilling a well or using the well to extract hydrocarbons using the at least one of the multiple scenario solutions.

8 more elements

Dependencies are inferred from the claim text.

Method and system for augmented inversion and uncertainty quantification for characterizing geophysical bodies

Life of this patent

Method and system for augmented inversion and uncertainty quantification for characterizing geophysical bodies

Life of this patent

Abstract

Claims24