Exxonmobil Upstream Research Company

Exxonmobil Technology and Engineering Company

Exxonmobil Research and Engineering Company

Huseyin Denli

Cody J Macdonald

Kuang-Hung Liu

Wei D Liu

Diego A Hernandez

Victoria M Som De Cerff

Brent D Wheelock

Niranjan A Subrahmanya

Volkan Akcelik

Dimitar Vasilev Trenev

Michael H Kovalski

Jacquelyn Daves

Mary Johns

Alex Kanevsky

Jeremy S Brandman

Dimitar Trenev

A computer-implemented method for detecting geological elements or fluid in a subsurface from seismic images is disclosed. Seismic data may be analyzed to identify one or both of fluid or geologic elements in the subsurface. As one example, the analysis may include unsupervised learning, such as variational machine learning, in order to learn relationships between different sets of seismic data. For example, variational machine learning may be used to learn relationships among partially-stack images or among pre-stack images in order to detect hydrocarbon presence. In this way, an unsupervised learning framework may be used for learning a Direct Hydrocarbon Indicator (DHI) from seismic images by learning relationships among partially-stack or pre-stack images.

Detection of hydrocarbon presence in subsurface from seismic images using relational learning

A method and apparatus for seismic analysis include obtaining an initial geophysical model and seismic data for a subsurface region; producing a subsurface image of the subsurface region with the seismic data and the geophysical model; generating a map of one or more geologic features of the subsurface region by automatically interpreting the subsurface image; and iteratively updating the geophysical model, subsurface image, and map of geologic features by: building an updated geophysical model based on the geophysical model of a prior iteration constrained by one or more geologic features from the prior iteration; imaging the seismic data with the updated geophysical model to produce an updated subsurface image; and automatically interpreting the updated subsurface image to generate an updated map of geologic features. The method and apparatus may also include post-stack migration, pre-stack time migration, pre-stack depth migration, reverse-time migration, gradient-based tomography, and/or gradient-based inversion methods.

Automated seismic interpretation-guided inversion

A method and system of machine learning-augmented geophysical inversion includes obtaining measured data; obtaining prior subsurface data; (a) partially training a data autoencoder with the measured data to learn a fraction of data space representations and generate a data space encoder; (b) partially training a model autoencoder with the prior subsurface data to learn a fraction of model space representations and generate a model space decoder; (c) forming an augmented forward model with the model space decoder, the data space encoder, and a physics-based forward model; (d) solving an inversion problem with the augmented forward model to generate an inversion solution; and iteratively repeating (a)-(d) until convergence of the inversion solution, wherein, for each iteration: partially training the data and model autoencoders starts with learned weights from an immediately-previous iteration; and solving the inversion problem starts with super parameters from the previous iteration.

Machine learning-augmented geophysical inversion

A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.

Automated reservoir modeling using deep generative networks

A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.

Automated seismic interpretation systems and methods for continual learning and inference of geological features

A method and apparatus for modeling a subsurface region, including: obtaining a training set of geologically plausible models for the subsurface region; training an autoencoder with the training set; extracting a decoder from the trained autoencoder, wherein the decoder comprises a geologic-model-generating function; using the decoder within a data-fitting objective function to replace output-space variables of the decoder with latent-space variables, wherein a dimensionality of the output-space variables is greater than a dimensionality of the latent-space variables; and performing an inversion by identifying one or more minima of the data-fitting objective function to generate a set of prospective latent-space models for the subsurface region; and using the decoder to convert each of the prospective latent-space models to a respective output-space model. A method and apparatus for making one or more hydrocarbon management decisions based on the estimated uncertainty.

Subsurface models with uncertainty quantification

A method and apparatus for machine learning for use with automated seismic interpretation include: obtaining input data; extracting patches from a pre-extraction dataset based on the input data; transforming data of a pre-transformation dataset based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and generating augmented data from the extracted patches and the transformed data. A method and apparatus for machine learning for use with automated seismic interpretation include: a data input module configured to obtain input data; a patch extraction module configured to extract patches from a pre-extraction dataset that is based on the input data; a data transformation module configured to transform data from a pre-transformation dataset that is based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and a data augmentation module configured to augment data from the extracted patches and the transformed data.

Data augmentation for seismic interpretation systems and methods

A method and apparatus for seismic interpretation including machine learning (ML). A method of training a ML system for seismic interpretation includes: preparing a collection of seismic images as training data; training an interpreter ML model to learn to interpret the training data, wherein: the interpreter ML model comprises a geologic objective function, and the learning is regularized by one or more geologic priors; and training a discriminator ML model to learn the one or more geologic priors from the training data. A method of hydrocarbon management includes: training the ML system for seismic interpretation; obtaining test data comprising a second collection of seismic images; applying the trained ML system to the test data to generate output; and managing hydrocarbons based on the output. A method includes performing an inference on test data with the interpreter and discriminator ML models to generate a feature probability map representative of subsurface features.

Training machine learning systems for seismic interpretation

A method including: storing, in a computer memory, geophysical data obtained from a survey of a subsurface region; and extracting, with a computer, a subsurface physical property model by processing the geophysical data with one or more convolutional neural networks, which are trained to relate the geophysical data to at least one subsurface physical property consistent with geological prior information.

Geophysical inversion with convolutional neural networks

A method, including: storing, in a computer memory, seismic data acquired from a seismic survey of a subsurface region; and generating, with a computer, a final subsurface physical property model of the subsurface region by processing the seismic data with an iterative full wavefield inversion method, wherein the iterative full wavefield inversion method generates the final subsurface physical property model by iteratively applying a linear solver with a preconditioner that is generated from information from one or more previous iterations of the linear solver.

Krylov-space-based quasi-newton preconditioner for full-wavefield inversion

Inversion techniques are provided for imaging subsurface regions from geophysical data sets. The inversion techniques provide a geophysical data set containing data measured from a subsurface region to an objective function. These techniques further optimize the objective function to invert for an implicitly represented geologic structure of the subsurface region and at least one geophysical parameter of the subsurface region. In addition, the inversion techniques use a computer system to present results produced by the objective function optimization.

Shape-based geophysical parameter inversion

A method, including: obtaining an initial geophysical model; modeling a forward wavefield with viscoacoustic or viscoelastic wave equations; modeling an adjoint wavefield with adjoint viscoacoustic or adjoint viscoelastic wave equations, wherein the adjoint viscoacoustic wave equations are based on an auxiliary variable that is a function of pressure and a memory variable or the adjoint viscoelastic wave equations are based on a combination of stress and a memory variable, respectively; obtaining a gradient of a cost function based on a combination of a model of the forward wavefield and a model of the adjoint wavefield; and using the gradient of the cost function to update the initial geophysical model and obtain an updated geophysical model.

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Basking Ridge, NJ, United States of America

Huseyin Denli