Exxonmobil Upstream Research Company

Other

Exxonmobil Technology and Engineering Company

Jan Schmedes

Ratnanabha Sain

Charlie Jing

Rebecca L Saltzer

Anoop A Mullur

Christopher J DiCaprio

Garrett M Leahy

David D McAdow

Brent D Wheelock

Di Yang

Amit Kushwaha

Yunfei Yang

David D Mcadow

Rebecca Saltzer

Christopher Dicaprio

A method and system for modeling a subsurface region include applying a trained machine learning network to an initial petrophysical parameter estimate to predict a geologic prior model; and performing a petrophysical inversion with the geologic prior model, geophysical data, and geophysical parameters to generate a rock type probability model and an updated petrophysical parameter estimate. Embodiments include managing hydrocarbons with the rock type probability model. Embodiments include checking for convergence of the updated petrophysical parameter estimate; and iteratively: applying the trained machine learning network to the updated petrophysical parameter estimate of a preceding iteration to predict an updated rock type probability model and another geologic prior model; performing a petrophysical inversion with the updated geologic prior model, geophysical seismic data, and geophysical elastic parameters to generate another rock type probability model and another updated petrophysical parameter estimate; and checking for convergence of the updated petrophysical parameter estimate.

Petrophysical inversion with machine learning-based geologic priors

A method and apparatus for generating a fluid saturation model for a subsurface region. One example method generally includes obtaining a model of the subsurface region; for each of a plurality of fluid types: flooding the subsurface region model with the fluid type to generate a flood model; and running a trial petrophysical inversion with the flood model to generate a trial petrophysical model; identifying potential fluid contact regions in the trial petrophysical models; partitioning the subsurface region model at the identified potential fluid contact regions; and constructing the fluid saturation model from the partitioned subsurface region model.

Fluid saturation model for petrophysical inversion

A method and apparatus for generating an image of a subsurface region including obtaining geophysical data/properties for the subsurface region; resampling the geophysical data/properties to generate a resampled data set; iteratively (a) inverting the resampled data set with an initial prior model to generate a new model; and (b) updating the new model based on learned information to generate an updated prior model; substituting the initial prior model in each iteration with the updated prior model from an immediately-preceding iteration; and determining an end point for the iteration. A final updated model may thereby be obtained, which may be used in managing hydrocarbons. Inversion may be based upon linear physics for the first one or more iterations, while subsequent iterations may be based upon non-linear physics.

Reservoir characterization utilizing ReSampled seismic data

A method including: obtaining geophysical data for a subsurface region; generating, with a computer, at least two subsurface property models of the subsurface region for at least two subsurface properties by performing an inversion that minimizes a misfit between the geophysical data and forward simulated data subject to one or more constraints, the inversion including generating updates to the at least two subsurface property models for at least two different scenarios that both fit the geophysical data with a same likelihood but have different values for model materiality, with the model materiality being posed as an equality constraint in the inversion, wherein the model materiality is a functional of model parameters that characterize hydrocarbon potential of the subsurface region; analyzing a geophysical data misfit curve or geophysical data misfit likelihood curve, over a predetermined range of values of the model materiality to identify the at least two subsurface property models that correspond to a high-side and low-side, respectively, for each of the at least two subsurface properties, with the high-side and low-side quantifying uncertainties in the subsurface properties; and prospecting for hydrocarbons in the subsurface region with the at least two models that correspond to the high-side and the low-side for each of the at least two subsurface properties.

Reservoir materiality bounds from seismic inversion

Method for estimating geological properties in a subsurface region using multiple types of geophysical data (). An initial physical properties modelis constructed. Some parameters in the model are frozen () and optionally portions of the model wave number and spatial domains () and the data frequency and data time domains (), are also frozen. Then, a joint inversion () of the multiple data types is performed to calculate an update to the model only for the portions that are not frozen. The converged model () for this inversion is used as a new starting model, and the process is repeated (), possibly several times, unfreezing more parameters and data each time until the desired spatial and parameter resolution () has been achieved.

Domain freezing in joint inversion

Method for adaptive weighting of geophysical data types in iterative joint inversion to speed convergence and aid escape from local minima of the penalty (objective) function. Two or more geophysical data sets () representing a region of interest are obtained, and are jointly inverted to infer models of the physical properties that affect the particular types of data used. The misfit for each data type is a weighted tem in the penalty function (). The invention involves changing the weights () as the iteration cycles progress when the iteration convergence criteria are satisfied (), to see if they remain satisfied () with the modified penalty function.

Adaptive weighting of geophysical data types in joint inversion

Method for joint inversion of geophysical data to obtain 3-D models of geological parameters for subsurface regions of unknown lithology. Two or more data sets of independent geophysical data types are obtained, e.g. seismic and electromagnetic. Then they are jointly inverted, using structural coupling, to infer geophysical parameter volumes, e.g. acoustic velocity and resistivity. Regions of common lithology are next identified based on similar combinations of geophysical parameters. Then a joint inversion of the multiple data types is performed in which rock physics relations vary spatially in accordance with the now-known lithology, and 3-D models of geological properties such as shale content and fracture density are inferred. The computational grid for the last inversion may be defined by the lithology regions, resulting in average geological properties over such regions, which may then be perturbed to determine uncertainty in lithologic boundaries.

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Bellaire, TX, United States of America

Jan Schmedes