Patent Description:
This disclosure relates generally to the field of geophysical prospecting and, more particularly, to prospecting for hydrocarbons and related data processing. Specifically, exemplary embodiments relate to methods and apparatus for generating subsurface models of rock properties applicable at multiple scales, such as seismic scales, sub-seismic scales, and well log scales.

This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present disclosure. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.

An important goal of geophysical prospecting is to accurately image subsurface structures to assist in the identification and/or characterization of hydrocarbon-bearing formations. Geophysical prospecting may employ a variety of data-acquisition techniques, including seismic prospecting, electromagnetic prospecting, well logging, etc. Such data may be processed, analyzed, and/or examined with a goal of identifying geological structures that may contain hydrocarbons.

Geophysical data (e.g., acquired seismic data) and/or reservoir surveillance data (e.g., well logs) may be analyzed to develop subsurface models (e.g., models of geology, including rock types). For example, one or more inversion procedures may be utilized to analyze the geophysical data and produce models of rock properties and/or fluid properties. Generally, inversion is a procedure that finds a parameter model, or collection of models, which, through simulation of some physical response to those parameters, can reproduce to a chosen degree of fidelity a set of measured data. Inversion may be performed, for example, on seismic data to derive a model of the distribution of elastic-wave velocities within the subsurface of the earth. Naive parameterization of a subsurface model (e.g., by uniform discretization) may utilize many volume elements (voxels) of uniform elastic-wave velocities to match simulated data to the observed seismic data.

Non-uniqueness is a pervasive feature of geophysical inversion problems. Geophysical surveys typically acquire data at locations remote from the subsurface region of interest (e.g., at the surface of the earth or a body of water) and at narrow frequency bands (e.g., from about <NUM> to about <NUM>) due to the physical limitations of the survey (e.g., to generate lower frequencies, impractically large sources may be utilized, while mechanical loss and wavefield scattering tend to attenuate seismic waves at higher frequencies). These limitations lead to incomplete information and large uncertainty about the subsurface region of interest.

Some recently-proposed geophysical data analysis methods utilize machine learning. For example, horizon interpretation and/or fault interpretation problems have been staged as machine leaming tasks, where a set of manually-labelled horizon images and/or fault images are part of training data. Typically, machine learning systems utilize an objective function to characterize the error between manually-labeled images and predicted labeling.

Petrophysical inversion generally transforms elastic parameters, such as seismic velocity and density, to petrophysical parameters, such as porosity and volume of clay (Vclay). For example, petrophysical inversion can transform compressional velocity, shear velocity, and density well logs to porosity and/or Vclay logs. As another example, petrophysical inversion can utilize elastic information from seismic data, including traditional images of reflectivity and tomographic velocity, to predict three-dimensional volumes of porosity and Vclay. (Elastic information may be determined from seismic data by any suitable means, including in some cases by seismic inversion to solve for an elastic or similar geophysical properties model based on input seismic data. ) As used herein, Vclay refers to rock volumes including anything that is not sand (e.g., shale). That is, we will treat clay and shale (and associated properties such as Vclay and Vshale) interchangeably with the recognition that they are not strictly the same from a mineralogical standpoint. For the present application's purposes, however, it is suitable to treat them interchangeably as one of the volumetric mineral end-members of subsurface rocks, the other one being sand. Furthermore, petrophysical inversion can include other geophysical data types, namely electromagnetic data or resistivity, which tend to have a better sensitivity to water saturation than elastic parameters. Although petrophysical inversion may be carried out with input elastic information or elastic parameters (which may, as noted, be determined from seismic data via, e.g., seismic inversion), or carried out with input electromagnetic data or resistivity as just noted, in some cases petrophysical inversion may be used to determine petrophysical parameters from input seismic data. In such a case, the petrophysical inversion may be referred to as an "integrated petrophysical inversion" insofar as it encompasses inversion sometimes associated with seismic inversion processes (e.g., determining elastic parameters from seismic data).

Seismic data is typically sampled in a limited frequency band (e.g., about <NUM> to about <NUM>). Rock properties predicted from seismic and/or petrophysical inversion (including integrated petrophysical inversion) may maintain the bandlimited nature of the seismic data, resulting in smooth representations of sharp layer boundaries. Attribute calibration workflows, which are often uncertain, are typically used to estimate layer thickness from the smooth representations. Layer thickness is useful for reservoir assessment, geologic model building, well planning, and other aspects of hydrocarbon management, including prospecting, exploration, and development. However, layer thickness and petrophysical property estimates may become inaccurate as thickness approaches the detectability limit.

Petrophysical inversion may be performed on data obtained (and/or performed on parameters derived from data obtained) at typical seismic frequency bands. However, resolution may be lacking at higher frequencies (e.g., frequencies larger than ~ <NUM>), resulting in a lack of resolution at finer spatial scales, known as sub-seismic resolution (e.g., less than about <NUM> spacial scale in the vertical direction, meaning that it is possible to resolve a sand or other geological feature that is thinner than <NUM> in the petrophysical inversion carried out using such data). Resolution at these sub-seismic scales is important for understanding the flow behavior of a reservoir, e.g. fluctuation of properties on the order of <NUM> in the depth domain. Although a variety of algorithms are known for estimating properties at sub-seismic resolution scales from a petrophysical inversion, none provide certainty. For example, several different models may have the same low frequency (e.g., less than about <NUM>) components as the inversion result while having different spatial components (e.g., layer thickness) at sub-seismic resolution scales.

Moreover, existing approaches useful for estimating petrophysical parameters may not be capable of identifying rock types with certainty. For example, rock types identified at seismic resolution scales may not extend to well log resolution scales, e.g., on the order of <NUM> to <NUM>. Current implementations may only be able to predict simplistic rock types.

More efficient equipment and techniques to identify rock types and/or rock type probabilities from petrophysical inversion would be beneficial.

<NPL>), discloses a technique for lithology/fluid prediction and simulation from prestack seismic data in a Bayesian framework. Lithography/fluid classes are determined along 1D profiles through a reservoir target zone. A stationary Markov-chain model is used to model vertical continuity of lithography/fluid classes along the profile. The likelihood relates the lithography/fluid classes to the elastic properties and to the seismic data, and introduces vertical correlation because the seismic data are band-limited. An approximation of the likelihood provides an approximate posterior model that is a Markov chain. The approximate posterior can be assessed by an exact and efficient recursive algorithm. The lithography/fluid inversion approach is evaluated on a synethic 1D profile that is inspired by a North Sea Sandstone reservoir. The study demonstrates the impact of a vertically coupled prior Markov model for the lithology/fluid classes.

<CIT> discloses a method that includes storing, in a computer memory, geophysical data obtained from a survey of a subsurface region. The method includes extracting, with a computer, a subsurface physical property model by processing geophysical data with one or more convolutional neural networkds, which are trained to relate the geophysical data to at least one subsurface physical property consistent with geological prior information.

<CIT> discloses a method that includes obtaining geophysical data for a subsurface region and 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 misfits between the geophysical data and forward simulated data subject to one or more constraints. The inversion includes 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 materiality, with the model materiality being posed as an equality constraint in the inversion. The model materiality is a function of model paraeters that characterize hydrocarbon potential of the subsurface region. The method includes analyzing a geophysical data misfit curve or geophysical data misfit likelihood curve over a pre-determined 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. The method includes 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.

The present invention relates to a method for modeling a subsurface region as described in claim <NUM>. Further advantages of the method are presented in the dependent claims. Embodiments of the present invention provide enhanced methods for estimating rock properties. Better estimation of rock properties improves results from geophysical modeling and/or interpretation (e.g., identification of geologic features, faults, horizons, salt domes, etc.). For example, rock type probability models exhibit sharper boundaries than seismic data models, thereby facilitating more precise interpretation. Such rock type probability models facilitate sharp, geologically-consistent predictions for object extraction by incorporating geological priors and/or interpreters' expectations into training for learning seismic patterns. Machine learning technology is utilized to automatically infer rock types from petrophysical parameters in the context of a sequence labeling problem. Embodiments enhance the automation of generation of subsurface models. Embodiments include modeling a subsurface region by 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.

So that the manner in which the recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only exemplary embodiments and are therefore not to be considered limiting of its scope, may admit to other equally effective embodiments.

It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used herein, the singular forms "a," "an," and "the" include singular and plural referents unless the content clearly dictates otherwise. Furthermore, the words "can" and "may" are used throughout this application in a permissive sense (i.e., having the potential to, being able to), not in a mandatory sense (i.e., must). The term "include," and derivations thereof, mean "including, but not limited to. " The term "coupled" means directly or indirectly connected. The term "uniform" means substantially equal for each sub-element, within about ±<NUM>% variation. The term "nominal" means as planned or designed in the absence of variables such as wind, waves, currents, or other unplanned phenomena. "Nominal" may be implied as commonly used in the fields of seismic prospecting and/or hydrocarbon management.

The term "seismic data" as used herein broadly means any data received and/or recorded as part of the seismic surveying process, including particle displacement, velocity and/or acceleration, continuum pressure and/or rotation, wave reflection, and/or refraction data; but "seismic data" also is intended to include any data or properties, including geophysical properties such as one or more of: elastic properties (e.g., P and/or S wave velocity, P-Impedance, S-Impedance, density, and the like); seismic stacks (e.g., seismic angle stacks); compressional velocity models; or the like, that the ordinarily skilled artisan at the time of this disclosure will recognize may be inferred or otherwise derived from such data received and/or recorded as part of the seismic surveying process. Thus, we may at times refer to "seismic data and/or data derived therefrom," or equivalently simply to "seismic data. " Both terms are intended to include both measured/recorded seismic data and such derived data, unless the context clearly indicates that only one or the other is intended. "Seismic data" may also include data derived from traditional seismic (i.e., acoustic) datasets in conjunction with other geophysical data, including, for example, gravity plus seismic, gravity plus electromagnetic plus seismic data, etc. For example, joint-inversion utilizes multiple geophysical data types.

As used herein, "inversion" refers to a geophysical method which is used to estimate subsurface properties (such as elastic properties like velocity or density). Typically, inversion begins with a starting subsurface physical properties model. Synthetic seismic data may be generated (e.g., by solving a wave equation, in order to simulate "waves" passing through the modeled subsurface with the starting physical properties). The synthetic seismic data generated by this simulation are compared with the field seismic data, and, using the differences between the two, the value of an objective function is calculated. To minimize the objective function, a modified subsurface physical properties model is generated which is used to simulate a new set of synthetic seismic data. This new set of synthetic seismic data is compared with the field data to recalculate the value of the objective function. Typically, an objective function optimization procedure is iterated by using the new updated model as the starting model for finding another search direction, which may then be used to perturb the model in order to better explain the observed data. The process continues until an updated model is found that satisfactorily explains the observed data. A global or local optimization procedure can be used to minimize the objective function and to update the subsurface model. Commonly used local objective function optimization procedures include, but are not limited to, gradient search, conjugate gradients, quasi-Newton, Gauss-Newton, and Newton's method. Commonly used global methods include, but are not limited to, Monte Carlo or grid search. Inversion may also refer to joint inversion with multiple types of data used in conjunction. Specific inversion techniques may include Full Wavefield Inversion (seismic or electromagnetic), seismic tomography, seismic velocity model building, potential fields inversion, reservoir history matching, and any combination thereof.

The term "physical property model" or other similar models discussed herein refer to an array of numbers, typically a <NUM>-D array (although it may instead be a <NUM>-D array), where each number, which may be called a model parameter, is a value of velocity, density, or another physical property in a cell, where a subsurface region has been conceptually divided into discrete cells for computational purposes. For example, a <NUM>-D geologic model may be represented in volume elements (voxels), in a similar way that a <NUM>-D photograph is represented by picture elements (pixels). However, it should be appreciated that where a "pixel" is referenced, it should be understood that the term "voxel" can equivalently be substituted to extend the concept to the context of the <NUM>-D case, and vice-versa, that where a "voxel" is referenced, the term "pixel" can equivalently be substituted to extend the referenced concept into the context of the <NUM>-D case.

As used herein, "hydrocarbon management" or "managing hydrocarbons" includes any one or more of the following: hydrocarbon extraction; hydrocarbon production, (e.g., drilling a well and prospecting for, and/or producing, hydrocarbons using the well; and/or, causing a well to be drilled to prospect for hydrocarbons); hydrocarbon exploration; identifying potential hydrocarbon-bearing formations; characterizing hydrocarbon-bearing formations; identifying well locations; determining well injection rates; determining well extraction rates; identifying reservoir connectivity; acquiring, disposing of, and/or abandoning hydrocarbon resources; reviewing prior hydrocarbon management decisions; and any other hydrocarbon-related acts or activities. The aforementioned broadly include not only the acts themselves (e.g., extraction, production, drilling a well, etc.), but also or instead the direction and/or causation of such acts (e.g., causing hydrocarbons to be extracted, causing hydrocarbons to be produced, causing a well to be drilled, causing the prospecting of hydrocarbons, etc.).

As used herein, "obtaining" data or models generally refers to any method or combination of methods of acquiring, collecting, or accessing data or models, including, for example, directly measuring or sensing a physical property, receiving transmitted data, selecting data from a group of physical sensors, identifying data in a data record, generating models from assemblages of data, generating data or models from computer simulations, retrieving data or models from one or more libraries, and any combination thereof.

The term "label" generally refers to identifications and/or assessments of correct or true outputs provided for a given set of inputs. Labels may be of any of a variety of formats, including text labels, data tags (e.g., binary value tags), pixel attribute adjustments (e.g., color highlighting), n-tuple label (e.g., concatenation and/or array of two or more labels), etc..

If there is any conflict in the usages of a word or term in this specification and one or more patent or other documents that may be cited herein, the definitions that are consistent with this specification should be adopted for the purposes of understanding this disclosure.

Embodiments of the present disclosure provide enhanced systems and methods for estimating rock properties. One of the many potential advantages of the disclosed embodiments include better estimation of rock properties that may directly enable improved results from geophysical modeling and/or interpretation (e.g., identification of geologic features, faults, horizons, salt domes, etc.). For example, rock type probability models may exhibit sharper boundaries than seismic data models, thereby facilitating more precise interpretation. Other potential advantages include one or more of the following, among others that will be apparent to the skilled artisan with the benefit of this disclosure: producing sharp, geologically-consistent predictions for object extraction; incorporating geological priors and/or interpreters' expectations (e.g., image priors) into training for leaming seismic patterns (especially training of a machine learning system); mitigating uncertainty in rock type probability models with the use of additional data, such as geologic priors (geological information that was available before the solution was formed and which was incorporated into the solution), well logs, and/or joint inversion of different geophysical data sets; utilizing machine learning technology to automatically infer rock types from petrophysical parameters in the context of a sequence labeling problem; and enhanced automation of procedures for generating subsurface models. Such automation may accelerate the generation of subsurface models, reduce subjective bias or error, and reduce the geoscience workforce's exposure to ergonomic health risks (e.g., exposure to repetitive tasks and injuries therefrom). Embodiments of the present disclosure can thereby be useful in hydrocarbon management, including in the prospecting for, discovery of, and/or extraction of hydrocarbons from subsurface formations.

Embodiments disclosed herein include utilizing a machine learning system to infer rock type from petrophysical parameters. For example, a deep neural network (DNN) may be trained to infer rock type from petrophysical parameters. Training data for a DNN may, in various embodiments, include synthetically generated subsurface physical property models consistent with provided geological priors. The computer-simulated data may be based on the governing equations of geophysics and the generated subsurface physical property models. The training data for the DNN may include migrated or stacked geophysical (e.g., seismic) data with interpretations (e.g., labeling) done manually. The DNN may be trained using a combination of synthetic and acquired geophysical data. The DNN may represent the rock types and/or petrophysical parameters as a nested hierarchy of concepts, with each concept defined in relation to simple concepts, and more abstract representations computed in terms of less abstract ones.

<FIG> illustrates an exemplary method <NUM> of petrophysical inversion with machine leaming-based geologic priors according to some embodiments. As illustrated, method <NUM> begins at block <NUM> where a training dataset is created. The training dataset may exhibit plausible geologic behavior relevant to the subsurface region of interest, including petrophysical parameters (e.g., porosity, permeability, density, resistivity, elastic wave velocities, etc.) and corresponding rock types. The training dataset may comprise actual field-recorded data, or interpretations thereof, in geologic model form, and/or models resulting from computer simulations of earth processes. The training dataset may comprise multiple petrophysical parameters. For example, the training dataset may include a tabular listing of petrophysical parameters and potentially corresponding rock types. As another example, the training dataset may include a listing of petrophysical parameters and probability-weighted listings of pluralities of potentially corresponding rock types. As another example, the training dataset may include charts, graphs, and/or other data structures relating petrophysical parameters to potentially corresponding rock types. As yet another example, the training dataset may include representations of subsurface regions (e.g., models and/or images) with identified rock types (e.g., labels). In some embodiments, a combination of any two or more of these types of datasets may be included in the training dataset.

In some embodiments, the training datasets may be generated from existing datasets (e.g., representations of known subsurface regions). For example, existing subsurface data may be manually and/or automatically labeled to identify petrophysical parameters and corresponding rock types. In some embodiments, the training datasets may be generated by simulation to synthesize subsurface data, including petrophysical parameters and corresponding rock types. In some embodiments, a combination of any two or more of these methods may be utilized to generate the training dataset. Note that a robust training dataset may be characterized as including representations (e.g., <NUM>-D pseudo wells, <NUM>-D models created by process stratigraphy, etc.) of subsurface regions (actual or simulated) at a one or more scales (e.g., grid spacing of <NUM>, <NUM>, and <NUM>) and/or frequency regions (e.g., for seismic data with a maximum frequency of about <NUM>, selected frequency regions may include <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>). For example, four or five frequency ranges may be utilized. In some embodiments, one or more sets of frequencies and/or mixtures thereof may be utilized (e.g., broadband). In some embodiments, one or more frequencies may be selected (e.g., randomly) for each trace from a large number of frequencies in a pre-defined range. For example, the training dataset may be generated by creating synthetic <NUM>-D, <NUM>-D, or <NUM>-D volumes of petrophysical parameters (at the same sampling scale as will be used with the inversion) and rock types (at various scales), and by filtering the petrophysical parameters at various frequencies to get a multitude of subsets, each with the multitude of rock types at various scales. The final training set may consist of sets of petrophysical parameters at various frequencies and various scales of rock types.

In some embodiments, the training dataset created at block <NUM> may include large volumes of data suitable for use with a deep learning algorithm. Suitable deep learning systems and methods are further described in co-pending <CIT>. In some embodiments, the learning process may utilize a large-volume training dataset to fit numerous parameters. It should be appreciated that large volumes of data having appropriate petrophysical parameter and/or rock property information may be rare in typical oil and gas operations. For example, an appropriate training dataset may include thousands of well logs, each having various rock type labels, as provided by one or more experienced geoscientists. It should be appreciated that such well data is sparse in typical offshore projects. Even for onshore projects with a multitude of wells, labelling all of the rock types is a daunting and often impractical task. Consequently, in some embodiments the training dataset may include many (e.g., thousands) of synthetic well logs. For example, the synthetic well logs may be generated using an existing forward model, resulting in geologically-plausible data. In some embodiments, pre-defined distributions of rock properties for different rock types and/or a single-order transition matrix (meant to mimic geologic stacking patterns) may be utilized as input to generate the synthetic well logs. In some embodiments, the synthetic data may include various datasets having different frequency content (seismic and sub-seismic) and also different samplings (well log and geologic model scale) of rock types.

Methods according to some embodiments may complete after the training dataset is created at block <NUM>. For example, one or more training datasets may be created, cataloged, stored, selected, and/or disseminated for future use with machine learning systems and/or subsurface data.

Methods according to other embodiments may continue, e.g., as is the case for method <NUM> illustrated in <FIG>. It should also be noted that methods according to yet further embodiments may omit creation of training datasets <NUM> (e.g., where such datasets are already available). As illustrated in <FIG>, however, method <NUM> continues at block <NUM> where a machine leaming network (e.g., a convolutional neural network, or more in particular a DNN, or other suitable machine learning network) is trained with a training dataset (e.g., the created training dataset of block <NUM>) to predict rock type probabilities. For example, the machine learning network may predict models, such as <NUM>-D trace data, <NUM>-D inline or crossline data, <NUM>-D data cubes, and/or any petrophysical parameter models useful for building geologic priors. In some embodiments, training of the machine learning network may be determined by a large number of weights. Unless otherwise specified, as used herein, "weights" generally refer both to multiplicative variables (commonly known as weights) and/or to additive variables (commonly known as biases). The machine learning network may leam a preferred and/or improved setting for the large number of weights through training.

Methods according to some embodiments may complete after the machine learning network is trained at block <NUM>. For example, one or more machine learning networks may be trained, cataloged, stored, selected, and/or disseminated for future use with machine learning systems and/or subsurface data.

As illustrated in <FIG>, however, the method <NUM> continues at block <NUM> where an initial petrophysical parameter estimate is obtained (note that, methods according to yet further embodiments may begin at block <NUM>, e.g., where a trained machine learning network is already available for use). Obtaining an initial petrophysical parameter estimate (<NUM>) may include, for example, generating the initial petrophysical parameter estimate as a model of porosity and/or Vclay. Also or instead, the initial petrophysical parameter estimate may be built from a prior seismic interpretation or inversion or modeled on one or more horizons. In some embodiments, the initial petrophysical parameter estimate may be as simple as a half space model with a fixed porosity and a fixed Vclay for all parameters. In some embodiments, the initial petrophysical parameter estimate may be obtained from a pre-existing library of models.

Method <NUM> continues at block <NUM> where the trained machine learning network (e.g., from block <NUM>) is used with the initial petrophysical parameter estimate (from block <NUM>) to predict a geologic prior model. In some embodiments, the trained machine learning network may predict and/or classify rock type probabilities in conjunction with predicting geologic priors at block <NUM>.

Method <NUM> continues with obtaining input information for the inversion. For example, at block <NUM>, geophysical data (e.g., seismic data) is obtained. The geophysical data may include data representative of a subsurface volume (e.g., images) and corresponding identifications of geologic features for the subsurface volume (e.g., labels). As another example, at block <NUM>, geophysical parameters (e.g., elastic parameters) are obtained. In some embodiments, elastic parameters (e.g., velocity model, resistivity model, etc.) may be derived from tomography or Full Wavefield Inversion (FWI) or other imaging/processing methods of seismic data. Suitable systems and methods for estimating geophysical parameters are further described in co-pending <CIT>, entitled "Method for Estimating Petrophysical Properties for Single or Multiple Scenarios from Several Spectrally Variable Seismic and Full Wavefield Inversion Products," filed <NUM>/<NUM>/<NUM>. The actions of blocks <NUM> and <NUM> may occur in parallel, sequentially, and/or in any order. More generally, methods according to some embodiments may include obtaining geophysical data (represented by block <NUM>) and/or data derived therefrom (wherein the geophysical parameters represented in block <NUM> are an example of such data derived therefrom).

In some embodiments, a seismic survey may be conducted to acquire the input information for the inversion (noting that these and other embodiments may also or instead include obtaining other geophysical data in addition to or, or instead of, seismic data-such as obtaining, electromagnetic, electrical resistivity, gravity measurements). In these and other embodiments, simulation models may be utilized to generate synthetic input information for the inversion (e.g., computer simulation). In some embodiments, the input information for the inversion may be obtained from a library of data from previous seismic surveys or previous computer simulations. In some embodiments, obtaining input information for the inversion includes processing acquired data and/or simulated data (e.g., generating images, identifying and/or labeling features, manually and/or automatically annotating data elements). In some embodiments, a combination of any two or more of these methods may be utilized to generate the input information for the inversion.

Method <NUM> continues at block <NUM> where a petrophysical inversion is performed to generate a rock type probability model and an updated petrophysical parameter estimate. The petrophysical inversion may be based on the geologic prior model (e.g., the geologic prior model from block <NUM>, or the geologic prior model from block <NUM> as further discussed below), the geophysical data from block <NUM>, and the geophysical parameters from block <NUM>. In some embodiments, a decoder (i.e., the generative function) of a machine learning network (e.g., from block <NUM>) may be extracted and inserted into an objective function of the petrophysical inversion. In some embodiments, the updated petrophysical parameter estimate may be applicable to multiple scales, such as seismic scales, sub-seismic scales, and well log scales. For example, the updated petrophysical parameter estimate may include resampled data with deep learning to be applicable to multiple scales. Suitable data resampling systems and methods are further described in the aforementioned co-pending <CIT>.

In some embodiments, the petrophysical inversion may seek a subsurface model which is consistent with one or more geophysical data types (e.g., seismic, electromagnetic, gravity, petrophysical well-log data, etc.). In some embodiments, the decoder may replace high-dimensional variables of an output space which describe the subsurface with lower-dimensional variables in a latent space. In some embodiments, the petrophysical inversion may minimize, or at least reduce, the objective function to find a preferred low-dimensional description of the subsurface. For example, during minimization and/or reduction of the objective function, a Jacobian of the decoder may be calculated with respect to the latent-space parameters, as means to determine a data-misfit-reducing search direction in latent space. As another example, products of that Jacobian with latent-space and output-space vectors may be used, circumventing storage of a Jacobian calculation in computer memory. In some embodiments, a preferred low-dimensional description of the subsurface may be converted into high-dimensions using the decoder. In some embodiments, uncertainty in the subsurface model is assessed by running multiple inversions with different decoders extracted from machine learning networks (from block <NUM>) trained with different training sets (from block <NUM>), thereby incorporating different geologic assumptions, processes, or environments. In some embodiments, uncertainty in the subsurface model is assessed by running multiple inversions with different objective functions which reduce or minimize data misfit as well as minimizing/maximizing the values of any of the low-dimensional parameters or combinations thereof.

Method <NUM> continues at block <NUM> where the result of the petrophysical inversion of block <NUM> is checked for convergence. As will be further discussed, in the absence of convergence (e.g., at least for a specified period or number of iterations), the method <NUM> continues to iteratively update petrophysical parameter estimates and geologic prior models in order to iteratively perform petrophysical inversions. Convergence may be identified when the results of successive petrophysical inversions are appreciably similar, and/or when the estimated error therein is below a specified threshold. Once convergence has been identified, method <NUM> ends at block <NUM>. In some embodiments, method <NUM> may also end at block <NUM> once a specified number of iterations have occurred, and/or once an error state has been on the geologic prior model of the final iteration. In some embodiments, the final rock type probabilities model may be used for geologic model building, geologic interpretation, seismic imaging, reservoir identification, operational planning, and/or other hydrocarbon management activities.

In the absence of convergence at block <NUM>, method <NUM> iteratively continues at block <NUM> where the trained machine learning network (e.g., from block <NUM>) is used with the updated petrophysical parameter estimate (from block <NUM> of the prior iteration) to predict an updated rock type probabilities model and/or a geologic prior model (noting that, as illustrated in <FIG>, the method <NUM> includes predicting both the updated rock type probabilities model and the geologic prior model). The iteration continues anew at block <NUM> where another petrophysical inversion is performed. The petrophysical inversion may be based on the geologic prior model from block <NUM>, the geophysical data from block <NUM>, and the geophysical parameters from block <NUM>.

<FIG> illustrates an exemplary schematic <NUM> of petrophysical inversion with machine learning-based geologic priors. As illustrated, a machine learning network has been trained to predict rock type probabilities according to blocks <NUM> and <NUM> of <FIG>. The schematic <NUM> illustrates using an initial petrophysical parameter estimate <NUM> (as from block <NUM> of <FIG>) to predict a geologic prior model <NUM> according to block <NUM> of <FIG>. The schematic <NUM> illustrates geophysical data <NUM> (as from block <NUM> of <FIG>) being used together with the initial petrophysical parameter estimate <NUM> and geologic prior model <NUM> to perform a petrophysical inversion (e.g., an optimization) to generate an updated parameter estimate <NUM>, according to block <NUM> of <FIG>. Schematic <NUM> also illustrates use of the trained machine learning network to infer rock type probabilities <NUM> based on the updated parameter estimate <NUM>, according to block <NUM> of <FIG>. Schematic <NUM> also illustrates use of the trained machine learning network to update the rock type probabilities <NUM> from the inversion to generate updated rock type probabilities <NUM>, according to block <NUM> of <FIG>. Lastly, schematic <NUM> illustrates iteration <NUM> of method <NUM> of <FIG>. Note that iteration <NUM> is illustrated in <FIG> following the learning and preceding the optimization. A variation of schematic <NUM> could equally represent iteration <NUM> following the optimization and preceding the learning.

In some embodiments, the machine learning network (of block <NUM>) may include a DNN. The DNN may in certain embodiments be, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), and/or a generative adversarial network (GAN).

<FIG> illustrates an exemplary CNN <NUM> that would be suitable as the machine learning network of block <NUM>. As illustrated, CNN <NUM> is generally an encoder-decoder machine learning construct with an hour-glass shape. CNN <NUM> may be used to characterize a low-dimensional form of patterns found in a library of geologic examples. For example, input space <NUM> may contain a library of geologic examples. The input space <NUM> may generally contain the training set for the CNN <NUM>. During training, the encoder network <NUM> may characterize input space <NUM> in terms of a low-dimensional encoded space <NUM>. Moreover, during training, a decoder network <NUM> may be found to characterize encoded space <NUM> in terms of an output space <NUM>. Decoder network <NUM> may convert low-dimensional encoded space <NUM> into a full-scale (high-dimensional) model in output space <NUM>. As such, output space <NUM> may conform to the geologic behavior exhibited in the training set contained in input space <NUM>. Models in output space <NUM>, generated by decoder network <NUM> of CNN <NUM>, may be used as input to a deterministic inversion to find a geologically reasonable model, or collection of models, which are each consistent with the geophysical, petrophysical, and other observed data represented in the training set.

In some embodiments, by transforming low-dimensional parameters to high-dimensional parameters, the model-generative ability of the decoder network <NUM> may be utilized with an optimization (e.g., petrophysical inversion). With the benefit of the trained decoder network <NUM>, the optimization may be able to search a low-dimensional, geology-conforming space for models which are consistent with quantifiable data (e.g., geophysical, seismic, electromagnetic, gravimetric, well-logs, core samples, etc.).

In some embodiments, the optimization may be a joint inversion. For example, a training set for joint inversion may include models which are described by multiple voxelized rock parameters: resistivity, density, compressional- or shear-wave velocities, porosity, permeability, lithology type, etc. Covariance and/or interactions between these different categories of rock description may be ingrained in the training set examples by nature of the simulations or real-world observations which created these examples. Then the decoder may capture information about the different parameter interactions and distill the interactions into a simpler "latent space" description (e.g., encoded space <NUM>). As the joint inversion proceeds, the expected rock parameter covariance (e.g., between resistivity and velocity) may be reproduced by the decoder. Consequently, the inversion models may conform to realistic rock-parameter covariance while simultaneously fitting the various observed data (e.g., electromagnetic and seismic records).

In some embodiments, CNN <NUM> may extract spatial patterns common among training models. In some embodiments, the CNN <NUM> may approximate the common spatial patterns, for example with a non-linear function. In some embodiments, the encoder network <NUM> may utilize such approximations to develop the latent parameters of encoded space <NUM>. In some embodiments, the latent parameters may be much fewer in number than the parameters of input space <NUM>. For example, the original training models may be represented as a large number of voxelized physical properties. In some embodiments, during training, the CNN <NUM> produces a decoder network <NUM>. In some embodiments, the decoder network <NUM> may be a non-linear function, which maps the latent parameters back to the full-dimensionality of the original training models.

<FIG> illustrates an exemplary RNN <NUM> that would be suitable as the machine learning network of block <NUM>. As illustrated, the RNN is bi-directional and includes long/short-term memory (LSTM) units. RNN <NUM> may advantageously provide flexibility of incorporating training data sequences of variable lengths. The LSTM units may be developed to deal with exploding and vanishing gradient problems that can be encountered when training traditional RNNs. The LSTM units may also be designed to leam long-term dependencies, which may be useful for labelling rock types. For example, the LSTM units may allow the RNN to focus on more than just local features to classify the rock type. In some embodiments, the RNN may be directional in nature, only utilizing information from the past. The illustrated embodiment utilizes complete logs, having information from both future and past. Therefore, the illustrated RNN is a bi-directional LSTM network.

In some embodiments, the machine learning network may have a modified cost function. For example, a class imbalance problem may result when the training dataset includes many examples for some rock types, but far fewer examples of other rock types. To address such a class imbalance problem, the cost function of the machine learning network may be modified to highly penalize the machine learning network for making incorrect predictions on the rock types with fewer examples.

In some embodiments, different performance measures may be utilized to track the accuracy of the machine learning network (e.g., as part of the training of the machine learning network at block <NUM> of <FIG>). For example, performance measures may include such measures as confusion matrices, Precision, Recall, F1-score etc. <FIG> illustrate an exemplary set of confusion matrices comparing the prediction accuracy of a machine learning network trained on four rock types and <NUM> data, and making predictions on test data containing six different frequencies (e.g., simulated frequency utilized in creating synthetic seismic data during an inversion step). Each of the matrices of <FIG> is labeled with its corresponding "test data" frequency. As illustrated, the true rock type is classified on the vertical axes, while the predicted rock type is illustrated on the horizontal axes. It should be appreciated that perfect predictions would result in scores of "<NUM>" in each of the diagonal cells, and scores of "<NUM>" in each of the off-diagonal cells (where the diagonal tracks the cells in which the "true label" value matches the "predicted label" value; as illustrated in <FIG>, from top left to bottom right). Consequently, a scalar measurement of accuracy may be based on a net variance from such perfect prediction. As illustrated, the prediction accuracy of the machine learning network improves as the frequency of the test data used for training the network increases.

However, per some embodiments, the frequency of training data may also be matched, as closely as feasible, to the expected frequency of input data to which the machine learning network will be applied. For example, while a machine learning network trained on <NUM> frequency test data may give good accuracy, that same network applied to <NUM> input data (e.g., the output of an inversion, which is at <NUM> resolution) may not work well. Moreover, in some embodiments, the expected rock type(s) for the subsurface region of interest may influence the performance measures. For example, the performance metrics may be weighted to emphasize one or more particular frequency bands and/or one or more particular rock types based on the expected rock type(s) for the subsurface region of interest. Further, in these and other embodiments, it may be beneficial to train a more robust machine learning network (e.g., one capable of handling a variety of frequencies for its inputs), comprising training the machine learning network using training data with a plurality of different frequencies (with one example being the training using seismic scale and sub-seismic scale frequencies, discussed in more detail below).

In some embodiments, the machine learning network of block <NUM> may be selected from several different machine learning networks, including CNNs, RNNs, and GANs. In some embodiments, the machine learning networks may be selected based on prediction performance (e.g., Precision, Recall, F1-score, etc.) on a validation dataset and/or on a test dataset. In some embodiments, the machine learning networks may be automatically selected (e.g., based on meeting pre-set or otherwise predetermined performance prediction indicators).

A trained machine learning system infers rock properties from petrophysical parameters. The machine learning system is trained with supervised learning. As illustrated in <FIG>, predicting rock properties (e.g., rock type) from petrophysical parameters (e.g., porosity, Vclay) may be formulated as a supervised-leaming problem with input/output pairs. The input may include petrophysical parameters at both high frequency (e.g., sub-seismic scale) and low frequency (e.g., seismic scale). The machine learning system is trained to infer rock type at sub-seismic scales even for seismic-scale input. Similar to a super-resolution problem in imaging, the machine learning system may be trained with supervised learning and a low-pass filter to infer sub-seismic scale rock properties from seismic-scale petrophysical parameters. In these and similar embodiments, desired high-frequency scales (sub-seismic resolution scales) relate to geologic features of interest and are typically finer scales not resolved by conventional seismic data (hence they are called "sub-seismic scales"). Scales resolved or not resolved by seismic data vary depending on a host of factors, such as acquisition method, depth, etc. As an example per some embodiments, one could have frequencies of <NUM> in seismic data (seismic scale), and desired scales with relevant geologic features are at <NUM> (sub-seismic scale). In such a case, one would train a machine learning network with training data for <NUM> input (low resolution, seismic scales) and <NUM> output (high resolution, subseismic scales). In other embodiments, one could have frequencies of greater than <NUM> in seismic data, such as <NUM> or less; <NUM> or less; <NUM> or less; <NUM> or less; <NUM> or less; <NUM> or less; <NUM> or less; <NUM> or less; or even <NUM> or less - again, depending on factors such as those noted above, as the ordinarily skilled artisan would recognize. The desired scales with relevant geologic features (sub-seismic scale) may in such cases still be even higher - such as greater than <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or even <NUM>. In sum for such embodiments, then, the desired sub-seismic scales have frequency greater than the seismic scales of input data.

In some embodiments, a training set may include only elements that are each geologically plausible. The training set may include only a subspace of all possible models.

For example, the geologically plausible elements generally follow the same rules (e.g., patterns of layering: sequence, continuity, faulting; and ductile buoyancy flow: salt bodies) as seen in the training set. Rather than representing the followed-rules (which may be quite numerous) as individual constraints, the training set may be a spanning representation of how plausible geology works and/or how rocks are actually arranged. In some embodiments, the training set may be specific to a certain region of the earth. In other embodiments, the training set may generally include plausible geology for any region of the full earth. The training set may exemplify in at least one example each of the pertinent geologic rules. Thus, plausibility may be defined by the statistics of the training set.

In some embodiments, training set elements may be created from synthetic geologic models. In some embodiments, a computer simulation may be run to create some or all of the elements in the training set. For example, the training set examples may be generated with process stratigraphy (PS). Generally, PS is a method for simulating geologic patterns. PS may include a numerical simulation of the physics governing how grains of rock are transported, eroded, and/or deposited in a fluid (e.g., a simulation of sediment-laden water flowing from the outlet of a river, into an ocean, and out to the down-dip extent of a delta lobe). In some embodiments, a synthetic earth generator (e.g., a PS simulator) may produce a library of training models. Additional examples of computer simulations of geologic patterns may include salt body plastic flow simulations, geomechanical simulations, and/or basin and petroleum system simulations. Each training model may thus represent an instance of plausible geologic behavior in the subsurface region of interest.

In some embodiments, training set elements may be created from heuristic methods for producing geologic models (e.g., earth modeling with functional forms, interpreted seismic sections, and/or digitized observations of rock outcrops).

The training set elements may represent geologic parameters (e.g., three-dimensional stacking patterns of rock layers) on a scale similar to that of the desired geologic model. For example, rock layers within these models may be described by such parameters as facies type (e.g., sand, shale, or salt) and/or grain-size distributions. By merit of the rules and input parameters governing the chosen earth-model generator, the rock layers of the training set elements may adhere to depositional, erosional, tectonic physics, and/or the constraints of a specific basin (e.g., observed base morphology and historical sediment flux).

In some embodiments, a training set may be selected to include only elements that are each geologically plausible. CNN <NUM> may be trained with such a training set. The encoder network <NUM> may take any model in input space <NUM> and convert this model to a latent encoded space <NUM>. For example, geologic plausibility may be measured in latent encoded space <NUM> by some metric (e.g., by distance from some paragon or mean latent-space model, Z). The decoder network <NUM> may take any geologically plausible description in latent encoded space <NUM> and convert this description to an output space <NUM>, which conforms to a description usable by a physics simulator (e.g., voxelized parameters). After training, latent encoded space <NUM>, output space <NUM>, and decoder network <NUM> may then be utilized with a deterministic inversion method. The inversion may perform a parameter search in latent space. The inversion may use the decoder network (and its functional derivatives) to convert proposed models to the output space. The physical consistency of the converted proposed models may be measured with observed and/or synthetic data. For example, the synthetic data may be created by physics simulation using the output space. The inversion may produce models which reproduce physical responses that lie within acceptable proximity to those observed (e.g., subspace <NUM>). Since the training set included only subspace <NUM>, the inversion may thus produce geologically-plausible models within subspace <NUM> which are consistent with the observed data (e.g., subspace <NUM>). In other words, such inversion may produce those models of subspace <NUM>.

In practical applications, the present technological advancement must be used in conjunction with a geophysical data analysis system (e.g., a high-speed computer) programmed in accordance with the disclosures herein. For example, any of the petrophysical or other inversion techniques will in various of these embodiments be carried out using such a system. Likewise, generating the various models (e.g., geologic prior models, rock type probability models) and/or generating petrophysical or other parameter estimates will be carried out using such a system. Similarly, training and applying a machine learning network will be carried out using such a system. Such a geophysical data analysis system may be referred to in generic shorthand simply as a "computer. " The same or a different computer (and/or geophysical data analysis system) may be used to carry out different inversions, and/or different steps of generating models, and/or different generation, training, or application of machine learning networks. Thus, referring to any of these steps as carried out "using a computer" will be understood to mean that the same or different computers may be used for such steps, unless context clearly dictates otherwise.

Preferably, a geophysical data analysis system employed for any of the aforementioned processes is a high performance computer (HPC), as known to those skilled in the art. Such high performance computers typically involve clusters of nodes, each node having multiple CPUs and/or graphics processing unit (GPU) clusters, and computer memory, with configuration that allows parallel (and particularly massively parallel) computation. The various models may be visualized and edited using any interactive visualization programs and associated hardware, such as monitors and projectors. The architecture of the system may vary and may be composed of any number of suitable hardware structures capable of executing logical operations and displaying the output according to the present technological advancement. Those of ordinary skill in the art are aware of suitable supercomputers available from Cray or IBM, as well as other architectures such as HPCs with multiple GPU clusters.

<FIG> illustrates a block diagram of a geophysical data analysis system <NUM>. A central processing unit (CPU) <NUM> is coupled to system bus <NUM>. The CPU <NUM> may be any general-purpose CPU, although other types of architectures of CPU <NUM> (or other components of exemplary system <NUM>) may be used as long as CPU <NUM> (and other components of system <NUM>) supports the operations as described herein. Those of ordinary skill in the art will appreciate that, while only a single CPU <NUM> is shown in <FIG>, additional CPUs may be present. Moreover, the system <NUM> may comprise a networked, multi-processor computer system that may include a hybrid parallel CPU/GPU system. The CPU <NUM> may execute the various logical instructions according to various teachings disclosed herein. For example, the CPU <NUM> may execute machine-level instructions for performing processing according to the operational flow described.

The geophysical data analysis system <NUM> may also include computer components such as non-transitory, computer-readable media. Examples of computer-readable media include a random access memory (RAM) <NUM>, which may be SRAM, DRAM, SDRAM, or the like. The system <NUM> may also include additional non-transitory, computer-readable media such as a read-only memory (ROM) <NUM>, which may be PROM, EPROM, EEPROM, or the like. RAM <NUM> and ROM <NUM> hold user and system data and programs, as is known in the art. The system <NUM> may also include an input/output (I/O) adapter <NUM>, a communications adapter <NUM>, a user interface adapter <NUM>, and a display adapter <NUM>; the system <NUM> may potentially also include one or more graphics processor units (GPUs) <NUM>, and one or more display driver(s) <NUM>.

The I/O adapter <NUM> may connect additional non-transitory, computer-readable media such as a storage device(s) <NUM>, including, for example, a hard drive, a compact disc (CD) drive, a floppy disk drive, a tape drive, and the like to geophysical data analysis system <NUM>. The storage device(s) may be used when RAM <NUM> is insufficient for the memory requirements associated with storing data for operations of the present techniques. The data storage of the system <NUM> may be used for storing information and/or other data used or generated as disclosed herein. For example, storage device(s) <NUM> may be used to store configuration information or additional plug-ins in accordance with the present techniques. Further, user interface adapter <NUM> couples user input devices, such as a keyboard <NUM>, a pointing device <NUM> and/or output devices to the system <NUM>. The display adapter <NUM> is driven by the CPU <NUM> to control the display on a display device <NUM> to, for example, present information to the user. For instance, the display device may be configured to display visual or graphical representations of any or all of the models discussed herein, and/or to display visual or graphical representations of a subsurface region (e.g., based at least in part upon any one or more of the models or parameters described and/or generated herein).

The architecture of geophysical data analysis system <NUM> may be varied as desired. For example, any suitable processor-based device may be used, including without limitation personal computers, laptop computers, computer workstations, and multi-processor servers. Moreover, the present technological advancement may be implemented on application specific integrated circuits (ASICs) or very large scale integrated (VLSI) circuits. In fact, persons of ordinary skill in the art may use any number of suitable hardware structures capable of executing logical operations according to the present technological advancement. The term "processing circuit" encompasses a hardware processor (such as those found in the hardware devices noted above), ASICs, and VLSI circuits. Input data to the system <NUM> may include various plug-ins and library files. Input data may additionally include configuration information.

Geophysical data analysis system <NUM> may include one or more machine learning architectures, such as autoencoders and convolutional neural networks (or any other suitable network such as those discussed and referenced herein). The machine learning architectures may be trained on various training datasets in accordance with the description herein. The machine learning architectures may be applied to analysis and/or problem solving related to various unanalyzed datasets. It should be appreciated that the machine learning architectures perform training and/or analysis that exceed human capabilities and mental processes. The machine learning architectures, in many instances, function outside of any preprogrammed routines (e.g., varying functioning dependent upon dynamic factors, such as data input time, data processing time, dataset input or processing order, and/or a random number seed). Thus, the training and/or analysis performed by machine learning architectures is not performed by predefined computer algorithms and extends well beyond mental processes and abstract ideas.

The above-described techniques, and/or systems implementing such techniques, can further include hydrocarbon management based at least in part upon the above techniques. For instance, methods according to various embodiments may include managing hydrocarbons based at least in part upon models of subsurface regions and/or uncertainty therein constructed according to the above-described methods. In particular, such methods may include drilling a well, and/or causing a well to be drilled, based at least in part upon the models of subsurface regions and/or uncertainty therein (e.g., such that the well is located based at least in part upon a location determined from the models of subsurface regions and/or uncertainty therein, which location may optionally be informed by other inputs, data, and/or analyses, as well) and further prospecting for and/or producing hydrocarbons using the well.

Claim 1:
A method for modeling a subsurface region, comprising:
obtaining a trained machine learning network, wherein obtaining the trained machine learning network comprises training the machine learning network (<NUM>) with a training dataset to predict rock type probabilities from petrophysical parameters;
obtaining an initial petrophysical parameter estimate (<NUM>);
applying the trained machine learning network to the initial petrophysical parameter estimate to predict a geologic prior model (<NUM>);
obtaining geophysical data for the subsurface region acquired during a geophysical survey of the subsurface region (<NUM>);
obtaining geophysical parameters for the subsurface region (<NUM>); 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 (<NUM>);
wherein each one of (i) applying the trained machine learning network and (ii) performing the petrophysical inversion is carried out using a geophysical data analysis system, and
wherein the machine learning network is trained to predict rock type probabilities at sub-seismic scales for inputs comprising petrophysical parameters obtained from seismic-scale data.