Patent Application
20240393489
The present application relates generally to the field of hydrocarbon exploration, development and production. Specifically, the disclosure relates to a methodology and framework for unsupervised machine-learning for detecting amplitude variations with offset (AVO) anomalies from seismic images by learning relationships across partially-stack or pre-stack images.
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 step of hydrocarbon prospecting is to accurately model subsurface geologic structures and detect fluid presence in those structures. For example, a seismic survey may be gathered and processed to create a mapping (e.g., subsurface images such as 2-D or 3-D partially-stacked migration images presented on a display) of the subsurface region. The processed data may then be examined (e.g., analysis of seismic images) with a goal of identifying subsurface structures that may contain hydrocarbons. Some of those geologic structures, particularly hydrocarbon bearing reservoirs, may be directly identified by comparing pre- or partially-stacked seismic images (e.g., near stack image, mid stack image and far stack image).
One quantitative way of comparing the stack images is based on analysis of amplitude changes with offset or angle (amplitude versus offset (AVO) or amplitude versus angle (AVA)). Examples of AVO and AVA are disclosed in US Patent Application Publication No. 2003/0046006 A1, US Patent Application Publication No. 2014/0278115 A1, US Patent Application Publication No. 2020A0132873 A1, and U.S. Pat. No. 8,706,420, each of which is incorporated by reference herein in their entirety.
Typically, the relationship among the pre- or partially-stacked images (e.g., transition from near-stack to far-stack images) are considered to be multimodal (e.g., exhibiting multiple maxima) due to the offset-dependent responses of the geological structures and fluids (e.g., amplitude-versus-offset responses of hydrocarbon bearing sand, water-bearing sand, shale facies or salt facies can be different). It may be easier to detect such AVO changes in clastic reservoirs than ones in carbonate reservoirs. At reflection regimes, the relations among the stack images (AVO) may be explained by the Zoeppritz equation that describes the partitioning of seismic wave energy at an interface, a boundary between two different rock layers. Typically, the Zoeppritz equation is simplified for the pre-critical narrow-angle seismic reflection regimes and range of subsurface rock properties (e.g., Shuey approximation), and may be reduced to:
where R is the reflectivity, θ is the incident angle, A is the reflectivity coefficient at zero incident angle (θ=0), and B is the AVO gradient. The stack images may be used to determine A and B coefficients. These coefficients may be estimated over each pixel over the seismic image, over a surface (boundaries along the formations) or over a geobody (e.g., performing mean and standard deviations of A and B values over a geobody region). AVO is not the only indicator of fluid presence and may not be the most reliable indicator because the fluid effects may be obscured due to the inaccuracies in seismic processing, the seismic resolution, and presence of noise or the seismic interference of thin beds. Other hydrocarbon indicators may be useful for derisking hydrocarbon presence include: amplitude terminations; anomaly consistency; lateral amplitude contrast; fit to structure; anomaly strength; and fluid contact reflection. Thus, distributions of A and B values may be interpreted to distinguish the AVO anomalies from the background as an AVO response of hydrocarbon presence is expected to be anomalous. Further, this AVO analysis may be combined with the other indicators to increase the confidence around fluid presence.
In one or some embodiments, a computer-implemented method for detecting anomalous features from seismic images is disclosed. The method includes: accessing input seismic stack images; performing unsupervised machine learning, using at least a part of the seismic stack images, to generate a model that is configured to reconstruct the seismic stack images; using the model in order to generate reconstructed seismic stack images; assessing reconstructive errors based on the reconstructed seismic stack images with the input seismic stack images; detecting the anomalous features based on the assessment of the reconstructive errors; and using the detected anomalous features for hydrocarbon management.
The present application is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of exemplary implementations, in which like reference numerals represent similar parts throughout the several views of the drawings. In this regard, the appended drawings illustrate only exemplary implementations and are therefore not to be considered limiting of scope, for the disclosure may admit to other equally effective embodiments and applications.
The methods, devices, systems, and other features discussed below may be embodied in a number of different forms. Not all of the depicted components may be required, however, and some implementations may include additional, different, or fewer components from those expressly described in this disclosure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth hemin. Further, variations in the processes described, including the addition, deletion, or rearranging and order of logical operations, may be made without departing from the spirit or scope of the claims as set forth hemin.
It is to be understood that the present disclosure is not limited to particular devices or methods, which may, of course, vary. It is also 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 hemin, 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 word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. The term “uniform” means substantially equal for each sub-element, within about ±10% variation.
As used herein, “hydrocarbon management” or “managing hydrocarbons” includes any one, any combination, or all 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, e.g., 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, such activities typically taking place with respect to a subsurface formation. 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.). Hydrocarbon management may include reservoir surveillance and/or geophysical optimization. For example, reservoir surveillance data may include, well production rates (how much water, oil, or gas is extracted over time), well injection rates (how much water or CO2 is injected over time), well pressure history, and time-lapse geophysical data. As another example, geophysical optimization may include a variety of methods geared to find an optimum model (and/or a series of models which orbit the optimum model) that is consistent with observed/measured geophysical data and geologic experience, process, and/or observation.
As used herein, “obtaining” data generally refers to any method or combination of methods of acquiring, collecting, or accessing data, 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, and retrieving data from one or more data libraries.
As used herein, terms such as “continual” and “continuous” generally refer to processes which occur repeatedly over time independent of an external trigger to instigate subsequent repetitions. In some instances, continual processes may repeat in real time, having minimal periods of inactivity between repetitions. In some instances, periods of inactivity may be inherent in the continual process.
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 incorporated herein by reference, the definitions that are consistent with this specification should be adopted for the purposes of understanding this disclosure.
As discussed in the background, the prior art attempts to detect AVO anomalies in order to identify hydrocarbon presence. However, there are several failings in the current methodologies to detect anomalies. First, the Zoeppritz equation is crude approximation to the relationships of the field pre- and partially stack images because of the complex interactions of seismic waves, noise and inaccuracies in processing and migration imaging. Such equations may be useful for reasoning about how seismic waves interact with the subsurface but may be insufficient to process the data. Second, flat reflections may be caused by a change in stratigraphy and may be misinterpreted as a fluid contact. Third, rocks with low impedance could be mistaken for hydrocarbons, such as coal beds, low density shale, ash, mud volcano, etc. Fourth, polarity of the images could be incorrect, causing a bright amplitude in a high impedance zone. Fifth, AVO responses may be obscured by superposition of seismic reflections and tuning effects. Sixth, the signal may be contaminated with systematic or acquisition noise.
Various workflows to identify anomalies are contemplated. One example workflow may heavily depend on engineered image attributes to identify anomalies, which may typically lead to an unreliable and biased pick of the anomalous features. Other example workflows may rely on supervised machine learning, which do not rely on the feature engineering but do require an abundant amount of labelled examples to train the network. The requirement of a large amount of labelled training data is a challenge for many important interpretation tasks, particularly for direct hydrocarbon indicators (DHIs), for a variety of reasons. One reason is that generating labelled data for fluid detection is a labor-intensive process. Another reason is that DHIs are often difficult to pick, particularly subtle DHIs. This may limit the total number of training data that may be generated, even with the unlimited resources.
Thus, in one or some embodiments, an unsupervised machine learning framework is used to generate a model, which in turn may be used to identify anomalies of interest (e.g., anomalous feature presence) in a subsurface, and in turn hydrocarbon presence. In one or some embodiments, the model may be trained to learn the relationships among sets of images (e.g., partially-stack images or among pre-stack images). In particular, the unsupervised learning methodology may learn the relationships between pairs of images for detecting anomalous features from seismic images over a geological background (e.g., by randomly sampling patches from the input seismic stack images to train the model, thereby using at least a part of the seismic stack images to generate the model). In turn, the trained model may be used to reconstruct images, with a comparison of the reconstructed images and the original images being used to identify anomalies in the subsurface. This is in contrast to current workflows for identifying anomalies.
Various anomalies may be present in the subsurface; however, not all anomalies in the subsurface may be of interest. As such, in one or some embodiments, only a subset of reconstruction errors (caused by the model inaccurately reconstructing the image) corresponding to anomalies indicative of hydrocarbon presence, are of interest. To focus on the anomalies of interest, the unsupervised training of the model is configured to train the model to reconstruct certain features competently (such as the background, discussed below) and not to reconstruct other features competently (such as the anomalies of interest).
Various methods are contemplated to tailor the training of the model to enable the model to reconstruct certain features while being unable to reconstruct others. As one example, the type of data may be selected to train the model so that the model may competently reconstruct a part of the subsurface (e.g., the background for a specific region, such as a specific zone). In particular, various data preparation techniques, such as selecting image patches from the specific zone of interest and/or masking sections of images outside of the specific zone of interest. In this way, the training may be tailored to a specific zone so that the background associated with the specific zone may be competently reconstructed, whereas other features, such as anomalies that may be present in the specific zone separate from the background and indicative of hydrocarbon presence, may not be competently reconstructed (e.g., the unsupervised machine learning is constrained to a geologic context where anomalous features are defined, such as any one, any combination or all of a geologic age, zone, environment of deposition (EoD), or facies). Thus, in one or some embodiments, the inputs to the model may include geophysical inversion results, depth, geologic zone, geologic age, environment of deposition, and the input seismic stack images. As another example, training may be tailored so that certain types of features are competently reconstructed whereas other types of features are not.
Further, various types of anomalies may be of interest. Merely by way of example, two types of anomalies comprise amplitude anomalies (e.g., anomalies regarding amplitude versus offset or angle effect associated with the reservoir relative to the background) and structural anomalies (e.g., a geometrical anomaly). In one embodiment, the anomalies of interest may comprise amplitude anomalies whereas structural anomalies are not of interest. As such, the model is trained using data that enables the trained model to reconstruct the structure competently and does not enable the trained model to reconstruct amplitude accurately or competently. For example, the methodology may augment the seismic data (e.g., generate additional training data by rotating the seismic data, such as rotating images based on ranges of dipping angles) in a way that the trained model will reconstruct images that are invariant to the structural changes (e.g., the trained model is structurally invariant). For example, one or more pre-stack input images may be used to construct other pre-stack images, with some or all of the pre-stack images being inputs to the model and some or all pre-stack images being outputs of the model. Thus, when the trained model reconstructs an image, amplitude anomalies may be present (and geometrical/structural components are competently recreated in the reconstructed image). Conversely, the anomalies of interest may comprise structural anomalies as opposed to amplitude anomalies. As such, the model is trained using data that enables the trained model to reconstruct the amplitude competently and does not enable the trained model to reconstruct structure competently. Still alternatively, the anomalies of interest may include both amplitude and structural anomalies.
After training the model, the model may be used in order to identify anomalous features. In one or some embodiments, the model may be used to reconstruct an image, with the reconstructed image being compared in one or more ways with the original image in order to identify the anomaly(ies). Various ways are contemplated to ways to identify anomalous features including (1) reconstruction loss at the pixel level (e.g., the model generates a reconstructed pixel image, which is compared with the original pixel image in order to identify anomalies based on differences in pixel values greater than a predetermined threshold; (2) reconstruction at the latent space; and (3) generative adversarial network (GAN) rating of the anomalous feature (e.g., with the GAN rating indicative of whether the patch has an anomalous feature or not). One, some, or each of the ways may generate a corresponding score or indication of anomaly. In one or some embodiments, the scores of only one of the ways may be analyzed. Alternatively, scores from more than one way (such as scores from each of the three ways listed above) may be analyzed (such as combined) to determine whether that patch includes an anomaly.
Separate from, or in addition to, identifying whether a part of the image (such as an image patch) includes an anomaly, the location of the anomaly may likewise be determined. For example, pixel level reconstruction may be used to identify an anomaly (such as by comparison with the original pixel image). In addition, the methodology may identify where within the original pixel image, the anomaly occurs. In this regard, the methodology may identify a location of the anomaly.
Further, in one or some embodiments, the methodology may be used alone to identify the anomalies of interest. Alternatively, the methodology, which includes unsupervised training, may be paired with another methodology, such as supervised training. For example, in one embodiment, two separate models, one generated via supervised training and another generated via unsupervised training, may be used to determine anomalous features (e.g., the anomalous features may be detected based on assessment of reconstruction errors from images generated from a first model (generated via unsupervised learning) and from a second model (generated via supervised learning)).
As discussed above, various types of seismic data including any one, any combination, or all of the following may be analyzed in order to determine anomalies: pre-stack images; partial-stack images; geophysical property maps (e.g., compressional wave speed, shear wave speed, anisotropy, attenuation quality factors, density, pore pressure, etc.); or depth and travel time images. In one particular embodiment, anomalous features may be identified from seismic images by analyzing the changes among pre- or partially-stack images. For the data, multiple stacks (e.g., near and far stacks) may be used. Further, the analysis may be performed in one or more ways including: (1) learning from near to far seismic stacks (reconstruction from near to far); or (2) near to far of different stacks.
Without loss of generality, one may assume that there are two seismic images (e.g., near-stack and far-stack images) A and B, and the changes between two images may depend on some aspect of the subsurface, such as on the fluid type (e.g., hydrocarbon or water). For example, AVO may measure the amplitude changes between near-offset and far-offset images. In this case, A may represent the distributed values of near-offset images and B may represent the distributed values of far-offset images. In another case, A and B may be derived from equation (1) R(θ)=A+B sin2(θ) for near and far-offset angles θNear (e.g. 5°) and θFar (e.g. 15°) and near- and far-offset image R(θNear) and R(θFar). In another case, A and B axes may represent first two principal axes derived from the PCA analysis of the distribution of near and far offset image values R(θNear) and R(θFar).
A and B may also be collections of seismic images or volumes, such as a set of datasets X1, . . . , XN (e.g., pre-stack images). For instance, pre-stack images may be split into two groups,
or a combination of these groupings.
For paired A and B images, modern machine learning models such as autoencoders (illustrated in
In particular, the methodology may include an unsupervised learning approach, as discussed above. Various methods of unsupervised learning are contemplated. Example unsupervised learning methods for extracting features from images may be based on clustering methods (k-means), generative-adversarial networks (GANs), transformer-based networks, normalizing-flow networks, transformer networks. Siamese network, recurrent networks, or autoencoders, such as illustrated in the block diagram 100 in
Training of an autoencoder may determine θ and μ by solving the following optimization problem:
After training, one may use the learned encoding function E(x) to map an image (or a portion of image such as a patch) to its latent space (or embedding space) representation in Z. This representation of the image may be used for the image analysis. An example of an autoencoder architecture, which may include encoder 210 and decoder 220, is shown in the block diagram 200 in
The latent space typically captures the high-level features in the image x and has dimension much smaller than that of x. It is often difficult to interpret the latent space because the mapping from x to z is nonlinear and no structure over this space is enforced during the training. One approach may be to compare the images in this space with reference images (or patches) using a distance function measuring similarity between the pair (e.g., |zx−zReference|). See A. Veillard, O. Morére, M. Grout and J. Gruffeille, “Fast 3D Seismic Interpretation with Unsupervised Deep Learning: Application to a Potash Network in the North Sea”, EAGE, 2018. There are two challenges for detecting DHIs with such an approach. First. DHIs are anomalous features in seismic images and autoencoders are designed to represent salient features, not the anomalous features. Anomalous features are typically treated as statistically not meaningful or significant for reconstructing images. Second, an autoencoder cannot guarantee to cluster image features in latent space and they may not be separable in the latent space.
The generative model may be based on a deep network, such as U-nd, as illustrated in the block diagram 300 of
An unsupervised learning method may be used in order to construct B from A or in order to learn to reconstruct A and B from a pair of A and B. Statistically, a machine learning model learns, when trained, to construct seismic features which are common to the data set. One may infer if a feature is anomalous from its reconstruction quality. In particular, anomalous features may be poorly constructed. As discussed above, there are various ways to quantify whether the feature is poorly or adequately reconstructed. In particular, the quality of reconstructions may be inferred in a pixel-level image space, a learned representation space (e.g., latent space), or in a discriminator space. In one embodiment, the methodology may use, one, some, or each of the quality measurements in reconstructions of images to recognize or identify anomalous features, such as illustrated at 400 in
If A and B images are not paired, then the model may be trained in a cyclic fashion (e.g., cycleGANs). In this case, the model may learn the mapping between A and B by mapping A and “B space” and back to A and B to “A space” and back to B. The mapped images in “A space” and “B space” need not be compared to their pairs because image patches in A and B are not paired. Instead, the quality of images constructed in “A space” and “B space” may be evaluated with a discriminator which is also trained along the process to learn the distributions of realistic A and B images relative to the constructed ones (e.g., cycleGANs as illustrated in the block diagram 500 in
Specifically,
During the encoding, the spatial information of the input may often be reduced while the number of features may be increased. During the decoding, the features and the spatial information may be combined to produce the output. Additional constraints may be applied on the latent space to regularize the distribution of the latent space (e.g., standard normal distribution for latent space). Variational autoencoders and normalizing flow networks may impose such distributions on their latent spaces either explicitly or with a divergence penalty (e.g., Kulback-Liebler divergence).
The parameters of the model (e.g., weights and biases) may be optimized to pass the input image (e.g., near stack image) into the latent space, which is very often a reduced dimensional space compared to the input image space and reconstruct its pair (e.g., far stack images) to the best of its ability. It is expected that the dimensionality reduction will lead to an output image that is close, but not a perfect, reconstruction of the input image. As discussed above, in one or some embodiments, the loss of information or constraints over the latent space (e.g., standard normal distribution on the latent space) is purposefully exploited to highlight anomalies. The anomalous regions in the image may be statistically difficult to learn since the training samples containing these anomalies may be unbalanced and may be of insufficient size to effectively be modeled. Therefore, the model may fail to learn how to map (e.g., encode and decode) those areas in the image. By comparing the input and output in a norm (e.g., mean absolute error), the methodology may identify the most difficult areas to reconstruct in pixel space, and in turn identify the most anomalous. In one or some embodiments, various types of postprocessing may be performed. As one example, thresholding and/or denoising methods may be applied, thereby filtering the anomalies based on the neural network's reconstructive performance.
The quality and resolution of seismic images may often be depth and overburden dependent. The seismic responses of the rocks may also be depth dependent due to compaction. In addition, there may be dependences on the environment of deposition (EoD). Due to these dependencies, anomaly detection may be tailored in one or more ways based on these dependencies. As one example, anomaly detection may be evaluated in a particular context, which may be dependent on one or more factors, such as dependent on the depth. For instance, the shallow depth seismic responses are typically richer in quality, resolution, and amplitude dynamic ranges than their deeper counterparts even in similar EoD systems. Anomalous regions in one seismic section may not be anomalous for a different geologic context (e.g., depth or EoD). The anomalous features in the deeper seismic sections may also be expected to be more subtle than the shallower sections. For this reason, indicators of anomalous features may be evaluated in its local context. The geologic context may be provided by a stratigraphic zone EoD (e.g., channel versus delta), depth and/or geologic time (e.g., Jurassic versus Cretaceous). The model learning the mapping between A and B may be trained within a context to find anomalous features within that region. For instance, a supervised machine learning model (e.g., U-net such as illustrated in
Because of the depth, EoD and overburden dependencies of seismic responses, additional information including geophysical models (e.g., P and S velocities and density models), depth information, EoD classes and stratigraphic zone classes may be provided to the training process as inputs (e.g., via input channels) to the model. In particular, various additional channels of information, such as geophysical models (e.g., P and S velocity models, density) may allow the model to condition in geophysically meaningful fashion the constructions of partial stacks with the information via the additional channels so to better determine anomalous features of interest. The model may then learn the mapping from (A, C) to B where C corresponds to the set of additional information. C may reside in the pixel space as similar to the seismic images. This additional information may inform the model about where the patch is coming from (e.g., any one, any combination, or all of depth, EoD, stratigraphic zone, geological age, geophysical properties or etc.), so that the model may construct B (e.g., far stack image) within a geophysical and geological expectations.
In some instances, some of the anomalous regions may be known and may purposefully be avoided in the training by minimizing samples from those regions containing anomalies. During training, patches may be extracted from seismic volume for the model to reconstruct as described above. By withholding example patches that have anomalous features of interests within them, the methodology purposefully inhibits the model's ability to learn the features necessary to reconstruct features (e.g., salient features) within the image. Thus, the model may be trained using data in order to reconstruct certain features that, while potentially being considered an anomaly, are not considered anomalies of interest. In this way, the model may reconstruct the image with these certain features accurately, so that these features are not later identified as anomalies. Conversely, data may be explicitly excluded (or minimized) that are directed to certain features of interest (e.g., pinch-out structures or bright spots). Thus, the model may fail to reconstruct these certain features of interest, and in turn be identified as anomalies. In particular, even when training samples still contain anomalies of interest, by providing an overwhelming large amount of non-anomalous (e.g., background) data supplied to the model during training biases the model to learn background more effectively and ignore the rare instances of anomalies (or foreground) within the training dataset.
At 630, anomalous features are detected. As discussed above, various ways are contemplated to detect anomalous features. As one example, the anomalous features are detected based on the reconstruction quality of A or B. Further, various anomalous features to detect am contemplated. For example, anomalous features may have forms of structural irregularities (e.g., channel systems) or amplitude irregularities (e.g., bright spots) or a combination of both within a seismic volume.
In one embodiment, data preparation comprises gathering partial stack image patches paired or unpaired and augmentation of patches. As discussed above, not all anomalous features may be of interest for subsurface exploration. In order not to detect the uninteresting anomalous features (see 630), those features may be sampled more often than other features of interest. Alternatively, or in addition, augmentation strategies may be used to enforce model invariances on those uninteresting anomalous features. For instance, dipping structures may be detected as anomalous features if the common structural patterns in training patches are horizontal (with zero dipping angle). Horizontal features (e.g., layers) may be rotated over a range of dipping angles to enforce that machine learning model learns rotational invariance over the dip angles.
At 640, anomalous geobodies are created. Specifically, the detection of anomalous features from 630 may enable the delineation of geobodies using, for instance, a seed detection algorithm [Oz Yilmaz, Seismic Data Analysis, 2001]. At 650, the geobodies may be characterized. For example, these geobodies may later be classified with respect to their AVO types (e.g., I, II, Ip, III, IV) and characterized with additional seismic analysis methods such as petrophysical inversions to estimate porosity and volume of clay distributions within the geobodies. In this way, the detected anomalous features may be converted into geobody objects that are characterized by a geophysical inversion method such as AVO inversion.
At 660, user feedback may be obtained to label. For example, geobodies (e.g., subsurface objects with attributes) generated using anomalous features may be used to obtain the user feedback to discriminate whether they are the geobodies of interest for hydrocarbon exploration or not. Based on this feedback, at 670, a supervised or semi-supervised algorithm may train a model to learn segmenting those interesting geobodies.
The following is one example for illustration purposes only. For a given stratigraphic context, a set of near- and far-stack seismic images are gathered (see, e.g., the near and far-stack seismic images are obtained from New Zealand Petroleum and Minerals (NZPM) and they are released to public at http://data.nzpam.govt.nz/GOLD/system/mainframe.asp). To identify the anomalous features within this stratigraphic context, a model architected in
The seismic images are normalized between [−1, 1] and a hyperbolic-tangent (tanh) layer is used with the decoder to strictly enforce its outputs between [−1, 1]. The normalization eases the learning for the model since there original numerical range of the seismic is quite large.
Following to the training, the prediction is performed on the near-stack volume patch-by-patch to generate the far-stack volume. The true far-stack volume is normalized for the analysis to align with the distribution learned by the model. The generated far-stack is compared to the normalized true far-stack using an absolute distance norm |dGenerated−dTrue|. The regions that are most accurately predicted (e.g., the background) are diminished, and the regions which are less accurately predicted are brought to the foreground. Using thresholding, the background may be eliminated and the anomalous (e.g., foreground) features may be highlighted, such as shown in the illustration 700 in
In all practical applications, the present technological advancement must be used in conjunction with a computer, programmed in accordance with the disclosures herein. For example,
The computer system 1100 may also include computer components such as non-transitory, computer-readable media. Examples of computer-readable media include computer-readable non-transitory storage media, such as a random-access memory (RAM) 1106, which may be SRAM, DRAM, SDRAM, or the like. The computer system 1100 may also include additional non-transitory, computer-readable storage media such as a read-only memory (ROM) 1108, which may be PROM, EPROM, EEPROM, or the like. RAM 1106 and ROM 1108 hold user and system data and programs, as is known in the art. The computer system 1100 may also include an input/output (I/O) adapter 1110, a graphics processing unit (GPU) 1114, a communications adapter 1122, a user interface adapter 1124, a display driver 1116, and a display adapter 1118.
The I/O adapter 1110 may connect additional non-transitory, computer-readable media such as storage device(s) 1112, including, for example, a hard drive, a compact disc (CD) drive, a floppy disk drive, a tape drive, and the like to computer system 1100. The storage device(s) may be used when RAM 1106 is insufficient for the memory requirements associated with storing data for operations of the present techniques. The data storage of the computer system 1100 may be used for storing information and/or other data used or generated as disclosed herein. For example, storage device(s) 1112 may be used to store configuration information or additional plug-ins in accordance with the present techniques. Further, user interface adapter 1124 couples user input devices, such as a keyboard 1128, a pointing device 1126 and/or output devices to the computer system 1100. The display adapter 1118 is driven by the CPU 1102 to control the display on a display device 1120 to, for example, present information to the user such as subsurface images generated according to methods described herein.
The architecture of computer system 1100 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 computer system 1100 may include various plug-ins and library files. Input data may additionally include configuration information.
Preferably, the computer is a high-performance computer (HPC), known to those skilled in the art. Such high-performance computers typically involve clusters of nodes, each node having multiple CPU's and computer memory that allow parallel computation. The models may be visualized and edited using any interactive visualization programs and associated hardware, such as monitors and projectors. The architecture of 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 or other cloud computing based vendors such as Microsoft. Amazon.
The above-described techniques, and/or systems implementing such techniques, can further include hydrocarbon management based at least in part upon the above techniques, including using the AI model in one or more aspects of hydrocarbon management. For instance, methods according to various embodiments may include managing hydrocarbons based at least in part upon the one or more generated AI models and data representations constructed according to the above-described methods. In particular, such methods may include performing various welds in the context of drilling a well, and/or causing a well to be drilled, based at least in part upon the one or more generated geological models and data representations discussed herein (e.g., such that the well is located based at least in part upon a location determined from the models and/or data representations, 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.
It is intended that the foregoing detailed description be understood as an illustration of selected forms that the invention can take and not as a definition of the invention. It is only the following claims, including all equivalents which are intended to define the scope of the claimed invention. Further, it should be noted that any aspect of any of the preferred embodiments described herein may be used alone or in combination with one another. Finally, persons skilled in the art will readily recognize that in preferred implementation, some, or all of the steps in the disclosed method are performed using a computer so that the methodology is computer implemented. In such cases, the resulting physical properties model may be downloaded or saved to computer storage.
The following example embodiments of the invention are also disclosed:
Embodiment 1: A computer-implemented method for detecting anomalous features from seismic images comprising:
Embodiment 2: The method of embodiment 1: wherein the unsupervised machine learning is performed so that the model is trained not to reconstruct the anomalous features competently.
Embodiment 3: The method of embodiments 1 or 2:
Embodiment 4: The method of any of embodiments 1-3:
Embodiment 5: The method of any of embodiments 14:
Embodiment 6: The method of any of embodiments 1-5:
Embodiment 7: The method of any of embodiments 1-6:
Embodiment 8: The method of any of embodiments 1-7:
Embodiment 9: The method of any of embodiments 1-8:
Embodiment 10: The method of any of embodiments 1-9:
Embodiment 11: The method of any of embodiments 1-10:
Embodiment 12: The method of any of embodiments 1-11:
Embodiment 13: The method of any of embodiments 1-12:
Embodiment 14: The method of any of embodiments 1-13:
Embodiment 15: The method of any of embodiments 1-14:
Embodiment 16: The method of any of embodiments 1-15:
Embodiment 17: The method of any of embodiments 1-16:
Embodiment 18: The method of any of embodiments 1-17:
Embodiment 19: The method of any of embodiments 1-18:
Embodiment 20: The method of any of embodiments 1-19:
Embodiment 21: The method of any of embodiments 1-20:
Embodiment 22: A system comprising:
Embodiment 23: A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause a computing system to perform a method according to any of embodiments 1-21.
The following references are hereby incorporated by reference herein in their entirety, to the extent they are consistent with the disclosure of the present invention:
The present application claims priority to U.S. Provisional Application No. 63/261,792 filed on Sep. 29, 2021, the entirety of which is incorporated by reference herein.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US22/43781 | 9/16/2022 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63261792 | Sep 2021 | US |
