Graphical modeling of offshore cable routes by geological process models

Abstract

Certain aspects of the disclosure provide apparatuses and methods for graphical geological process models that use FM to generate maps for offshore cable routes. A method includes receiving, by a geological process model (GPM), a data input; selecting, an FM algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input; generating, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor; and generating, by the GPM, a graphical layer of an interactive graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

Description

Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WOGRAPHICAL MODELING OF OFFSHORE CABLE ROUTES BY GEOLOGICAL PROCESS MODELSBACKGROUNDField

[0001] Aspects of the present disclosure relate to determining optimal offshore cable routes.Description of Related Art

[0002] Wind energy is an alternative renewable energy source that harnesses kinetic energy from wind to generate electricity. Globally, energy production from wind energy generation sites (e.g., “wind farms”) is a growing contributor to overall power generation. Offshore wind energy generation sites need electrical connections between, for example, wind turbines, intermediate offshore substations, and mainland substations to convey generated power to end users, so undersea electrical cabling generally needs to be planned and installed as part of commissioning an offshore wind energy generation site. Ocean floor topographies and conditions vary, which generates technical challenges in routing cables in effective and durable ways. Accordingly, there is a need in the art for improved methods for undersea cable routing.SUMMARY

[0003] One aspect provides a method including receiving, by a geological process model (GPM), a data input; selecting, a forward modeling (FM) algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input; generating, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor; and generating, by the GPM, a graphical layer of a graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

[0004] Other aspects provide processing systems configured to perform the aforementioned method as well as those described herein; non-transitory, computer-readable media comprising instructions that, when executed by a processors of a processing system, cause the processing system to perform the aforementioned methods as well as those described herein; a computer program product embodied on a computer readable storage medium comprising code for performing the aforementioned methods as well as those further described herein; and a processingClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO system comprising means for performing the aforementioned methods as well as those further described herein.

[0005] The following description and the related drawings set forth in detail certain illustrative features of one or more aspects.DESCRIPTION OF THE DRAWINGS

[0006] The appended figures depict certain aspects and are therefore not to be considered limiting of the scope of this disclosure.

[0007] FIG. 1 depicts an example offshore wind energy generation site and its connection with onshore systems.

[0008] FIG. 2 depicts an example process of a geological process model (GPM) to predict sedimentation characteristics and generate a map for offshore wind energy generation site cable routes.

[0009] FIG. 3 depicts an example user interface (UI) of an interactive graphical model for selecting wind energy generation sites on a graphical model.

[0010] FIG. 4 depicts an example UI of an interactive graphical model displaying sedimentation characteristics results of a GPM for a bounded area designated for cable routes.

[0011] FIG. 5 depicts an example of a two dimensional graphical display of graphical layers of GPM results by an interactive graphical model.

[0012] FIG. 6 depicts an example three-dimensional graphical view of combined graphical layers of GPM results by an interactive graphical model.

[0013] FIG. 7 depicts an example method of generating maps for offshore cable routes.

[0014] FIG. 8 depicts an example processing system with which aspects of the present disclosure can be performed.

[0015] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the drawings. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WODETAILED DESCRIPTION

[0016] Aspects of the present disclosure provide apparatuses, methods, processing systems, and computer-readable mediums for implementing graphical geological process models (GPMs) that use forward modeling (FM) algorithms to generate maps for offshore cable routes.

[0017] Alternative energy sources, such as wind and solar, are growing forms of energy production used around the world. The transmission of electricity from wind turbines to electrical grids generally occurs through electrical cables. For offshore wind turbines, such electrical cables are often installed underwater, such as along an ocean floor. A technical problem with electricity bearing cables, especially those underwater, is energy loss due to thermal conductivity. That is, the loss of transmitted energy by conversion into heat that is dissipated into the surrounding environment. Because water is an excellent conductor of thermal energy, this dissipation is particularly problematic in undersea electrical cabling and reduces the efficiency of electrical power transmission.

[0018] Further, underwater electrical cables used to transfer energy from a wind energy generation site are exposed to various levels and types of sedimentation. Sedimentation, such as heavy silt and clay deposits, may increase around the cables on the ocean floor. Underwater electrical cables generate heat as they conduct electricity. In certain instances, this generated heat dissipates through the cable into the surrounding water. However, underwater electrical cables may be prone to sedimentation (sediment build up) over time. Both sediment type and sediment quantity affect soil thermal conductivity (thermal conductivity) of sediment surrounding the underwater electrical cables. Thermal conductivity describes the thermal properties of soil and its ability to conduct heat. As sediment builds up around the underwater electrical cables, thermal conductivity around the cable is reduced, and the thermal resistivity around the electricity cables starts to increase rapidly and exponentially. As thermal resistivity increases in the sediment surrounding the electrical cables, a stronger insulating barrier is created for heat expelled from the hot electrical wire to pass through. This reduces the ability of heat to escape the cables into the surrounding environment increasing heat in the cables. Increasing heat in the cables may lead to overheating of wires that reduces cable efficiency, further increasing cable heat generation and adding to energy loss through heat. In some instances, overheating may cause catastrophic cable and electrical transmission failures. Therefore sedimentation build up around the cables may beClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO removed manually on a periodic basis, for example, by underwater silt clearing equipment and remotely controlled robotic platforms. This manual sedimentation removal process is costly, unreliable, and time-consuming. The manual clean-up process is especially problematic if the cable route is particularly prone to sedimentation buildup, because the manual clean-up has to be repeated many times over the lifetime of a cable.

[0019] Aspects described herein generate and display color coded maps, via an interactive graphical model. The color coded maps are generated based on sedimentation characteristics around cable routes for offshore wind energy generation sites. Aspects may use a GPM to predict sedimentation build-up and thermal insulation of cables on various potential cable routes over a time period. Aspects described herein may display each area around potential cable routes in several interactive user interfaces of a graphical model.

[0020] Aspects described herein may include a graphical model displaying one or more potential underwater cable routes (e.g., on an ocean or lake floor) over a lifecycle of a wind energy generation site. For example, the graphical model may include a three-dimensional interactive geocellular model (e g., a computer-based representation of a geological volume) of potential areas for electrical cable routes. This three-dimensional geocellular model may be used by operators to plan sites and associated potential electrical cable routes for the lifetime duration of the wind energy generation site.

[0021] In some aspects, a GPM uses data associated with a proposed offshore wind energy generation site to predict one or more sedimentation factors and sedimentation characteristics in a proposed area of the wind energy generation site, to generate an interactive graphical model that displays potential sedimentation characteristics of cable routes around the site.

[0022] Because sedimentation patterns (e.g., of sedimentation factor(s)) across an ocean floor represent various interrelated variables subject to changes over time, a prediction of these sedimentation patterns may be generated from several complex data inter-relationships that may not be conducive for display without a specialized computer-generated graphical model. The display of prediction results of sedimentation characteristics for cable routes via an interactive and dynamic graphical model beneficially allows for graphical interactions with various layers of underlying data that make up the predicted cable routes, which would otherwise not be accessible.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0023] A technical benefit of a graphical model configured to display interactive predictions of potential electrical cable routes is the ability to display several layers of complex data to characterize a dynamic environment, such as an underwater environment over time. This graphical model provides an improvement to the display of cable routing predictions and underlying input data by allowing users to navigate input data and output results of complex algorithms and models via the graphical model displayed on a specific UI. This data would otherwise be inaccessible or difficult to navigate on a computing platform, Navigating and interfacing with complex layers of data using a specific graphical model displayed via a specific interface with a specific implementation of generating cable routes uses techniques unique to computers and provides an improvement to computing functionality.

[0024] Another technical benefit is the improvement of cable routes for offshore wind energy generation sites. Cable routes are improved by using a graphical model with GPM to generate and display accurate predictions of potential cable routes that minimize environmental effects on laid cables. The environmental effects minimized include thermal insulation effects on cable routes due to sedimentation around laid cables. The reduction of these environmental effects with improved cable routes improve the transmission efficiency of a laid cable along these routes.Example Offshore Wind Energy Generation Site

[0025] FIG. 1 depicts an example offshore wind energy generation site 101 and its relationship with onshore systems.

[0026] In some aspects, the example offshore wind energy generation site 101 may comprise offshore wind turbines 102 connected to an offshore substation 104 via “inter-array” cables 103. Inter-array cables 103 refer to subsea cables used in connecting offshore wind turbines 102 to each other or to the offshore substation 104. The offshore wind turbines 102 convert the kinetic energy of wind into electrical energy, which is then transferred to the offshore substation 104 via the interarray cables 103.

[0027] The offshore substation 104 converts the current to the right voltage and frequency to be fed into an electrical grid 109. In some aspects, offshore substation 104 may transfer the current via offshore cables 105 into land cables 106 that connect into an offshore grid substation 107. The offshore grid substation 107 acts as an intermediary and collects and delivers generated electricityClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO to other components in the offshore wind farm system. The offshore grid substation 107 is connected to an onshore substation 108, which is the onshore intermediary component of the off shore wind farm system that collects and delivers generated electricity to the electrical grid 109.

[0028] In some aspects, at least a portion of the offshore substation, the offshore cables 105, the land cables 106, and the offshore grid substation comprise an offshore grid 110. In some aspects, the onshore substation 108 and the electrical grid 109 comprise an onshore grid 111.Example Aspects of Sedimentation Modeling Using GPM

[0029] FIG. 2 depicts an example GPM process 200 to predict sedimentation characteristics and generate a cable route map for offshore wind energy generation sites.

[0030] In some aspects, the example GPM process 200 is undertaken by a GPM tool on a computing system. In some aspects, the example GPM process 200 includes data input(s) 201. The data input(s) 201 may include data for bathymetry, paleo topography, sediment information, physical processes, boundary conditions, time steps, and high-resolution forward model simulation data (high-res data). Bathymetry is the study of underwater depth of ocean floors, lake floors, or river floors. Bathymetry therefore includes contour data, e.g., elevations and depressions in bodies of water, such as ocean floor and offshore areas. The physical processes data relate to natural physical processes that affect deposition of sediment (e.g., wave movements, tremors, wind).

[0031] Paleo topography refers to the reconstruction of ancient land surface elevations. In some aspects, the bathymetry data may comprise data relating to ocean depth. In some aspects, the paleo topography data may relate to elevation of various topographical landmarks, e.g., areas on the ocean floor. In some aspects, the sediment information may include data on sediment type, quantity, and thermal-conductivity. In some aspects, the time step data may relate to a time duration, e.g., a time duration of a lifetime of a proposed offshore wind energy generation site. The time duration may include a prediction time frame, a maximum prediction time frame, or a minimum prediction time frame. In some aspects, boundary conditions may include information on maximums or minimums for any of the data input(s).

[0032] In some aspects, the input hi-res data comprises vertical resolution settings. The vertical resolution settings may be set by a user via a UI. These settings determine a number of sets of outputs to be generated for a vertical distance of a subsurface area, where each set of output dataClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO corresponds to a sedimentation layer in the offshore ocean floor. The sedimentation layer may be comprised of a specific vertical height on or below the ocean floor. For example, if the vertical resolution settings are set at fifty layers for a fifty meter vertical distance of the offshore ocean floor area. Then the GPM generates one set of data outputs (corresponding to one sedimentation layer) per meter. By contrast, a setting of a hundred layers for the same fifty meter vertical distance generates two sets of data outputs per meter, one set for each sedimentation layer per meter. Therefore the higher the number of layers, the higher the resolution of the outputs produced.

[0033] A sedimentation layer refers to a layer formed over a period of time due to accumulation of soil or sediment, e.g., on the ocean floor. Sediment can be the natural result of rocks and minerals, as well as the remains of plants and animals that are broken down by processes of weathering and erosion.

[0034] In some aspects, the example GPM process 200 may include providing the data input(s) 201 to one or more forward modeling (FM) algorithm(s) 202. FM refers to the use of a model to simulate an outcome. For example, it may refer to a technique that creates synthetic seismic models (e g., that simulate an outcome) from known geological information. The providing of the data input(s) 201 can include inputting the data input(s) 201 into the FM algorithm(s) 202.

[0035] In some aspects, the FM algorithms may include one or more of a diffusion algorithm, a steady flow algorithm, an unsteady flow algorithm, a wave dissipation / action algorithm, a carbonate growth or redistribution algorithm, and a sediment accumulation algorithm.

[0036] In some aspects, the diffusion algorithm predicts sediments from an area where sediment is eroded and is naturally carried downslope and deposited into a relevant offshore ocean floor area. In some aspects, the steady flow algorithm may mimic sediments carried by rivers, channels, or deltas. In some aspects, the unsteady flow algorithm may model sediments carried by gravitational disturbances or mass transport complexes. In some aspects, the wave dissipation algorithm models wave intensity, direction, and erosion effects. In some aspects, the carbonate growth and redistribution algorithm models the formation and movement of carbonate sediments in geological environments, including underwater environments. In some aspects, the sediment accumulation algorithm models sediment accumulation over time in geological environments, including underwater environments.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0037] In some aspects, the example GPM process 200 may include selecting the FM algorithm(s) 202. Selection of the FM algorithm(s) may be undertaken manually by a user, or in some aspects, autonomously by the GPM tool. In some aspects, selecting the FM algorithms may be based on the physical processes affecting the particular offshore setting. For example, if the impact on the offshore area is largely by wave movements, then the wave dissipation / action algorithm is selected. For example, if a location of a proposed offshore wind energy generation site is on the Gulf of Mexico, which is heavily impacted by unsteady currents, then the selecting may be of a combination of the steady flow algorithm and the unsteady flow algorithm. Any number of FM algorithm(s) 202 can be selected based on the physical environment of the proposed wind energy generation site.

[0038] In some aspects, the FM algorithm(s) 202 utilize a combination of the data input(s) 201 to produce output(s) 203 that include values (e.g., prediction result(s)) for sedimentation factor(s). The sedimentation factor(s) may include sediment size, sediment shape, sediment gradation, sediment cement, sediment porosity, sediment sorting, and organic content prediction. The prediction result(s) may be related to a time step or duration, e.g., a thirty year life duration of the offshore wind energy generation site, defined by the time step data of the data input(s) 201. For example, the output(s) 203 can be for sedimentation factors at different years of the thirty year life duration of the offshore wind energy generation site.

[0039] In some aspects, the smaller the sediment size and finer the shape the higher the insulation produced by the sediment. The organic content relates to the content of organic compounds in the sediment. Sediment sorting refers to the manner the sediment particles are arranged and the combinations of particle types at an individual particle level, with higher homogeneity positively correlated with higher insulation effects and thermal resistivity.

[0040] Gradation relates to the collective homogeneity of sediment particles. Sediment cement refers to minerals that bind soil particles together, e.g., quartz, silica, clay minerals, etc. These cement particles affect sedimentation bonding, e.g., clay minerals in cement cause higher insulation than quartz. Sediment porosity may refer to the gaps in the sediment with higher porosities correlating with reduced insulation effects.

[0041] In some aspects, the FM algorithm(s) 202 generate a set of output(s) 203 for each sedimentation layer. For example the FM algorithm(s) 202 generate a set of output(s) 203 at aClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO sedimentation layer by layer basis, by using FM algorithm(s) 202 on data input(s) 201 associated with each sedimentation layer as set by the high-res data vertical resolution settings. The data input(s) 201 corresponding to each layer may have one or more FM algorithms applied to it to generate the output(s) 203, wherein the output(s) 203 may comprise prediction result(s) of the sedimentation factor(s) associated with the sedimentation layer. Therefore if fifty layers are set by the vertical resolution settings of the high-res data of the data input(s) 201, fifty sets of output(s) 203 are generated, each set associated with a sedimentation layer, and each set of the output(s) 203 including prediction result(s) of the sedimentation factors associated with the layer.

[0042] In some aspects, the FM algorithm(s) 202 may use the output(s) 203 as inputs. For example, the output(s) 203 of a first sedimentation layer (e.g., prediction results of sedimentation factor(s) may be used as data input(s) 201 to derive the output(s) 203 for another sedimentation layer, e g., the closest layer under the previous layer. For example the output(s) 203 may also be used as data input(s) for the same layer, e g., to derive prediction results of other sedimentation factor(s). For example, the output(s) 203 may be used by different FM algorithm(s) to derive prediction results of other sedimentation factor(s). For example, the output(s) 203 of a diffusion FM algorithm 202 that predicts sedimentation factor(s) may be input into a sediment accumulation FM algorithm 202 to determine accumulation of sediment at a certain sedimentation layer based on the predicted sedimentation factors from the output(s) 203 that are used as inputs.

[0043] In some aspects, the prediction results of sedimentation factor(s) from the output(s) 203 may be combined to determine sediment characteristics at a specific sedimentation layer. For example, for each set of the output(s) 203 associated with a sedimentation layer, sediment characteristics of that sedimentation layer can be determined by a combination of the sedimentation factor prediction results(s) from that set of the output(s) 203.

[0044] A sedimentation characteristic may be a combination of prediction results of multiple sedimentation factor(s), where different combinations of different sedimentation factor(s) generate different sedimentation characteristics. Example sediment characteristics may include thermal conductivity, insulation, the type of sediment, the concentration of a sediment type in an area, and the like.

[0045] In some aspects, for each set of the output(s) 203 associated with one sedimentation layer, a graphical layer 204 is generated. For each graphical layer 204, one or more sedimentClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO characteristics, based on the prediction result(s) of sedimentation factor(s) of the output(s) 203, may be selected to be presented on the graphical layer 204. For example, the graphical layer may comprise an area, such as an area of a proposed wind energy generation site. The graphical layer may comprise colors or patterns, where each color or pattern corresponds to a sediment characteristic (or a sedimentation factor). Each portion or grid of the graphical layer may be occupied by a different color or pattern associated with a sediment characteristic or a sedimentation factor that is selected for the graphical layer 204. For example, a dark color may be associated with clay sediment, while a light blue color may be associated with silt.

[0046] The example GPM process 200 may combine various graphical layers 204 to generate a graphical model 205 comprised of various combined sedimentation layers (graphical model). For example, the interactive graphical model 205 can be a model of multiple sedimentation layers surrounding cables on the ocean floor, e.g., generated from a combination of the number of layers set by the vertical resolution settings. In some aspects, the various layers may be combined and displayed graphically as one unified layer in a two-dimensional or three-dimensional view.

[0047] The example GPM process 200 includes generating a graphical layer of a graphical model (e.g., an interactive graphical model), based on the sedimentation factor(s) of the output(s) 203. For example, the views 501-506 of FIG. 5 may each represent a graphical layer 204 of the graphical model.Example Graphical Model Utilizing GPM

[0048] FIG. 3 depicts an example UI of an interactive graphical model for selecting wind energy generation sites on a graphical model.

[0049] In some aspects, the interactive graphical model may correspond to the interactive graphical model 205 of FIG. 2. The UI 300 comprises a bounded area 301 that may represent an ocean floor area designated for wind energy generation site(s). A user may select or set the bounded area 301 as well as various different locations as proposed wind energy generation sites 302 to be displayed in the bounded area 301. Graphical layers may be generated for the bounded area 301. The graphical layers may correspond to the graphical layers 204 of FIG. 2 and may be generated according to the example GPM process 200 of FIG. 2.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0050] FIG. 4 depicts an example UI 400 of an interactive graphical model displaying sedimentation characteristics results of a GPM for a bounded area designated for potential cable routes. In some aspects, the interactive graphical model may correspond to the interactive graphical model 205 of FIG. 2.

[0051] In some aspects, the example UI 400 is generated subsequent to selections made on the UI 300 of FIG. 3. In some aspects, the example UI 400 is generated by the example GPM process 200 of FIG. 2. In some aspects, a bounded area 401 may represent an ocean floor area designated for wind energy generation site(s). The bounded area 401 may correspond with the bounded area 301 of FIG. 3. In some aspects, the example UI 400 comprises selected sites 402 for a potential wind energy generation site that correspond with the selected wind energy generation sites 302 of FIG. 3

[0052] In some aspects, the example UI 400 comprises a map 404, e.g., a color-coded grid map within the bounded area 401. The map 404 comprises a colored grid to display sedimentation characteristics of potential cable route(s) 403 for the selected sites 402. The example UI 400 may also include a key 405 that explains the different colors / patterns of the map 404 and what type of sediment or sediment characteristic they are associated with. For example the key 405 may represent silt clay with a first color, coarse sand with a second color, medium sand with a third color, and clay / shale with a fourth color on the map 404. In some aspects, the example UI 400 includes a map scale 406.

[0053] The example UI 400 may also include a key 407 that sets out the various colors or color coding used for each of the potential cable route(s) 403. For example the most optimum route may be a first color, e.g., green, a non-feasible route being a second color, e.g., red, and a feasible by risky route being a third color e.g., orange.

[0054] FIG. 5 depicts an example of a two dimensional graphical display of graphical layers 500 of GPM results by an interactive graphical model. In some aspects, each of the graphical layers 500 correspond to the graphical layer 204 that represent output(s) 203 resulting from the example GPM process 200 of FIG. 2.

[0055] In some aspects, each of the graphical layers 500 represent an ocean floor area designated for wind energy generation site(s). In some aspects, the ocean floor area mayClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO correspond with the bounded areas 301 or 401 of FIGs. 3 and 4. In some aspects, the GPM results displayed by the graphical layers 500 may be generated by the example GPM process 200 of FIG. 2

[0056] In some aspects, graphical layers 501-503 each represent a single graphical layer. Meanwhile the graphical layer 504 represents a combination of any of graphical layers 502-506, e.g., a combination of the graphical layers. Each of the graphical layers 502-506 represent one set of the output(s) 203 of FIG. 2 associated with a sedimentation layer. Therefore, each graphical layer 502-506 may display or represent prediction results of sedimentation factor(s) or sedimentation characteristics derived from the sedimentation factor(s).

[0057] The graphical layers 500 may comprise a graphic display associated with a prediction result from outputs of a GPM. For example a graphical layer of the graphical layers 500 may represent a prediction result(s) of sedimentation factor(s) of the output(s) at 203 of FIG. 2 or a sedimentation characteristic comprised of a combination of the prediction result(s) of sedimentation factor(s) of the output(s) at 203 of FIG. 2.

[0058] In some aspects, the graphical layers 500 may represent the predicted distribution of different types of sediment. For example, a graphical layer 501 presents the distribution of sand that is coarse, a graphical layer 502 presents the distribution of silt, while a graphical layer 503 presents the distribution of clay / shale. In some aspects, the predicted distribution of a sediment type may correspond to output(s) 203, of FIG. 2. In some aspects, a graphical layer can also represent a depth profile of various sections of the ocean floor.

[0059] The graphical layer 501 may be a combined view of any one or more of the graphical layers 501-503, where each graphical layer of the graphical layers 501-503 are combined to form the graphical layer 504 of the interactive graphical model. For example each layer may be imposed on the other where the colors of corresponding grids may be combined. In some aspects, the corresponding output(s) 203 from FIG. 2 from each graphical layer 501-503 are combined to generate the graphical layer 504. In some aspects, the graphical layers 500 may all be color coded according to a key, such as the key 405, and the key 411 of FIG. 4.

[0060] FIG. 6 depicts an example three-dimensional graphical view 600 of combined graphical layers of GPM results by an interactive graphical model. In some aspects, the three-Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO dimensional graphical view 600 comprises a bounded area 601 that may correspond with the graphical layer 504, of FIG. 5 or any of the bounded areas 301 or 401 of FIGs. 3 or 4.

[0061] In some aspects, the three-dimensional graphical view 600 may be comprised of several layers 602-606. For example each layer may represent a sedimentation layer. These various graphical layers may be combined into the one three-dimensional graphical view 600. Each layer 602-606 may correspond with one or more of the graphical layers 501-503 of FIG. 5. In some aspects, each of the graphical layers 602-606 or the combined three-dimensional graphical view 600 may be generated by the example GPM process 200 of FIG. 2.

[0062] In some aspects, the three-dimensional graphical view 600 may be manipulated via a UI, for example, that allows rotation, expansion, or selection of the three-dimensional graphical view 600 or the graphical layers 602-606. In some aspects, selection of a layer of the graphical layers 602-606 may reveal additional information, e.g., information regarding the layer and output(s) associated with the layer, e.g., textual information associated with the sedimentation factor(s) of the output(s) 203 of FIG. 2. For example, the selection of a layer 602-606 of the three- dimensional graphical view 600 may provide a user with information on the UI of the three- dimensional graphical view 600, for example about the composition of the sediment, its thermal conductivity as well as other information associated with the selection In some aspects, the UI may allow a user to go back and forth from the three-dimensional graphical view 600 to any one of the graphical layers 500 of FIG. 5 (two-dimensional).Example Operations

[0063] FIG. 7 shows a method 700 for GPMs using forward modeling to generate maps for offshore cable routes at an apparatus. In one aspect, method 700, or any aspect related to it, may be performed by an apparatus or a processing system, such as processing system 800 of FIG. 8, which includes various components operable, configured, or adapted to perform the method 700. Processing system 800 is described below in further detail.

[0064] Method 700 begins at block 702 with receiving, by a GPM, a data input.

[0065] Method 700 then proceeds to block 704 with selecting, by the GPM, an FM algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0066] Method 700 then proceeds to block 706 with generating, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor.

[0067] Method 700 then proceeds to block 708 with generating, by the GPM, a graphical layer of an interactive graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

[0068] In one aspect, method 700 further includes displaying the interactive graphical model via a display device.

[0069] In one aspect, method 700 further includes calibrating the data input according to a hard data input.

[0070] In one aspect, the hard data input comprises offshore mapping data.

[0071] In one aspect, the offshore mapping data comprises one or more of seismic data, well data, or analog wave data.

[0072] In one aspect, method 700 further includes generating a cable route in the graphical layer based on the prediction result and input data of a proposed structural location.

[0073] In one aspect, method 700 further includes combining the graphical layer with one or more other graphical layers, to generate the interactive graphical model, wherein each one of the graphical layer and the one or more other graphical layers corresponds to an ocean floor layer or a sedimentation layer.

[0074] In one aspect, the one or more other graphical layers are based on one or more other data outputs that are associated with one or more other data inputs, and the interactive graphical model is an interactive model.

[0075] In one aspect, method 700 further includes receiving a user selection input associated with the graphical layer, wherein the graphical layer corresponds to at least one of an ocean floor layer or a sedimentation layer.

[0076] In one aspect, method 700 further includes displaying the graphical layer in an alternative representation based on the user selection input.

[0077] In one aspect, the alternative representation is an expanded view of the graphical layer.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0078] In one aspect, method 700 further includes receiving at least one live data input of at least one live sedimentation factor.

[0079] In one aspect, method 700 further includes generating at least one live graphical layer based on the at least one live sedimentation factor.

[0080] In one aspect, method 700 further includes generating an updated interactive graphical model based at least on the at least one the at least one live graphical layer.

[0081] In one aspect, method 700 further includes receiving input information associated with a proposed offshore wind energy generation site structure.

[0082] In one aspect, method 700 further includes generating at least one color coded map for a cable route associated with the proposed offshore wind energy generation site structure.

[0083] In one aspect, method 700 further includes displaying the proposed offshore wind energy generation site structure on the at least one color coded map by the interactive graphical model.

[0084] In one aspect, the proposed offshore wind energy generation site structure is interactively movable on the graphical layer to a new position, and the new position generates at least one new cable route.

[0085] In one aspect, the interactive graphical model comprises a seismic model, a source migration charge model, a play chance mapping risk assessment model, a stratigraphic model, a reservoir model, a reservoir simulation model, or a volumetric model.

[0086] In one aspect, the GPM comprises at least one machine learning model.

[0087] In one aspect, the graphical layer corresponds to at least one of an ocean floor layer or a sedimentation layer.

[0088] In one aspect, the data input comprises sedimentation data.

[0089] In one aspect, the sedimentation data comprise bathymetry data.

[0090] In one aspect, the sedimentation data comprise paleo topography data.

[0091] In one aspect, the sedimentation data comprise sediment type data.

[0092] In one aspect, the sedimentation data comprise physical processes data.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0093] In one aspect, the physical processes data comprise one or more of sea level curve data or tectonic event data.

[0094] In one aspect, the sedimentation data comprises boundary conditions.

[0095] In one aspect, the sedimentation data comprises high resolution forward model simulation data.

[0096] In one aspect, the sedimentation data comprises time settings data.

[0097] In one aspect, the time settings data define at least one of a prediction time frame, a maximum prediction time frame, or a minimum prediction time frame.

[0098] In one aspect, the sedimentation factor comprises at least one of sediment size, sediment shape, sediment gradation, sediment cement, sediment porosity, sediment sorting, organic content, or sediment thermal conductivity.

[0099] In one aspect, the plurality of FM algorithms comprise a diffusion algorithm.

[0100] In one aspect, the plurality of FM algorithms comprise a steady flow algorithm.

[0101] In one aspect, the plurality of FM algorithms comprise a wave dissipation algorithm.

[0102] In one aspect, the plurality of FM algorithms comprise a wave action current algorithm.

[0103] In one aspect, the plurality of FM algorithms comprise a carbonate growth algorithm.

[0104] In one aspect, the plurality of FM algorithms comprise a carbonate redistribution algorithm.

[0105] In one aspect, the plurality of FM algorithms comprise a sediment accumulation algorithm.

[0106] Note that FIG. 7 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.Example Processing System for Graphical Models Using Forward Modeling to Generate Maps for Cable Routes

[0107] FIG. 8 depicts an example processing system 800 configured to perform various aspects described herein, including, for example, method 700 as described above with respect toClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WOFIG. 7.

[0108] The processing system 800 is generally be an example of an electronic device configured to execute computer-executable instructions, such as those derived from compiled computer code, including without limitation personal computers, tablet computers, servers, smart phones, smart devices, wearable devices, augmented and / or virtual reality devices, and others.

[0109] In the depicted example, the processing system 800 includes one or more processor(s) 802, one or more input / output device(s) 804, one or more display device(s) 806, one or more network interface(s) 808 through which the processing system 800 is connected to one or more networks (e.g., a local network, an intranet, the Internet, or any other group of processing systems communicatively connected to each other), and computer-readable medium 826. In the depicted example, the aforementioned components are coupled by a bus 810, which may generally be configured for data exchange amongst the components. Bus 810 may be representative of multiple buses, while only one is depicted for simplicity.

[0110] The processor(s) 802 are generally configured to retrieve and execute instructions stored in one or more memories, including local memories like the computer-readable medium 826, as well as remote memories and data stores. Similarly, the processor(s) 802 are configured to store application data residing in local memories like the computer-readable medium 826, as well as remote memories and data stores. More generally, the bus 810 is configured to transmit programming instructions and application data among the processor(s) 802, the display device(s) 806, the network interface(s) 808, and / or the computer-readable medium 826. In certain embodiments, the processor(s) 802 are representative of a one or more central processing units (CPUs), graphics processing unit (GPUs), tensor processing unit (TPUs), accelerators, and other processing devices.[0U1] The input / output device(s) 804 may include any device, mechanism, system, interactive display, and / or various other hardware and software components for communicating information between the processing system 800 and a user of the processing system 800. For example, the input / output device(s) 804 may include input hardware, such as a keyboard, touch screen, button, microphone, speaker, and / or other device for receiving inputs from the user and sending outputs to the user.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0112] The display device(s) 806 may generally include any sort of device configured to display data, information, graphics, user interface elements, and the like to a user. For example, the display device(s) 806 may include internal and external displays such as an internal display of a tablet computer or an external display for a server computer or a projector. The display device(s) 806 may further include displays for devices, such as augmented, virtual, and / or extended reality devices. In various embodiments, the display device(s) 806 may be configured to display a graphical user interface.

[0113] The network interface(s) 808 provide the processing system 800 with access to external networks and thereby to external processing systems. The network interface(s) 808 can generally be any hardware and / or software capable of transmitting and / or receiving data via a wired or wireless network connection. Accordingly, the network interface(s) 808 can include a communication transceiver for sending and / or receiving any wired and / or wireless communication.

[0114] The computer-readable medium 826 may be a volatile memory, such as a random access memory (RAM), or a nonvolatile memory, such as nonvolatile random access memory (NVRAM), or the like. In this example, the computer-readable medium 826 includes receiving component 812, selecting component 814, generating component 816, calibrating component 818, combining component 820, GPM 822, and FM algorithm 824.

[0115] In certain embodiments, the receiving component 812 is configured to receive, by a GPM 822, a data input.

[0116] In certain embodiments, the selecting component 814 is configured to select, by the GPM 822, a FM algorithm 824 of a plurality of FM algorithms, wherein the FM algorithm 824 is based on a type of the data input

[0117] In certain embodiments, the generating component 816 is configured to generate, a data output using the FM algorithm 824, wherein the data output comprises a prediction result of a sedimentation factor

[0118] In certain embodiments, the generating component 816 is configured to generate, by the GPM 822, a graphical layer of an interactive graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0119] Note that FIG. 8 is just one example of a processing system consistent with aspects described herein, and other processing systems having additional, alternative, or fewer components are possible consistent with this disclosure.Example Clauses

[0120] Implementation examples are described in the following numbered clauses:

[0121] Clause 1 : A method, comprising receiving, by a GPM, a data input; selecting an FM algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input; generating, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor; and generating, by the GPM, a graphical layer of a graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

[0122] Clause 2: The method of Clause 1, further comprising: displaying the graphical model via a display device.

[0123] Clause 3: The method of any one of Clauses 1-2, further comprising: calibrating the data input according to a hard data input.

[0124] Clause 4: The method of Clause 3, wherein the hard data input comprises offshore mapping data.

[0125] Clause 5: The method of Clause 4, wherein the offshore mapping data comprises one or more of seismic data, well data, or analog wave data.

[0126] Clause 6: The method of any one of Clauses 1-5, further comprising: generating a cable route in the graphical layer based on the prediction result and input data of a proposed structural location.

[0127] Clause 7: The method of any one of Clauses 1-6, further comprising: combining the graphical layer with one or more other graphical layers, to generate the graphical model, wherein each one of the graphical layer and the one or more other graphical layers corresponds to an ocean floor layer or a sedimentation layer.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0128] Clause 8: The method of Clause 7, wherein: the one or more other graphical layers are based on one or more other data outputs that are associated with one or more other data inputs, and the graphical model is an interactive model.

[0129] Clause 9: The method of any one of Clauses 1-8, further comprising: receiving a user selection input associated with the graphical layer, wherein the graphical layer corresponds to at least one of an ocean floor layer or a sedimentation layer; and displaying the graphical layer in an alternative representation based on the user selection input.

[0130] Clause 10: The method of Clause 9, wherein the alternative representation is an expanded view of the graphical layer.

[0131] Clause 11 : The method of any one of Clauses 1-10, further comprising: receiving at least one live data input of at least one live sedimentation factor; generating at least one live graphical layer based on the at least one live sedimentation factor; and generating an updated graphical model based at least on the at least one the at least one live graphical layer.

[0132] Clause 12: The method of any one of Clauses 1-11, further comprising: receiving input information associated with a proposed offshore wind energy generation site structure; generating at least one color coded map for a cable route associated with the proposed offshore wind energy generation site structure; and displaying the proposed offshore wind energy generation site structure and the at least one color coded map by the graphical model.

[0133] Clause 13: The method of Clause 12, wherein: the proposed offshore wind energy generation site structure is interactively movable on the graphical layer to a new position, and the new position generates at least one new cable route.

[0134] Clause 14: The method of any one of Clauses 1-13, wherein the graphical model comprises a seismic model, a source migration charge model, a play chance mapping risk assessment model, a stratigraphic model, a reservoir model, a reservoir simulation model, or a volumetric model.

[0135] Clause 15: The method of any one of Clauses 1-14, wherein the GPM comprises at least one machine learning model.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0136] Clause 16: The method of any one of Clauses 1-15, wherein the graphical layer corresponds to at least one of an ocean floor layer or a sedimentation layer.

[0137] Clause 17: The method of any one of Clauses 1-16, wherein the data input comprises sedimentation data.

[0138] Clause 18: The method of Clause 17, wherein the sedimentation data comprise bathymetry data.

[0139] Clause 19: The method of Clause 17, wherein the sedimentation data comprise paleo topography data.

[0140] Clause 20: The method of Clause 17, wherein the sedimentation data comprise sediment type data.

[0141] Clause 21 : The method of Clause 17, wherein the sedimentation data comprise physical processes data.

[0142] Clause 22: The method of Clause 21, wherein the physical processes data comprise one or more of sea level curve data or tectonic event data.

[0143] Clause 23: The method of Clause 17, wherein the sedimentation data comprises boundary conditions.

[0144] Clause 24: The method of Clause 17, wherein the sedimentation data comprises high resolution forward model simulation data.

[0145] Clause 25: The method of Clause 17, wherein the sedimentation data comprises time settings data.

[0146] Clause 26: The method of Clause 25, wherein the time settings data define at least one of a prediction time frame, a maximum prediction time frame, or a minimum prediction time frame.

[0147] Clause 27: The method of any one of Clauses 1-26, wherein the sedimentation factor comprises at least one of sediment size, sediment shape, sediment gradation, sediment cement, sediment porosity, sediment sorting, organic content, or sediment thermal conductivity.

[0148] Clause 28: The method of any one of Clauses 1-27, wherein the plurality of FM algorithms comprise a diffusion algorithm.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO

[0149] Clause 29: The method of any one of Clauses 1-28, wherein the plurality of FM algorithms comprise a steady flow algorithm.

[0150] Clause 30: The method of any one of Clauses 1-29, wherein the plurality of FM algorithms comprise a wave dissipation algorithm.

[0151] Clause 31 : The method of any one of Clauses 1-30, wherein the plurality of FM algorithms comprise a wave action current algorithm.

[0152] Clause 32: The method of any one of Clauses 1-31, wherein the plurality of FM algorithms comprise a carbonate growth algorithm.

[0153] Clause 33: The method of any one of Clauses 1-32, wherein the plurality of FM algorithms comprise a carbonate redistribution algorithm.

[0154] Clause 34: The method of any one of Clauses 1-33, wherein the plurality of FM algorithms comprise a sediment accumulation algorithm.

[0155] Clause 35: One or more processing systems, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the one or more processing systems to perform a method in accordance with any one of Clauses 1-34.

[0156] Clause 36: One or more processing systems, comprising means for performing a method in accordance with any one of Clauses 1-34.

[0157] Clause 37: One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the operations of any one of Clauses 1-34.

[0158] Clause 38: One or more computer program products embodied on one or more computer-readable storage media comprising code for performing a method in accordance with any one of Clauses 1-34.Additional Considerations

[0159] The preceding description is provided to enable any person skilled in the art to practice the various embodiments described herein. The examples discussed herein are not limiting of theClient Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO scope, applicability, or embodiments set forth in the claims. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Also, features described with respect to some examples may be combined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0160] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0161] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

[0162] As used herein, unless stated otherwise, the term “or” is used in an inclusive sense. This inclusive usage of or is equivalent to “and / or”. Thus, when options are delineated using “or,” it permits the selection of one or more of the enumerated options concurrently. For example, if the document stipulates that a component may comprise option A or option B, it shall be understood to mean that the component may comprise option A, option B, or both option A and option B, and does not mean, unless stated expressly that the component includes either option A or option B.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WOThis inclusive interpretation ensures that all potential combinations of the options are permissible, rather than restricting the choice to a singular, exclusive option.

[0163] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and / or software component(s) and / or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor. Generally, where there are operations illustrated in figures, those operations may have corresponding counterpart means-plus- function components with similar numbering.

[0164] The following claims are not intended to be limited to the embodiments shown herein, but are to be accorded the full scope consistent with the language of the claims. Within a claim, reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. No claim element is to be construed under the provisions of 35 U.S.C. §112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.” All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WOCLAIMSWhat is claimed is:

1. An apparatus, comprising a processing system including one or more memories comprising executable instructions and one or more processors coupled to the one or more memories and configured to execute the executable instructions, the processing system configured to cause the apparatus to: receive, by a geological process model (GPM), a data input; select, a forward modeling (FM) algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input; generate, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor; and generate, by the GPM, a graphical layer of a graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

2. The apparatus of claim 1, wherein the processing system is configured to display the graphical model via a display device.

3. The apparatus of claim 1, wherein the processing system is configured to generate a map in the graphical layer based on the prediction result and input data of a proposed structural location.

4. The apparatus of claim 1, wherein the processing system is configured to: combine the graphical layer with one or more other graphical layers, to generate the graphical model, wherein each one of the graphical layer and the one or more other graphical layers corresponds to a sedimentation layer.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO5. The apparatus of claim 4, wherein: the one or more other graphical layers are based on one or more other data outputs that are associated with one or more other data inputs, and the graphical model is an interactive model.

6. The apparatus of claim 1, wherein the processing system is configured to: receive a user selection input associated with the graphical layer, wherein the graphical layer corresponds to a sedimentation layer; and display the graphical layer in an alternative representation based on the user selection input.

7. The apparatus of claim 6, wherein the alternative representation is an expanded view of the graphical layer.

8. The apparatus of claim 1, wherein the processing system is configured to: receive input information associated with a proposed offshore wind energy generation site structure; generate at least one color coded map for a cable route associated with the proposed offshore wind energy generation site structure; and display the proposed offshore wind energy generation site structure on the at least one color coded map by the graphical model.

9. The apparatus of claim 1, wherein the graphical layer corresponds to at least one sedimentation layer.

10. The apparatus of claim 1, wherein the data input comprises sedimentation data.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO11. The apparatus of claim 10, wherein the sedimentation data comprise bathymetry data.

12. The apparatus of claim 10, wherein the sedimentation data comprise paleo topography data.

13. The apparatus of claim 10, wherein the sedimentation data comprise sediment type data.

14. The apparatus of claim 10, wherein the sedimentation data comprise physical processes data.

15. The apparatus of claim 10, wherein the sedimentation data comprises high resolution forward model simulation data.

16. The apparatus of claim 10, wherein the sedimentation data comprises time settings data.

17. The apparatus of claim 16, wherein the time settings data define at least one of a prediction time frame, a maximum prediction time frame, or a minimum prediction time frame.

18. The apparatus of claim 1, wherein the sedimentation factor comprises at least one of sediment size, sediment shape, sediment gradation, sediment cement, sediment porosity, sediment sorting, organic content, or sediment thermal conductivity.Client Ref. No.: IS24.0966-WO-PCTD&S Ref. No.: SLBG240966WO19. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations for generating maps for offshore cable routes, the operations comprising: receiving, by a geological process model (GPM), a data input; selecting, by the GPM, a forward modeling (FM) algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input; generating, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor; and generating, by the GPM, a graphical layer of a graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

20. A method comprising receiving, by a geological process model (GPM), a data input; selecting, by the GPM, a forward modeling (FM) algorithm of a plurality of FM algorithms, wherein the FM algorithm is based on a type of the data input; generating, a data output using the FM algorithm, wherein the data output comprises a prediction result of a sedimentation factor; and generating, by the GPM, a graphical layer of a graphical model based on the data output, wherein the graphical layer comprises a graphic associated with the prediction result.

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