BACKGROUND

Field

[0001] The present disclosure is generally directed to oil and gas systems, and more specifically, for forecast systems and methods for drilling activity to serve as the basis for product and service demands.

Related Art

[0002] The most important factor in planning and operating of distribution systems is satisfying consumer demand. This means continuously providing customers with products/services at reasonable price and in a timely manner. Consumers/customers can involve active parties in drilling areas that require products and services such as pipes, transportation, rigs, and so on. Satisfying consumer demand is also important for industrial sectors such as oil industry in requiring a development plan for a country or global level.

[0003] Although the oil industry sector has attempted to predict output from individual wells and fields in order to plan future purchases of equipment and services, their performance is inadequate for multiple reasons. First, oil from most fields of any size takes years to appraise, develop, produce, transport and sell to market, while distributors need to address customer needs more frequently. Secondly, only the producing status of operating wells is insufficient to infer comprehensive insights about future drilling activities. More specifically, companies which provide drilling related products and services need to have an estimation of future wells and drilling areas, as such companies employ technologies that often require large front-end investments. Finally, there are external variables and events which effects the drilling activity around the world. Therefore, there is a need to provide solutions to address the aforementioned problems.

SUMMARY

[0004] There is no system in the related art that facilitates a unified general end to end forecast framework for oil and gas drilling activity related services and products. There are related art implementations for production forecast at the well level and overall production level, however, the current oil/gas production of drilled wells is insufficient for planning future drilling activities, as they are slowly changing, not useful for product distributors, and do not consider the potential for new drilling sites and wells.

[0005] The main motivation comes from the fact that oil/gas related products distributors in all three streams (i.e. upstream, midstream, and downstream) need to assess the large volume of available market in oil/gas industry, however they often miss important markets due to the fact that most available forecasting models work based on active wells and drilling sites. Example implementations described herein are directed to a forecasting tool that provides comprehensive insight into what drilling is currently happening, and what new area and wells are added in future. The example implementations utilize different sources of data both public and private to predict the amount of future drilling activities, potential locations for drilling, amount of drilling, and the measured depth of drilling. In the post forecast, the future predictions can be utilized to plan for product demand (from different sectors), inventory management, optimize transportation routes for product distribution and rigs movements, and be prepared for related services.

[0006] Example implementations are directed to facilitating a generic, end-to-end time series forecasting framework which is capable of managing heterogeneous sources of data for the oil/gas industry. Furthermore, the framework should be able to forecast for various geographical scales including state-level, county-level, and operator-level. With this description of the framework as a background, the proposed generic solution has the following components: 1) data preprocessing methods to integrate, aggregate, and stratification of data;

2) representative features to abstract meaningful and interpretable information from data; and

3) capability for forecasting multiple interrelated target outputs.

[0007] Related art implementations can only predict drilling forecasts one or two months ahead and is mostly based on rig, permit, and production data only. Such related art implementations only work based on univariable models and/or focus on the oil/gas production. Furthermore, they only predict the future of drilled areas.

[0008] In contrast, through the example implementations described herein, drilling activity can be predicted six months in advance so that it can engage potential customers long before they have made their drilling procurement decisions. The example implementations can facilitate robust and accurate forecasts and post forecast planning that is useful for different sectors of industry. [0009] Aspects of the present disclosure can involve a method, which can involve intaking operational data and external data, the operational data involving one or more of permit data, spud data, and lease data, and external data involves external macro-economic data associated with one or more rig systems, and global/local geopolitical events; training a plurality of models based on the operational data and the external data; and executing the plurality of models on operational data, streaming sensor data, and live external data associated with the one or more rig systems to generate a prediction of drilling activity for the one or more rig systems over a period of time.

[0010] Aspects of the present disclosure can involve a computer program, storing instructions which can involve intaking operational data and external data, the operational data involving one or more of permit data, spud data, and lease data, and external data involves macro-economic data associated with one or more rig systems, and global/local geopolitical events; training a plurality of models based on the operational data and the external data; and executing the plurality of models on operational data, streaming sensor data, and live external data associated with the one or more rig systems to generate a prediction of drilling activity for the one or more rig systems over a period of time. The instructions can be stored on a non- transitory computer readable medium to be executed by one or more processors.

[0011] Aspects of the present disclosure can involve a system, which can involve means for intaking operational data and external data, the operational data involving one or more of permit data, spud data, and lease data, and external data involves macro-economic data associated with one or more rig systems, and global/local geopolitical events; means for training a plurality of models based on the operational data and the external data; and executing the plurality of models on operational data, streaming sensor data, and live external data associated with the one or more rig systems to generate a prediction of drilling activity for the one or more rig systems over a period of time.

[0012] Aspects of the present disclosure can involve an apparatus, which can involve a processor, configured to intake operational data and external data, the operational data involving one or more of permit data, spud data, and lease data, and external data involves macro-economic data associated with one or more rig systems, and global/local geopolitical events; train a plurality of models based on the operational data and the external data; and execute the plurality of models on operational data, streaming sensor data, and live external data associated with the one or more rig systems to generate a prediction of drilling activity for the one or more rig systems over a period of time.

BRIEF DESCRIPTION OF DRAWINGS

[0013] FIG. 1(a) illustrates a system involving a plurality of rig systems and a management server, in accordance with an example implementation.

[0014] FIG. 1(b) illustrates an example timeline for a rig system, in accordance with an example implementation.

[0015] FIG. 2 illustrates an example rig in accordance with an example implementation.

[0016] FIG. 3 illustrates an example configuration of a rig system, in accordance with an example implementation.

[0017] FIG. 4 illustrates a configuration of a management server, in accordance with an example implementation.

[0018] FIG. 5 illustrates an example of the end-to-end time series forecasting framework in accordance with an example implementation.

[0019] FIG. 6 illustrates an example application of the framework in an oil and gas system, in accordance with an example implementation.

[0020] FIG. 7 illustrates an example framework for unstructured data, in accordance with an example implementation.

[0021] FIG. 8 illustrates examples of time series analysis and survival analysis, in accordance with an example implementation.

[0022] FIG. 9 illustrates examples of combining survival analysis and forecasting to create an overall forecast, in accordance with an example implementation.

[0023] FIG. 10 illustrates an example of data aggregation to form feature vectors, in accordance with an example implementation. DETAILED DESCRIPTION

[0024] The following detailed description provides details of the figures and example implementations of the present application. Reference numerals and descriptions of redundant elements between figures are omitted for clarity. Terms used throughout the description are provided as examples and are not intended to be limiting. For example, the use of the term “automatic” may involve fully automatic or semi-automatic implementations involving user or administrator control over certain aspects of the implementation, depending on the desired implementation of one of ordinary skill in the art practicing implementations of the present application. Selection can be conducted by a user through a user interface or other input means, or can be implemented through a desired algorithm. Example implementations as described herein can be utilized either singularly or in combination and the functionality of the example implementations can be implemented through any means according to the desired implementations.

[0025] FIG. 1(a) illustrates a system involving a plurality of rig systems and a management server, in accordance with an example implementation. One or more rig systems 101-1, 101- 2, 101-3, 101-4, and 101-5 can each involve a corresponding rig 200-1, 200-2, 200-3, 200-4, 200-5 as illustrated in FIG. 2 along with a corresponding rig node 300-1, 300-2, 300-3, 300-4, and 300-5 as illustrated in FIG. 3. Each of the rig systems 101-1, 101-2, 101-3, 101-4, and 101- 5 is connected to a network 100 which is connected to a management server 102. The management server 102 manages a database 103, which contains data aggregated from the rig systems in the network 100. In alternate example implementations, the data from the rig systems 101-1, 101-2, 101-3, 101-4, and 101-5 can be aggregated to a central repository or central database such as public databases that aggregate data from rigs or rig systems, such as for government compliance purposes, and the management server 102 can access or retrieve the data from the central repository or central database. Further, there is a database for permit/lease data 104 connected to the network 100 which can be managed by a government agency and is responsible for issuing or confirming leases and permits to operators, from which lease or permit data can be obtained by management server 102.

[0026] FIG. 1(b) illustrates an example timeline for a rig system, in accordance with an example implementation. The timeline for the rig system 101 may include multiple phases of rig operation. These phases can include (but are not limited to) a lease/permit phase, an exploration phase, a drilling phase, a completions phase, a production phase, a processing phase and a pipeline phase. In the following description, the term “process” may also be used interchangeably with the term “phase”. Example implementations may involve data or attributes associated with one or more of the phases of the timeline, depending on the desired implementation.

[0027] During the lease/permit phase, a six to twelve month lease or permit is obtained to permit drilling activity and exploration.

[0028] During the exploration phase, the well is initially drilled to determine whether reservoirs with oil or gas are present and the initial construction of the rig. In example implementations described herein, the rig node may be configured to assist the user in determining how to configure the rig and the parameters for the drilling during the exploration phase.

[0029] The drilling phase follows the exploration phase as determined in the exploration phase, e.g., if promising amounts of oil and gas are confirmed from the exploration phase. During the drilling phase, the size and characteristics of the discovery are determined, and technical information is utilized to allow for more optimal methods for recovery of the oil and gas. An appraisal drilling can be performed and a rig is established. In example implementations described herein, the rig node may be configured to assist the user in determining appropriate parameters for the drilling and assist in the management and obtaining of desired characteristics for the rig. During the drilling phase, sensor data will be produced by the rig system to indicate how well an area is producing, wherein the example implementations can then determine its effect on attracting attention for more activities in the same area or areas with similar geo characteristics.

[0030] The completions phase is directed to the determination as to whether the well should be completed as a well, or whether it should be abandoned as a dry hole. The completion phase transforms the drilled well into a producing well. During this phase, the casing of the rig may be constructed, along with the perforations. Various aspects of the construction of the rig, such as cementing, gravel packing and production tree installation may be employed. Sensors may be employed to determine various parameters for facilitating the completion of the rig, such as rate of flow, flow pressure and gas to oil ratio measurements, but not limited thereto.

[0031] The production phase follows the completions phase and is directed to the facilitation of production of oil or gas. The production phase includes the operation of wells and compressor stations or pump stations, waste management, and maintenance and replacement of facility components. Sensors may be utilized to observe the above operations, as well as determining environmental impacts from parameters such as sludge waste accumulation, noise, and so on. Example implementations described herein may provide feedback to rig system operators to maximize the production of the rig based on the use of model signatures.

[0032] During the processing and pipelining phase, the produced oil or gas is processed and transferred to refineries through a pipeline.

[0033] FIG. 2 illustrates an example rig 200 in accordance with an example implementation. The example implementation depicted in FIG. 2 is directed to a shale gas rig. However, similar concepts can be employed at other types of rigs as well without departing from the inventive scope of the present disclosure, for example, example implementations described herein can also be applied to horizontal oil wells by integrating features from multiple upstream processes. The well 201 may include one or more gas lift valves 201-1 which are configured to control hydrostatic pressure of the tubing 201-2. Tubing 201-2 is configured to extract gas from the well 201. The well 201 may include a case 201-3 which can involve a pipe constructed within the borehole of the well. One or more packers 201-4 can be employed to isolate sections of the well 201. Perforations 201-5 within the casing 201-3 allow for a connection between the shale gas reservoir to the tubing 201-2.

[0034] The rig 200 may include multiple sub-systems directed to injection of material into the well 201 and to the production of material from the well 201. For the injection system 250 of the rig 200, there may be a compressor system 202 that includes one or more compressors that are configured to inject material into the well such as air or water. A gas header system 202 may involve a gas header 202-1 and a series of valves to control the injection flow of the compressor system 202. A choke system 203 may include a controller or casing choke valve which is configured to reduce the flow of material into the well 201.

[0035] For the production system 260 of the rig 200, there may be a wing and master valve system 204 which contains one or more wing valves configured to control the flow of production of the well 201. A flowline choke system 205 may include a flowline choke to control flowline pressure from the well 201. A production header system 206 may employ a production header 206-1 and one or more valves to control the flow from the well 201, and to send produced fluids from the well 201 to either testing or production vessels. A separator system 207 may include one or more separators configured to separate material such as sand or silt from the material extracted from the well 201.

[0036] As illustrated in FIG. 2 various sensors may be applied throughout the rig to measure various data or attributes for a rig node, which are described in further detail below. The sensors are identified by an “S” in an octagon in FIG. 2. These sensors provide feedback to the rig node which can interact with the system as illustrated in FIGS. 1(a) and 1(b), and can be fed to the management server 102 for database storage 103, and/or supplied to a central repository or database such as a public database, which can then be harvested by management server 102. figure 8 as well is it is the well log data, also figure 5 as sensor data, and fig 6 as production data

[0037] FIG. 3 illustrates an example configuration of a rig system 101, in accordance with an example implementation. The rig system 101 includes a rig 200 as illustrated in FIG. 2 which contains a plurality of sensors 210. The rig system 101 includes a rig node 300 which may be in the form of a server or other computer device and can contain a processor 301, a memory 302, a storage 303, a data interface (I/F) 304 and a network I/F 305. The data I/F 304 interacts with the one or more sensors 210 of the rig 200 and store raw data in the storage 303, which can be sent to a management server for processing, or to a central repository or central database. The network I/F 305 provides an interface to connect to the network 100.

[0038] FIG. 4 illustrates a configuration of a management server 102, in accordance with an example implementation. Although the example implementation for apparatuses is described as management server 102, other implementations are also possible depending on the desired implementation. Management server 102 may involve a processor 401, a memory 402, a storage I/F 404 and a network I/F 405. The processor 401 is configured to execute one or more programs in the memory 402, to process data and for calculating composite similarity scores. The storage I/F 404 is the interface to facilitate connections between the management server 102 and the database 103. The network I/F 405 facilitates interactions between the management server 102 and the plurality of rig systems. Data is aggregated to the management server by the network I/F and then subsequently stored in the database, for example, for future analytics. Alternatively, the plurality of rig systems may send the data to a central database or repository, which is then processed by the management server 102. [0039] Processor(s) 401 can be configured to intake operational data and external data, the operational data involving one or more of permit data, spud data, and lease data, and external data involving one or more of macro-economic data associated with one or more rig systems, and geopolitical events; train a plurality of models based on the operational data and the external data; and executing the plurality of models on operational data, streaming sensor data, and the external data associated with the one or more rig systems to generate a prediction of drilling activity for the one or more rig systems over a period of time as illustrated in FIGS. 5- 10. The training of the models can be conducted by any machine learning technique in accordance with the desired implementation. Through such an example implementation, the predictions of the drilling activity can be made to be more accurate across a longer period of time (six months opposed to one or two months of the related art) due to the incorporation of operational data such as lease data. Further, the drilling progress can be helpful to provide a clue about the future of drilling new wells in advance. The example implementations can also be more robust than the related art implementations due to the incorporation of external data such as macro-economic data and geopolitical data, which can facilitate context-aware forecasts and dependence on events around the world. The real-time status of drilling in the area further validates the potential for future activity. The models can utilize signals with different ahead forecasting potential, low-level predictive signals such as real-time well-logs, lease and permits data which normally gives signal of drilling activity 6-12 months ahead, and context data such as event around the world which despite the potential of production delays/cancels/expedite drilling activities. Using all the above signals can thereby provide a more reliable prediction model in comparison to the related art.

[0040] Depending on the desired implementation, the plurality of models are a time series analysis model and a survival analysis model trained from a plurality of featurized vectors extracted from the operational data and the external data, wherein the prediction of drilling activity for the one or more rig systems over the period of time is conducted from an aggregation of predictions from the survival analysis model and the time series analysis model as illustrated in FIGS. 8 and 9. Such a plurality of featurized vectors can involve a feature vector having features extracted from historical data of the one or more rig systems; an event vector having features indicative of events extracted from a spatio-temporal database; and a context vector having features associated with drilling locations for the one or more rig systems. [0041] Processor(s) 401 can be configured to execute the plurality of models on the operational data, the streaming sensor data, and the external data by executing the time-series analysis model on the feature vector, the event vector, and the context vector extracted from the operational data, the streaming sensor data, and the external data to determine measured depth and number of wells to be drilled as illustrated in FIGS. 5-10

[0042] Processor(s) 401 can be configured to execute the plurality of models on the operational lease data, the streaming sensor data, and the external data by executing the survival analysis model on the feature vector, the event vector and the context vector extracted from the operational data, the streaming sensor data, and the external data to determine probability of drilling activity occurring at a well level, a location level, and a lease level as illustrated in FIGS. 5-10.

[0043] Processor(s) 401 can be configured to intake the operational data and the external data by executing embedding on unstructured data to transform the external data and events in an embedding space; executing clustering to cluster the embeddings of the unstructured data through topic modeling; conducting labeling on the clusters of embeddings to label each cluster as a weighted combination of events; and managing the labeled clusters in a spatio-temporal database associating each of the events according to time and location as illustrated in FIGS. 5-10.

[0044] FIG. 5 illustrates an example of the end-to-end time series forecasting framework in accordance with an example implementation. The example implementations described herein are directed to a generic, end-to-end time series forecasting framework which is capable of managing heterogeneous sources of data for the oil/gas industry. Furthermore, the framework should be able to forecast for various geographical scales including state-level, county-level, and operator-level. With this description of the framework as a background, the proposed generic solution, as shown in FIG. 5, has the following components: 1) data preprocessing methods 500 to integrate, aggregate, and stratify the data; 2) feature extraction 501 to obtain representative features to abstract meaningful and interpretable information from data; and 3) forecasting models 502 having the capability for forecasting multiple interrelated target outputs 503 which is used to provide to post-forecast models 504 to generate predictions such as demand forecast, land discovery, rig management, production transportation, and so on. [0045] Data preprocessing 500 is configured to conduct preprocessing on the data intake, which can include historical data, structured data, unstructured data and sensor data. Data preprocessing 500 can include converting the data into readable formats for feature extraction 501.

[0046] FIG. 6 illustrates an example application of the framework in an oil and gas system, in accordance with an example implementation.

[0047] Lease data records 601 are an arrangement between individual mineral owners and oil companies. The economic structure of the lease is straightforward: in exchange for an up front lease bonus payment, plus a royalty percentage of the value of any production, the mineral owner grants the oil company the right to drill and produce. This lease records can be a great signal to predict a drilling activity way back before drilling (i.e. 6 to 12 months ahead).

[0048] Spud data records 602 indicate the drilling operation for wells. The timing of the drilling operation varies from 10 days to 90 days, or even more. Completing the well can take a similar period. This data can help us to know about ongoing drilling activities, speed of drilling activities in an area, the overall interest to drill in a geographical location, and so on.

[0049] Permit data records 603 confirm if a land lease contract turns into an action for drilling. In example implementations, the permit data records 603 are a signal which is more robust to predict future drilling activities.

[0050] Product data 604 shows the amount of current and past production of oil/gas wells. This is an indicator of how much land has potential for future drilling activities.

[0051] Data mapping 610 maps the lease data records 601, spud data records 602, permit data records 603 and product data 604 based on the associated rig system. Determining the associated rig system can be conducted by any manner in accordance with the desired implementation, such as but not limited to reviewing the location of the data source or the location associated with the received data, from meta-data, and so on.

[0052] Data stratification 620 intakes the mapped data from data mapping 610 and intake external structured data 621, along with other data (e.g., list of states, counties, operators), to stratify all of the data together with respect to a particular rig system, location, and operator of a rig system. External structural data 621 can include any feature that can affect the oil/gas industry. Active oil rigs, market indicators, oil price, corporate yields, and so on can be in the external data. Other external data can involve overall information over each county, state, or an operator activity. Stratification is executed for different horizons in accordance with the desired implementation. Through such information, the drilling locations 630 for each of the rig systems can all be derived.

[0053] The stratified data associated with each of the drilling locations of the one or more rig systems can thereby be aggregated by data aggregation 640 to facilitate time series data that will be managed into equally spaced time series. The data is aggregated based on a period of time (e.g., monthly, daily, etc.). The aggregated data 640 to be processed by feature extraction 650 to form feature vectors form each time step for storage in the historical data 660.

[0054] FIG. 7 illustrates an example framework for unstructured data, in accordance with an example implementation. The type of data that can be included in unstructured context data 701 can include news data and documents data. News data contains news around the world which affects the interest and activity in oil and gas industry. Documents data can include the report from government, audit from executives, lease and spud operators, etc. The unstructured context data 701 are then provided to preprocessing 702 to format the data for consumption by embedding 703.

[0055] Embedding 703 is a low-dimensional representation of unstructured data which is used to transform high-dimensional vectors of unstructured data (e.g. text). Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models. This can bring similar documents to similar lower dimension representation, and therefore the clustering will be more robust and easier to perform.

[0056] Clustering 704 involves topic modeling methods to cluster embeddings of unstructured data.

[0057] Labeling 705 can be manual or rule-based to convert each cluster as a weighted combination of different events. Example of events can be economic sanctions, elections, pandemics, war, and so on. The labels are processed by event-detection 706 to determine the detected events for providing into spatio-temporal databases. [0058] Example implementations generate spatial-temporal event vectors around the world. For each period and each geographical location, example implementations produce a vector which includes occurrences of events.

[0059] FIG. 8 illustrates examples of time series analysis and survival analysis, in accordance with an example implementation.

[0060] Context Data Event is procured from the spatio-temporal databases (each time stamp has a context vector corresponds to events which affects the oil/gas industry (i.e. war, sanctions, pandemic, etc.). Well logs and geology reports are sensor data obtained from the sensors of the rig system as illustrated in FIG. 2. Such data shows the potential of a location for future drilling as well as the structure of an area and its correlation with oil/gas productions. This will be used mostly for survival analysis part, but still can be used for time series analysis. Drilling historical data is obtained from the historical time series data from FIG. 6.

[0061] Time series analysis 811 focuses on the history of drilling operation. Using different sources of data including well-logs, current drilling activities, production status, active rigs, active operators, active leases, and issued permits, contexts, events around the world, and so on, example implementations can forecast the future drilling activities.

[0062] The example implementations involve models that utilize signals with different ahead forecasting potential, including low-level predictive signals such as real-time well-logs, lease and permits data which normally gives signal of drilling activity 6-12 months ahead, context data such as event around the world which, despite the potential of production, delays/cancels/expedite drilling activities, and so on. Using all the above signals can facilitate a more reliable prediction model.

[0063] Survival analysis 812 analyzes the expected duration of time until one or more events happen. The events are different depending on the problem to be solved at hand. Survival analysis for drilling activity can be executed in three different levels: well level, lease level, and geological level. The well level is indicative of how a well will perform in terms of production. This will work as a signal for future drilling activity near the well. The lease level is based on detected leases, as a lease on itself shows a potential for future activities. However, many leases do not turn into drilling activities. Lease level survival analysis is conducted to be more realistic about the possibility of future activities. Location-level is lease survival analysis on a larger scale wherein based on the data on similar places (e.g. county, states) new drilling sites can thereby be identified.

[0064] The combination of the survival analysis 812 and the time series analysis 811 is provided to the optimization layer 830, which takes the forecast and incorporates other data such as rig locations 821, transportation plans 822, sale profile 823, and transportation routes 824 for post forecast processing and planning for inventory, transportation, production, services, and so on.

[0065] FIG. 9 illustrates examples of combining survival analysis and forecasting to create an overall forecast, in accordance with an example implementation. Time series analysis is conducted on active oil/gas activities 900, such as drilled leases, spudded wells and events to generate feature vector 901, event vector 902, and context vector 903. The situation for active areas of drilling is analyzed to see how the situation will continue in future.

[0066] Survival analysis is conducted on potential oil/gas activities 910, such as undrilled leases, geology reports, and events. For example, undrilled leases, new lands to see if they have potential. This also will be based on similarity of new leases to the current active areas and the probability of their success.

[0067] Feature vector 901 comes from the features in historical data from FIG. 6. An example of the feature vector 901 can be [oil price, active drilling lease, production status, etc], however, the number of features can be adjusted in accordance with the desired implementation.

[0068] Event vector 902 includes events that are extracted from the spatio-temporal databases. An example of event vector 902 with four features can be [pandemic yes, war yes, election yes, disaster no]

[0069] Context vector 903 includes features indicative of the situation where drilling is happening. An example of context vector 903 can be [current production status of wells, geological reports features]

[0070] The aggregation of this forecasts shows the overall activity in the future. This is conducted for analyzing the future activities for both situations: 1) forecasting progress 904 for places that are sufficiently known so that the future can be predicted, i.e. time series analysis, 2) probability of future activities 914 for places that need exploration sometime in the near future. In this case the aggregation will find places where there will be oil wells in the near future and their potential.

[0071] FIG. 10 illustrates an example of data aggregation to form feature vectors, in accordance with an example implementation. For each month, a record is obtained in terms of features from different kinds of data (e.g. context, structured, unstructured).

[0072] The vectors are aggregated based on the geographical scale that are targeted for the forecast.

[0073] Example implementations involve a demand forecast and decision support system involving products and services for drilling activities. Through the example implementations described herein, a context aware system for more reliable decisions on inventory management, and transportation of a decision supporting system for discovery of new sale markets can be realized.

[0074] Example implementations can also facilitate early signaling system for out-of- control / high techno-economic impact events.

[0075] Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations within a computer. These algorithmic descriptions and symbolic representations are the means used by those skilled in the data processing arts to convey the essence of their innovations to others skilled in the art. An algorithm is a series of defined steps leading to a desired end state or result. In example implementations, the steps carried out require physical manipulations of tangible quantities for achieving a tangible result.

[0076] Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, can include the actions and processes of a computer system or other information processing device that manipulates and transforms data represented as physical (electronic) quantities within the computer system’s registers and memories into other data similarly represented as physical quantities within the computer system’s memories or registers or other information storage, transmission or display devices. [0077] Example implementations may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored in a computer readable medium, such as a computer-readable storage medium or a computer-readable signal medium. A computer-readable storage medium may involve tangible mediums such as, but not limited to optical disks, magnetic disks, read-only memories, random access memories, solid state devices and drives, or any other types of tangible or non-transitory media suitable for storing electronic information. A computer readable signal medium may include mediums such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Computer programs can involve pure software implementations that involve instructions that perform the operations of the desired implementation.

[0078] Various general-purpose systems may be used with programs and modules in accordance with the examples herein, or it may prove convenient to construct a more specialized apparatus to perform desired method steps. In addition, the example implementations are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the example implementations as described herein. The instructions of the programming language(s) may be executed by one or more processing devices, e.g., central processing units (CPUs), processors, or controllers.

[0079] As is known in the art, the operations described above can be performed by hardware, software, or some combination of software and hardware. Various aspects of the example implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which if executed by a processor, would cause the processor to perform a method to carry out implementations of the present application. Further, some example implementations of the present application may be performed solely in hardware, whereas other example implementations may be performed solely in software. Moreover, the various functions described can be performed in a single unit, or can be spread across a number of components in any number of ways. When performed by software, the methods may be executed by a processor, such as a general purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions can be stored on the medium in a compressed and/or encrypted format.

[0080] Moreover, other implementations of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the teachings of the present application. Various aspects and/or components of the described example implementations may be used singly or in any combination. It is intended that the specification and example implementations be considered as examples only, with the true scope and spirit of the present application being indicated by the following claims.