Auto-Detection And Classification Of Rig Activities From Trend Analysis Of Sensor Data

Abstract

Systems and methods for auto-detection and classification of rig activities from trend analysis of sensor data are provided. Sensor data from rig equipment may be obtained during wellsite operations. The sensor data may be analyzed to identify one or more index points where a trend in the sensor data changes. The sensor data may be segmented into a first set of time segments representing macro activities performed during the well site operations, based on the one or more identified index points. Statistical analysis may be performed on the sensor data within each first time segment to identify points where statistical properties of the sensor data change. Each first time segment may he segmented into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties.

Description

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring wellsite operations and particularly to real time monitoring and identification of rig activities during wellsite operations using sensor data.

BACKGROUND

Rig activity classification is a critical step in drilling surveillance and performance improvement. For example, the limitations in the performance of the drilling crews or rig equipment can be identified based on the classifications and can be used for training drilling crews or in determining replacement of rig equipment. It is also desired to accurately and quickly classify complex or expensive rig activities to guarantee safe and cost-effective wellsite operations.

Current methods to classify rig activities are limited. For example, traditional methods are inaccurate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary drilling system suitable for implementing embodiments of the present disclosure.

FIG. 2 is a flow diagram of an exemplary process for detecting and classifying rig activities based on trend analysis of sensor data acquired during operations at a wellsite.

FIG. 3 is a plot diagram of sensor data acquired during wellsite operations.

FIG. 4 is a diagram of a time series data curve generated from the sensor data of FIG. 3, with index points identifying where a slope and/or associated trend in the data. changes as different macro-activities are performed at the wellsite.

FIG. 5 is a diagram of another time series data curve generated from the sensor data of FIG. 3, with index points identifying where the slope and/or associated trend in the data changes as different macro-activities are performed at the wellsite.

FIG. 6 is a diagram of time series data segmented into a first set of time segments corresponding to different macro-activities at the wellsite based on the index points along the time series data curve of FIG. 5.

FIG. 7 is a diagram of statistical analysis performed on a selected one of the first set of time segments of FIG. 6,

FIG. 8 is a diagram of the selected one of the first time segments of FIG. 7 that has been further segmented into a second set of time segments corresponding to micro-activities identified through statistical analysis of the relevant time series data.

FIG. 9 is a flow diagram of an additional process for detecting and classifying rig activities based on trend analysis of sensor data acquired during operations at a welisite.

FIG. 10 is a block diagram of an exemplary computer system in which embodiments of the present disclosure may he implemented.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Embodiments of the present disclosure relate to methods and systems utilizing sensor data to automatically detect and classify various rig activities during welisite operations. While the present disclosure is described herein with reference to illustrative embodiments for particular applications, it should be understood that embodiments are not limited thereto. Other embodiments are possible, and modifications can be made to the embodiments within the spirit and scope of the teachings herein and additional fields in which the embodiments would be of significant utility. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the relevant art to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

It would also be apparent to one of skill in the relevant art that the embodiments, as described herein, can be implemented in many different embodiments of software, hardware, firmware, and/or the entities illustrated in the figures. Any actual software code with the specialized control of hardware to implement embodiments is not limiting of the detailed description. Thus, the operational behavior of embodiments will be described with the understanding that modifications and variations of the embodiments are possible, given the level of detail presented herein,

In the detailed description herein, references to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment.

Illustrative embodiments and related methodologies of the present disclosure are described below in reference to FIGS. 1-10 as they might be employed in, for example, one or more computer systems for monitoring production operations at one or more wellsites within a hydrocarbon producing field. Other features and advantages of the disclosed embodiments will be or will become apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. It is intended that all such additional features and advantages be included within the scope of the disclosed embodiments, Further, the illustrated. figures are only exemplary and are not intended to assert or imply any limitation with regard to the environment, architecture, design, or process in which different embodiments may be implemented.

FIG. 1 illustrates an exemplary drilling system 100 suitable for implementing the embodiments of the present disclosure. While FIG. 1 generally depicts a land-based drilling system or rig, those skilled in the art will readily recognize that the principles described herein are equally applicable to subsea drilling operations that employ floating or sea-based platforms and rigs, without departing from the scope of the disclosure.

As illustrated, the drilling system 100 may include a drilling platform 102 that supports a derrick 104 having a traveling block 106 for raising and lowering a drill string 108. The drill string 108 may include, but is not limited to, drill pipe and coiled tubing, as generally known to those skilled in the art. A kelly 110 supports the drill string 108 as it is lowered through a rotary table 112. A drill bit 114 is attached to the distal end of the drill string 108 and is driven either by a downhole motor and/or via rotation of the drill string 108 from the well surface, As the bit 114 rotates, it creates a borehole 116 that penetrates a subterranean formation 118.

A pump 120 (e.g., a mud pump) circulates mud 122 through a feed pipe 124 and to the kelly 110, which conveys the mud 122 downhole through the interior of the drill string 108 and through one or more orifices in the drill bit 114. The mud 122 is then circulated back to the surface via an annulus 126 defined between the drill string 108 and the walls of the borehole 116. At the surface, the recirculated or spent mud 122 exits the annulus 126 and may be conveyed through chokes 136 (also referred to as a choke manifold) to one or more mud cleaning unit(s) 128 (e,g., a shaker, a centrifuge, a hydrocyclone, a separator (including magnetic and/or electrical separators), a desilter, a desander, a separator, a filter, a heat exchanger, any fluid reclamation equipment, and the like) via an interconnecting flow line 130. After passing through the mud cleaning unit(s) 128, a “cleaned” mud 122 is deposited into a nearby retention pit 132 (e.g., a mud pit or mud tank). While illustrated as being arranged at the outlet of the wellbore 116 via the annulus 126, those skilled in the art will readily appreciate that the mud cleaning unit(s) 128 may be arranged at any other location in the drilling system 100 to facilitate its proper function, without departing from the scope of the scope of the disclosure.

At the retention pit 132 (or before or after), the drilling system 100 may include one or more mud treatment units. The mud 122 may be treated to change its composition and properties. For example, weighting agents like barite may be added to the mud 122 to increase its density. In another example, base fluid may be added to the mud 122 to decrease its density. In the illustrated drilling system 100, the addition of materials to the mud 122 may be achieved with a mixing hopper 134 communicably coupled to or otherwise in fluid communication with the retention pit 132. The mixing hopper 134 may include, but is not limited to, mixers and related mixing equipment known to those skilled in the art. In other embodiments, however, the materials may be added to the mud 122 at any other location in the drilling system 100. In at least one embodiment, for example, there could he more than one retention pit 132, such as multiple retention pits 132 in series. Moreover, the retention pit 132 may be representative of one or more fluid storage facilities and/or units where the materials may be stored, reconditioned, and/or regulated until added to the mud 122.

The various components of the drilling system 100 may further include one or more sensors, gauges, pumps, compressors, and the like used store, monitor, regulate, convey, and/or recondition the exemplary muds 122 (e.g., sensors and gauges to measure the composition and/or pressure of the mud, compressors to change the pressure of the mud, and the like). The drilling system 100 may include further sensors to monitor drilling operations at a wellsite.

While not specifically illustrated herein, the disclosed drilling system 100 may further include drill collars, mud motors, downhole motors and/or pumps associated with the drill string 108, MWD/LWD tools and related telemetry equipment, sensors or distributed sensors associated with the drill string 108, downhole heat exchangers, valves and corresponding actuation devices, tool seals, packers and other wellbore isolation devices or components, and the like. The drilling system 100 may also further include a control system so communicably coupled to various components of the drilling system 100 (e.g., tools, pumps, sensors, and the like) and can execute the algorithms, methods, and drilling system control described herein.

FIG. 2 is a flow diagram of a process 200 for detecting and classifying rig activities based on trend analysis of sensor data acquired during operations at a wellsite. Examples of such wellsite operations include, but are not limited to, drilling, completion, and injection stimulation operations at the wellsite. The sensor data may be acquired from, for example, downhole sensors coupled to a drill string (e.g., drill string 108 of FIG. 1, as described above) disposed within a. wellbore being drilled at the wellsite. Process 200 in this example may provide a way for a well operator to identify when different types of rig activities are performed. and make any necessary decisions in real-time to ensure a safe and/or cost-effective drilling operation. For example, complex and/or expensive rig activities may be identified and monitored to calculate and reduce non-productive time (NPT) and/or invisible lost time (ILT). In addition, the steps of process 200 may increase the accuracy and/or classification of various rig activities or operations compared to traditional methods. Although not shown in FIG. 2, process 200 may include additional or alternative steps related to detecting rig activities during operations at a wellsite. In addition, each step shown in FIG. 2 may include one or multiple steps or processes.

The rig activities identified by process 200 may include, for example and without limitation: drilling, making a. connection, inslip, pre-connection activities, post-connection activities, reaming, trip in, and trip out. As described herein, drilling may refer to the operation of making borehole 116 in the ground or seabed through applying weight or load on the drill bit 114. Making a connection may refer to the operation of connecting an additional section of pipe to the drill string 108. In slip may refer to the operation of removing the drill bit 114 from the bottom of the borehole 116 and adding another section of pipe to the drill string 108 to increase its length to drill the borehole 116 deeper. Pre-connection may refer to the operation just before the inslip, when the drill hit 114 is not at the bottom of the borehole 116. Post-connection may refer to the operation just after the inslip, when the drill bit 114 is not at the bottom of the borehole 116. Inslip, pre-connection, and post-connection may be sub-activities or operations of making a connection. Reaming may refer to the operation of enlarging the borehole 116, such as in diameter. Trip in may refer to the operation of tripping the drill string 108 into the borehole 116 until the drill bit 114 touches the bottom of the borehole 116. Trip out may refer to the operation of tripping the drill string 108 out of the borehole 116, such as to change the drill bit 114 and/or downhole motor/assembly. These activities are exemplary only, and the process 200 may identify further rig activities or operations.

As described in further detail below, process 200 may provide a cascaded algorithm to detect layers of rig activities. For instance, process 200 may employ a multi-level approach to detect or identify one or more macro activities and one or more micro activities of wellsite operations. Each macro activity may be a broad general activity of the drilling system 100, such as the activities associated with the making a connection, trip in, trip out, and drilling operations described above. Each micro activity may be a single process within a macro activity. For instance, the micro activities may include one or more activities associated with the inslip, pre-connection, and post-connection operations described above. Each macro activity may include one or more micro activities, such as a plurality of micro activities.

As shown in FIG. 2, process 200 may begin with step 202, in which sensor data from rig equipment is obtained during wellsite operations. For example, the drilling system 100 of FIG. 1, described above, may include one or more sensors monitoring wellsite operations at one or more wellsites within a hydrocarbon producing field. Depending on the application, the sensor data may be obtained in real time or near real time. In some embodiments, the sensor data may be obtained from one or more sensors or computing systems over a network. The obtained sensor data may include data representing drill bit depth, borehole depth, and hook load values measured by one or more sensors coupled to the rig equipment during the wellsite operations.

In step 204, time series data may be generated from the obtained sensor data. For example, the obtained sensor data may be indexed, listed, or graphed in time order, such as in a sequence of discrete-time data. In one or more embodiments, a time series curve may be generated from the time series data. In some embodiments, a time series curve may be generated indicating a difference between respective values of borehole depth and drill bit depth measured by the one or more sensors during the wellsite operations over a period of time, as detailed below.

In step 206, the time series data generated in step 204 may be analyzed to identify one or more index points. The index points may be where a trend in the time series data changes. For example, a trend analysis may be utilized to identify one or more correlations exhibited by the time series data. In one or embodiments, a linear regression, such as a rolling window linear regression, may be performed on the time series data. The linear regression may determine a gradient transition of the obtained sensor data over time. The linear regression may be applied to the data obtained from any number of sensors. For instance, the linear regression may determine a gradient for the time series data or curve with respect to borehole depth and the difference between borehole depth and drill bit depth. In some embodiments, the one or more identified index points may correspond to one or more changes in the gradient transition of the sensor data. For example, the one or more index points may correspond to indexes where a slope of a line representing the gradient transition of the sensor data changes.

In step 208, the time series data may be segmented into a first set of time segments based on the one or more identified index points. The first set of time segments may represent macro activities performed during the wellsite operations. For example, the time series data may be segmented at the index points identified in step 206 and labelled for macro activities. The labelling of the macro activities may depend on the characteristics of the gradient transition of the sensor data between two identified index points. For instance, positive slope values of the line representing the gradient transition of the sensor data between two identified index points may correspond to a first macro activity of wellsite operations, such as trip in. Negative slope values of the line representing the gradient transition of the sensor data between two identified index points may correspond to a second macro activity of wellsite operations, such as trip out. Flat slope values of the line representing the gradient transition of the sensor data between two identified index points may correspond to a third macro activity of wellsite operations, such as drilling or reaming.

In step 210, statistical analysis may be performed on the time series data within each first time segment segmented in step 208. The statistical analysis may identify points within each first time segment where statistical properties of the time series data changes. For example, the statistical analysis performed on the time series data may identify where the mean or variance of the time series data changes. The statistical analysis may focus on one or more particular streams or types of data. For instance, the statistical analysis may be performed on the time series data to detect indexes or points where the statistical properties of the hook load data changes. In one or more embodiments, a Pruned Exact Linear Time (PELT) algorithm may be applied to the time series data to detect when an operating parameter drops. For example, the PELT algorithm may be applied to the time series data to detect when the hook load value drops.

In step 212, each first time segment may be segmented into a second set of time so segments based on points of change in the statistical properties of the corresponding time series data. The second set of time segments may represent micro activities performed during the wellsite operations. For example, the time series data may be segmented at the points of change identified in step 210 and labelled for micro activities. The labelling of micro activities may depend on the characteristics of the change in the statistical properties identified in step 210. For example, points where hook load values suddenly drop may indicate the start and/or end of inslip activity. In some embodiments, segmenting the first time segments into the second set of time segments may include comparing average values of the operation data before and after the identified points of change. In some embodiments, segmenting the first time segments into the second set of time segments may include comparing the variance of the operation data before and after the identified points of change. Such embodiments are illustrative only, and the first time segments may be segmented into the second set of time segments using other algorithms and methods. For example, other algorithms may exist that can be used to compare the distribution of the operation data before and after the identified points of change, such as, for example, a gaussian distribution, a t-distribution, or the like.

Steps 202-212 described above are exemplary only, and process 200 may include any number of additional or alternative steps. For instance, process 200 may include calculating one or more operation efficiency descriptors. The operation efficiency descriptors may be calculated by comparing the time segment duration of at least one macro activity or micro activity with a best practice target duration. In some embodiments, the calculated operation efficiency descriptors may include invisible lost time (ILT) and non-productive time (NPT).

FIG. 3 is a plot diagram of real-time sensor data 300 acquired during wellsite operations. Examples of such wellsite operations include, but are not limited to, drilling, completion, and injection stimulation operations at a wellsite within a hydrocarbon producing field. As shown in FIG. 3, the sensor data 300 may be graphed in time order. For example, FIG. 3 illustrates borehole depth and drill bit depth measured or obtained over the course of a drilling operation performed along a planned path of a borehole within a subterranean reservoir formation, e.g., subterranean formation 118 of FIG. 1, as described above. The sensor data 300 may be acquired in step 202 of FIG. 2, described above.

FIG. 4 is a diagram of a time series data curve 400 generated from the sensor data 300 of FIG. 3. The time series data curve 400 may be generated from one or more sources of the sensor data 300, e.g., one or more downhole sensors coupled to a drill string for measuring so borehole depth and drill bit depth over the course of a drilling operation within the subterranean formation. The time series data curve 400 may be generated at step 204 of FIG. 2, described above. In one or more embodiments, a gradient or slope feature for the time series data curve 400 may be generated using a linear or non-linear regression algorithm, for example, a rolling window regression algorithm, though other algorithms are contemplated. For instance, a gradient or slope of the time series data curve 400 may be based on a rolling window linear regression of a difference between borehole depth and drill bit depth as measured by the downhole sensors. In one or more embodiments, a change in the slope of gradient of the time series data may be used to identify one or more index points where the data trend changes. Such trend changes may correspond to changes in the types of macro-activities performed at the wellsite.

For example, as shown in FIG. 4, the changes in the slope and associated trend of the time series data curve 400 at index points 402 and 404 may correspond to instances where the macro-activities performed at the wellsite changes from one type to another. Accordingly, the portions of the time series data curve 400 before and after each of index points 402 and 404 may represent different types of macro-activities performed at the welisite. The index points (e.g., index points 402 and 404) may be identified at step 206 of FIG. 2, described above. While index points 402 and 404 are shown in the example of FIG. 4, it should be appreciated that embodiments are not intended to be limited thereto and that any number of index points may be identified along the time series data curve 400.

FIG. 5 is a diagram of a time series data curve 500 generated from the sensor data 300 of FIG. 3. The time series data curve 500 may be generated from the same or different data source(s) as time series data curve 400 of FIG. 4, described above. For example, the time series data curve 500 may represent borehole depth only. The time series data curve 500 may be generated at step 204 of FIG. 2, described above. The time series data curve 500 may be generated using a linear or non-linear regression algorithm, such as a rolling window regression algorithm, though other algorithms are contemplated. For instance, FIG. 5 shows a gradient or slope transition of the borehole depth determined through a linear regression algorithm.

The time series data represented by curve 500 in FIG. 5 may be used to identify one or more index points (e.g., index points 502-512) of data or trend change where different macro-activities are performed at the wellsite, such as where the slope andlor associated trend in the data (e.g., slope) changes. Similar to index points 402 and 404 of FIG. 4, described above, the index points 502-512 may correspond with portions of the time series data curve 500 where a slope and/or a trend in the slope of the curve 500 changes as different macro-activities are performed at the welisite. The index points identified in FIG. 5 (e.g., index points 502-512) may be identified at step 206 of FIG. 2, described above. The index points 502 512 identified in FIG. 5 are for purposes of illustration only, and analysis of the time series data curve 500 may identify any number of index points, such as more or less index points.

The index points may be identified in both FIG. 4 and FIG. 5 to validate the accuracy of the index points. For example, the index points identified in FIG. 5 (e.g., index points 502-512) may be identified to validate the accuracy of the index points identified in FIG. 4 (e.g., index points 402 and 404). Similarly, the index points identified in FIG. 4 may validate the accuracy of the index points identified in FIG. 5. Depending on the application, the index points identified in FIG. 5 may be insufficient by themselves to identify the one or more macro activities without comparison with the index points identified in FIG. 4, or vice versa.

FIG. 6 is a diagram of the time series data segmented into a first set of time segments 600 corresponding to different macro-activities at the weilsite based on the index points 402, 404 of FIG. 4 and/or the index points 502 -512 of FIG. 5 identified through trend analysis of the sensor data 300. The first set of time segments 600 may be determined in step 208 of FIG. 2, described above. As shown in FIG. 6, the time series data may be segmented and labelled. for macro activities of wellsite operations. For example, between each pair of index points identified in FIG. 4 and/or FIG. 5, the time series data may be segmented into corresponding macro activities, such as nip in, drilling, making a connection, and trip out. The macro activity segmentation may be based on the slope and/or trend of the time series data curve 400 and/or 500. For instance, a positive slope between a pair of index points may indicate a first type of macro activity (e.g., drilling), a negative slope between a pair of index points may indicate a second type of macro activity (e.g., making a connection), and a flat slope between a pair of index points may indicate a third type of macro activity (e.g., trip in and/or trip out). For example, time segment 602 may span between index points 508 and 510 and correspond to a making a connection macro activity.

FIG. 7 is a diagram of statistical analysis performed on a selected one of the first set of time segments 600 of FIG. 6, described above. Specifically, FIG. 7 illustrates statistical analysis performed on the portion of sensor data 300 within time segment 602 representing a making a connection macro activity, though statistical analysis may be performed on any one so or combination of the first set of time segments 600. As shown in FIG. 7, the statistical analysis may generate an analysis curve 700 identifying one or more points (e.g., points 702 and 704) within a macro activity where the statistical properties of the sensor data 300 changes. For instance, FIG. 7 clearly shows points 702 and 704 where the statistical properties of hook load drops or jumps in value, though other data sources and/or changes in statistical properties are contemplated. The statistical analysis performed on the sensor data 300 may be a PELT changepoint detection algorithm. The statistical analysis may be performed in step 210 of FIG. 2, described above.

FIG. 8 is a diagram of the selected time segment (i.e., time segment 602) analyzed in FIG. 7 that has been further segmented into a second set of time segments 800 corresponding to micro-activities identified through statistical analysis of the relevant time series data. The second set of time segments 800 may be based on the points (e.g., points 702 and 704 of FIG. 7, described above) identified through statistical analysis of the sensor data 300. The second set of time segments 800 may be determined in step 212 of FIG. 2, described above.

As shown in FIG. 8, each macro activity may be segmented and labelled for micro activities of wellsite operations. For example, each macro activity may be segmented into one or more micro activities. For instance, based on the points identified in FIG. 7, the macro activity may be segmented into corresponding micro activities, such as pre-connection, inslip, and post-connection. The micro activity segmentation may be based on the statistical properties of the sensor data 300 within the macro activity. For instance, a statistical drop in hook load values may indicate an inslip operation. In the embodiment illustrated in FIG. 8, the portion of the macro-activity to the left of point 702 may represent a first type of micro activity (e.g., pre-connection), the portion between points 702 and 704 may represent a second type of micro activity (e.g., inslip), and the portion to the right of point 704 may represent a third type of micro activity (e.g., post-connection).

FIG. 9 is a flow diagram of an additional process 900 for detecting and classifying rig activities based on trend analysis of sensor data acquired during operations at a wellsite. One or more steps of process 900 may be similar to process 200 of FIG. 2, described above. As shown in FIG. 9, process 900 includes a macro activity detection process 902 and a micro activity detection process 904. In this manner, process 900 may employ a multi-level approach of detecting or identifying one or more macro activities and one or more micro activities of wellsite operations.

The macro activity detection process 902 may include one or more steps or sub-processes for detecting and labelling one or more macro activities of wellsite operations. For example, macro activity detection process 902 may include step 914, in which the time series data may be analyzed using a trend analysis algorithm. For instance, a rolling window linear regression may be performed on a time series curve generated from real time sensor data. obtained during wellsite operations, such as in steps 202 and 204 of FIG. 2, described above. Step 914 may also include performing a rolling window linear regression on a time series curve representing borehole depth. The rolling window linear regression, or other statistical analysis method, may generate a gradient or slope feature of the time series curve(s). At step 916, the slope of the gradient or slope feature generated in step may be analyzed. The analysis may note or identify one or more index points where the slope or gradient changes. Step 914 and/or step 916 may be similar to step 206 of FIG. 2, described above.

At step 918, the time series data or curve may be segmented and labelled for macro activities of wellsite operations. The time series data or curve may be segmented at the index points identified in step 916. For instance, the time series data or curve may be segmented into a first set of time segments, with each segment of the first set of time segments between two identified index points. Each segment of the first set of time segments may represent a macro activity of wellsite operations, such as the activities associated with making a connection, trip in, nip out, and drilling, or the like. Step 918 may be similar to step 208 of FIG. 2, described above.

The micro activity detection process 904 may include one or more steps or sub-processes for detecting and labelling one or more micro activities of wellsite operations. Micro activity detection process 904 may include step 920, in which statistical analysis is performed on the time series data to identify points where statistical properties of the time series data changes. For instance, a PELT changepoint detection algorithm may be performed on the time series data to identify one or more points where the variance or mean of the time series data (e.g., hook load value) changes. At step 922, the time series data is compared before and after the points identified in step 920. For example, the average value of the time series data (e.g., hook load value) before the points identified in step 920 may be compared with the average value of the same time series data after the identified points. Step 920 and/or step 922 may be similar to step 210 of FIG. 2, described above.

At step 924, the time series data or curve may be segmented and labelled for micro activities of wellsite operations. The time series data or curve may be segmented at the points identified in step 920. For instance, the time series data or curve may be segmented into a so second set of time segments, with each segment of the second set of time segments between two identified points. Each segment of the second set of time segments may represent a micro activity of wellsite operations, such as the activities associated with inslip, pre-connection, post-connection, or the like. The micro activity labelling may be based on the comparison of average values before and after the identified points. For instance, a sudden drop in hook load value after an identified point may indicate a start or end of an inslip activity, Step 924 may be similar to step 212 of FIG. 2, described above.

The steps of macro activity detection process 902 and micro activity detection process 904 described above are exemplary only, and process 900 may include any number of additional or alternative steps. For instance, process 900 may include calculating one or more operation efficiency descriptors. The operation efficiency descriptors may be calculated by comparing the time segment duration of at least one macro activity or micro activity with a best practice target duration. In some embodiments, the calculated operation efficiency descriptors may include invisible lost time (ILT) and non-productive time (NPT).

It should be appreciated that the steps of processes 200 and 900 may be performed by any type of computing device having at least one processor and a memory for storing instructions that the processor may read and execute to perform a plurality of functions for implementing the steps of process 200 or 900, as described above. Examples of such a computing device include, but are not limited to, a server, a desktop computer, a laptop computer, a tablet or other handheld computer, a personal digital assistant (PDA), a cellular telephone, a network appliance, a smart phone, a media player, a navigation device, a game console, or a combination of any these computing devices or other computing devices. In one or more embodiments, processes 200 and 900 may be performed by a distributed-computing environment or server farm in which the steps of process 200 or 900 may be performed by multiple processing devices with shared or separate memory components that are linked through a communications network. In such a distributed-computing environment, program modules may be located in both local and remote computer-storage media including memory storage devices. The present disclosure therefore may be implemented using various hardware devices, software, or a combination thereof An example of a computer system for implementing the disclosed embodiments is shown in FIG. 9.

Referring now to FIG. 10, a block diagram of an exemplary computer system 1000 in which embodiments of the present disclosure may be implemented is presented. For example, the steps of process 200 of FIG. 2 or process 900 of FIG. 9, as described above, may so be implemented using system 1000, System 1000 can be a computer, phone. PDA, or any other type of electronic device. Such an electronic device includes various types of computer readable media and interfaces for various other types of computer readable media. As shown in FIG. 10, system 1000 includes a permanent storage device 1002, a system memory 1004, an otput device interface 1006, a system communications bus 1008, a read-only memory (ROM) 1010, processing unit(s) 1012, an input device interface 1014, and a network interface 1016.

Bus 1008 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of system 1000. For instance, bus 1008 communicatively connects processing unit(s) 1012 with ROM 1010, system memory 1004, and permanent storage device 1002.

From these various memory units, processing unit(s) 1012 retrieves instructions to execute and data to process in order to execute the processes of the subject disclosure. The processing units) can be a single processor or a multi-core processor in different implementations.

ROM 1010 stores static data and instructions that are needed by processing unit(s) 1012 and other modules of system 1000. Permanent storage device 1002, on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when system 1000 is off. Some implementations of the subject disclosure use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as permanent storage device 1002.

Other implementations use a removable storage device (such as a floppy disk, flash drive, and its corresponding disk drive) as permanent storage device 1002. Like permanent storage device 1002, system memory 1004 is a read-and-write memory device. However, unlike storage device 1002, system memory 1004 is a volatile read-and-write memory, such a random access memory. System memory 1004 stores some of the instructions and data that the processor needs at runtime. In some implementations, the processes of the subject disclosure are stored in system memory 1004, permanent storage device 1002, and/or ROM 1010. For example, the various memory units include instructions for performing the rig activity detection techniques disclosed. herein. From these various memory units, processing unit(s) 1012 retrieves instructions to execute and data to process in order to execute the processes of some implementations.

Bus 1008 also connects to input and output device interfaces 1014 and 1006. Input device interface 1014 enables the user to communicate information and select commands to the system 1000. Input devices used with input device interface 1014 include, for example, alphanumeric, QWERTY, or T9 keyboards, microphones, and pointing devices (also called “cursor control devices”). Output device interfaces 1006 enables, for example, the display of images generated by the system 1000. Output devices used with output device interface 1006 include, for example, printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some implementations include devices such as a touchscreen that functions as both input and output devices. It should be appreciated that embodiments of the present disclosure may be implemented using a computer including any of various types of input and output devices for enabling interaction with a user. Such interaction may include feedback to or from the user in different forms of sensory feedback including, but not limited to, visual feedback, auditory feedback, or tactile feedback. Further, input from the user can be received in any form including, but not limited to, acoustic, speech, or tactile input. Additionally, interaction with the user may include transmitting and receiving different types of information, e.g., in the form of documents, to and from the user via the above-described interfaces.

Also, as shown in FIG. 10, bus 1008 also couples system 1000 to a public or private network (not shown) or combination of networks through a network interface 1016. Such a network may include, for example, a cloud-based network, a private or local area network (“LAN”), such as an Intranet, a medium area network, or a wide area network (“WAN”), such as the Internet. Any or all components of system 1000 can be used in conjunction with the subject disclosure.

These functions described above can be implemented in digital electronic circuitry, in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products. Programmable processors and computers can be included in or packaged as mobile devices. The processes and logic flows can be performed by one or more programmable processors and by one or more programmable logic circuitry. General and, special purpose computing devices and storage devices can be interconnected through communication networks.

Some implementations include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM), recordable compact discs (CD-R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a variety of recordable/rewritable DVDs DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and/or solid state hard drives, read-only and recordable Blu-Ray® discs, ultra density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media can store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some implementations are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some implementations, such integrated circuits execute instructions that are stored on the circuit itself. Accordingly, the steps of process 200 of FIG. 2 or process 900 of FIG. 9, as described above, may be implemented using system 1000 or any computer system having processing circuitry or a computer program product including instructions stored therein, which, when executed by at least one processor, causes the processor to perform functions relating to these methods.

As used in this specification and any claims of this application, the terms “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. As used herein, the terms “computer readable medium” and “computer readable media” refer generally to tangible, physical, and non-transitory electronic storage mediums that store information in a form that is readable by a computer.

Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data (e.g., a. web page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.

It is understood that any specific order or hierarchy of steps in the processes disclosed is an illustration of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged, or that all illustrated steps be performed. Some of the steps may be performed simultaneously. For example, in certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products,

Furthermore, the exemplary methodologies described herein may be implemented by a system including processing circuitry or a computer program product including instructions which, when executed by at least one processor, causes the processor to perform any of the methodology described herein.

In one embodiment of the present disclosure, a computer-implemented method for detecting rig activities during operations at a wellsite includes obtaining, by a computer system, sensor data from rig equipment during weilsite operations; generating time series data from the obtained sensor data; analyzing the generated time series data to identify one or more index points where a trend in the time series data changes; segmenting the time series data into a first set of time segments representing macro activities pertormed during the wellsite operations, based on the one or more identified index points; performing statistical analysis on the time series data within each time segment of the first set of time segments to identify points where statistical properties of the time series data changes; and segmenting each time segment of the first set of time segments into a second set of time segments representing micro activities performed during the weilsite operations, based on the identified points of change in the statistical properties of the corresponding time series data.

In one embodiment of the present disclosure, a system for detecting rig activities during operations at a wellsite includes at least one processor and a memory coupled to the processor having instructions stored therein, which when executed by the processor cause the processor to perform a plurality of functions, including functions to obtain sensor data from rig equipment during wellsite operations; generate time series data from the obtained sensor data; analyze the generated time series data to identify one or more index points where a trend in the time series data changes; segment the time series data into a first set of time segments representing macro activities performed during the wellsite operations, based on the one or more identified index points; perform statistical analysis on the time series data within each time segment of the first set of time segments to identify points where statistical properties of the time series data change; and segment each time segment of the first set of time segments into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties of the corresponding time series data.

In one embodiment of the present disclosure, a computer-readable storage medium having instructions stored therein, which when executed by a computer cause the computer to perform a plurality of functions, including functions to obtain, by a computer system, sensor data from rig equipment during weilsite operations; generate time series data from the obtained sensor data; analyze the generated time series data to identify one or more index points where a trend in the time series data changes; segment the time series data into a first set of time segments representing macro activities performed during the weilsite operations, based on the one or more identified index points; perform statistical analysis on the time series data within each time segment of the first set of time segments to identify points where statistical properties of the time series data change; and segment each time segment of the first set of time segments into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties of the corresponding time series data.

While specific details about the above embodiments have been described, the above hardware and software descriptions are intended merely as example embodiments and are not intended to limit the structure or implementation of the disclosed embodiments. For instance, although many other internal components of the system 1000 are not shown, those of ordinary skill in the art will appreciate that such components and their interconnection are well known.

In addition, certain aspects of the disclosed embodiments, as outlined above, may be embodied in software that is executed using one or more processing units/components. Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Tangible non-transitory “storage” type media include any or all of the memory or other storage for the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives, optical or magnetic disks, and the like, which may provide storage at any time for the software programming.

Additionally, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The above specific example embodiments are not intended to limit the scope of the claims. The example embodiments may be modified by including, excluding, or combining one or more features or functions described in the disclosure.

As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise” and/or “comprising,” when used in this specification and/or the claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The illustrative embodiments described herein are provided to explain the principles of the disclosure and the practical application thereof, and to enable others of ordinary skill in the art to understand that the disclosed embodiments may be modified as desired for a particular implementation or use. The scope of the claims is intended to broadly cover the disclosed embodiments and any such modification.

Claims

1. A computer-implemented method for detecting rig activities during operations at a wellsite, the method comprising:

obtaining, by a computer system, sensor data from rig equipment during wellsite operations;

generating time series data from the obtained sensor data;

to analyzing the generated time series data to identify one or more index points where a trend in the time series data changes;

segmenting the time series data into a first set of time segments representing macro activities performed during the weilsite operations, based on the one or more identified index points;

performing statistical analysis on the time series data within each time segment of the first set of time segments to identify points where statistical properties of the time series data change; and

segmenting each time segment of the first set of time segments into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties of the corresponding time series data.

2. The method of claim 1, wherein generating the time series data from the obtained sensor data comprises using a linear regression to determine a gradient transition of the obtained sensor data over time.

3. The method of claim 2, wherein the one or more identified index points correspond to one or more changes in the gradient transition of the sensor data.

4. The method of claim 2, wherein:

positive slope values of a line representing the gradient transition of the sensor data between two identified index points correspond to a first macro activity of weilsite operations;

negative slope values of the line representing the gradient transition of the sensor data between two identified index points correspond to a second macro activity of wellsite operations; and

flat slope values of the line representing the gradient transition of the sensor data between two identified index points correspond to a third macro activity of wellsite operations.

5. The method of claim 1. wherein the statistical analysis performed on the time series data identifies where the mean or variance of the time series data changes.

6. The method of claim 1, wherein the sensor data obtained comprises bit depth, borehole depth, and hook load values measured by one or more sensors coupled to the rig equipment during the wellsite operations.

7. The method of claim 6, wherein generating the time series data from the obtained sensor data comprises generating a time series curve indicating a difference between respective values of borehole depth and bit depth measured by the one or more sensors during the wellsite operations over a period of time.

8. The method of claim 7. wherein generating the time series data of the obtained sensor data further comprises using a linear regression to determine a gradient for the time series curve with respect to borehole depth and the difference between borehole depth and bit depth.

9. The method of claim 1, wherein the macro activities performed during wellsite operations include trip in, trip out, drilling, and making a connection.

10. The method of claim 9, wherein the micro activities performed during wellsite operations include inslip, pre-connection, and post-connection activities of drilling operations.

11. The method of claim 1, wherein segmenting each time segment of the first set of time segments into a second set of time segments comprises comparing average values of the operation data before and after the identified points of change.

12. A system for detecting rig activities during operations at a wellsite, the system comprising:

at least one processor; and

a memory coupled to the processor having instructions stored therein, which when executed by the processor, cause the processor to perform a plurality of functions, including functions to: obtain sensor data from rig equipment during wellsite operations; generate time series data from the obtained sensor data; analyze the generated time series data to identify one or more index points where a trend in the time series data changes; segment the time series data into a first set of time segments representing macro activities performed during the wellsite operations, based on the one or more identified index points; perform statistical analysis on the time series data within each time segment of the first set of time segments to identify points where statistical properties of the time series data change; and segment each time segment of the first set of time segments into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties of the corresponding time series data.

13. The system of claim 12, wherein the plurality of functions comprises functions to calculate one or more operation efficiency descriptors by comparing the time segment duration of at least one macro activity or micro activity with a best practice target duration.

14. The system of claim 13, wherein the calculated operation efficiency descriptors include invisible lost time (ILT) and non-productive time (NPT).

15. The system of claim 12, wherein generating the time series data from the obtained sensor data comprises using a linear regression to determine a gradient transition of the obtained sensor data over time, wherein the one or more identified index points correspond to one or more changes in the gradient transition of the sensor data.

16. The system of claim 12, wherein:

the macro activities performed during wellsite operations include trip in, trip out, drilling, and making a connection; and

the micro activities performed during wellsite operations includes inslip, pre-connection, and post-connection activities of drilling operations.

17. A computer-readable storage medium having instructions stored therein, which when executed by a computer cause the computer to perform a plurality of functions, including functions to:

obtain, by a computer system, sensor data from rig equipment during wellsite operations;

generate time series data from the obtained sensor data;

analyze the generated time series data to identify one or more index points where a trend in the time series data changes;

segment the time series data into a first set of time segments representing macro activities performed during the wellsite operations, based on the one or more identified index points;

is perform statistical analysis on the time series data within each time segment of the first set of time segments to identify points where statistical properties of the time series data change; and

segment each time segment of the first set of time segments into a second set of time segments representing micro activities performed during the wellsite operations, based on the identified points of change in the statistical properties of the corresponding time series data.

18. The computer-readable storage medium of claim 17, wherein the sensor data obtained comprises bit depth, borehole depth, and hook load values measured by one or more sensors coupled to the rig equipment during the wellsite operations.

19. The computer-readable storage medium of claim 18, wherein performing statistical analysis on the time series data comprises applying a Pruned Exact Linear Time (PELT) algorithm to the time series data within each time segment of the first set of time segments to detect when a hook load value drops.

20. The computer-readable storage medium of claim 19, wherein generating the time series data of the obtained sensor data comprises using a linear regression to determine a gradient for the time series curve with respect to borehole depth and the difference between borehole depth and bit depth.