METHODS OF INTERPRETING A PLURALITY OF TIME-SERIES DATASETS GENERATED FROM OPERATION OF HYDROCARBON WELLS

Methods of facilitating human interpretation of a plurality of time-series datasets generated from operation of hydrocarbon wells. The methods include obtaining the plurality of time-series datasets and displaying a vector map. The plurality of time-series datasets is generated from an operation of the hydrocarbon well and includes a first time-series dataset and a second time-series dataset, and optionally may include a third time-series dataset. The vector map includes a time axis and a plurality of points distributed along the time axis at a plurality of corresponding times. A color of each point of the plurality of points is defined in a plural-component color space and includes a first color component at a first intensity and a second color component at a second color component at a second intensity, and optionally a third color component at a third intensity when the plurality of time-series datasets includes a third time-series dataset.

FIELD OF THE INVENTION

The present disclosure relates generally to methods of facilitating human interpretation of a plurality of time-series datasets and generated from operation of hydrocarbon wells.

BACKGROUND OF THE INVENTION

Drilling, completion, and/or production operations of hydrocarbon wells often may generate large numbers of time-series datasets that may be associated with a number of distinct variables. In general, it may be feasible to view, to interpret, and/or to compare a small number of such time-series datasets. As an example, various plotting, charting, and/or display methodologies are known. These display methodologies may be two-or-more dimensional and may utilize color. However, it may be challenging, or even impossible, to view, to interpret, and/or to compare a large number of such time-series datasets. Thus, there exists a need for improved methods of facilitating human interpretation of a plurality of time-series datasets generated from operation of hydrocarbon wells.

SUMMARY OF THE INVENTION

Methods of facilitating human interpretation of a plurality of time-series datasets and hydrocarbon wells that perform the methods. The methods include obtaining the plurality of time-series datasets and displaying a vector map. The plurality of time-series datasets is generated from an operation of the hydrocarbon well and includes at least a first time-series dataset and a second time-series dataset. The first time-series dataset includes values of a first variable at a plurality of corresponding times and the second time-series dataset includes values of a second variable at the plurality of corresponding times. The plurality of time-series datasets optionally further may include at least a third time-series dataset, and the third time-series dataset, when present, includes values of a third variable at the plurality of corresponding times.

The vector map includes a time axis and a plurality of points distributed along the time axis at a plurality of corresponding times. A color of each point of the plurality of points is defined in a plural-component color space and includes a first color component at a first intensity and a second color component at a second intensity. The first intensity at a given time of the plurality of corresponding times is based upon a magnitude of the first variable at the given time. The second intensity at the given time is based upon a magnitude of the second variable at the given time. When the plurality of time-series datasets includes a third time-series dataset, the color of each point of the plurality of points may include a third color component at a third intensity, with the third intensity at the given time being based upon a magnitude of the third variable at the given time.

DETAILED DESCRIPTION OF THE INVENTION

FIGS.1-9provide examples of hydrocarbon wells30, of methods100, and/or of vector maps, according to the present disclosure. Elements that serve a similar, or at least substantially similar, purpose are labeled with like numbers in each ofFIGS.1-9, and these elements may not be discussed in detail herein with reference to each ofFIGS.1-9. Similarly, all elements may not be labeled in each ofFIGS.1-9, but reference numerals associated therewith may be utilized herein for consistency. Elements, components, and/or features that are discussed herein with reference to one or more ofFIGS.1-9may be included in and/or utilized with any ofFIGS.1-9without departing from the scope of the present disclosure. In general, elements that are likely to be included in a particular embodiment are illustrated in solid lines, while elements that are optional are illustrated in dashed lines. However, elements that are shown in solid lines may not be essential and, in some embodiments, may be omitted without departing from the scope of the present disclosure.

FIG.1is a schematic illustration of examples of a hydrocarbon well30that may perform methods100and/or that may be utilized to generate time-series datasets, according to the present disclosure. Hydrocarbon well30includes a wellbore36that extends within a subsurface region20. Wellbore36also may be referred to herein as extending between a surface region10and a subterranean formation22, which may include hydrocarbons24. Hydrocarbon well30may include a wellhead32that may include a pressure sensor34. Hydrocarbon well30also may include a slurry supply system40, which may be configured to provide a slurry stream42to wellbore36at a slurry stream flow rate, which may be measured and/or determined by a slurry flow rate sensor44.

Hydrocarbon well30also includes a display60and a computing device70. Display60may, or may be utilized to, display vector maps according to methods100ofFIG.4. Computing device70may be programmed to direct display60to display the vector maps. Computing device70may include and/or be any suitable structure, device, and/or devices that may be adapted, configured, designed, constructed, and/or programmed to perform the functions discussed herein. This may include controlling the operation of the at least one other component of hydrocarbon well30, such as via and/or utilizing methods100, which are discussed in more detail herein. As examples, computing device70may include one or more of a controller, an electronic controller, a dedicated controller, a special-purpose controller, a personal computer, a special-purpose computer, a display device, a touch screen display, a logic device, a memory device, and/or a memory device having computer-readable storage media72.

The computer-readable storage media, when present, also may be referred to herein as non-transitory computer-readable storage media. This non-transitory computer-readable storage media may include, define, house, and/or store computer-executable instructions, programs, and/or code; and these computer-executable instructions may direct hydrocarbon well30and/or computing device70thereof to perform any suitable portion, or subset, of methods100. Examples of such non-transitory computer-readable storage media include CD-ROMs, disks, hard drives, flash memory, etc. As used herein, storage, or memory, devices and/or media having computer-executable instructions, as well as computer-implemented methods and other methods according to the present disclosure, are considered to be within the scope of subject matter deemed patentable in accordance with Section 101 of Title 35 of the United States Code.

A plurality of different and/or distinct operations may be performed on and/or utilizing hydrocarbon well30. As an example, a drilling operation may be utilized to form, to define, and/or to drill wellbore36. As another example, a plurality of completion operations may be utilized to form and/or define a plurality of fractures38. Fractures38may extend from wellbore36and/or within subsurface region20. In a specific example, the plurality of completion operations may be performed in a plurality of stages80, with each stage80being utilized to define one or more fractures38. In some such examples, slurry supply system40may be utilized to provide slurry stream42to wellbore36, such as to pressurize the wellbore and thereby generate fractures38. The slurry stream may be provided at a pressure, which may be measured by pressure sensor34. The slurry stream may include a proppant at a known and/or specified proppant concentration.

Subsequent to completion of hydrocarbon well30, production operations may be performed. Production operations may include flowing hydrocarbons24from subterranean formation22and/or into wellbore36. The hydrocarbons then may flow to surface region10and be produced from the hydrocarbon well as a produced fluid stream50.

As discussed, operations performed on a hydrocarbon well30may generate large numbers of time-series datasets, andFIG.2is an illustration of examples of a plurality of individual time-series datasets that may be generated by one or more hydrocarbon wells30and/or that may be utilized with methods100, according to the present disclosure. In the examples ofFIG.2, each individual time-series dataset (labelled 1-40) is generated during completion of a corresponding stage80of hydrocarbon well30, such as is schematically illustrated inFIG.1. Each time-series dataset plots values of a first variable, a second variable, and a third variable on the ordinate, or Y, axis as a function of time, on the abscissa, or X, axis. In the examples ofFIG.2, the red variable (R) denotes the slurry stream flow rate of the slurry stream that is provided to the wellbore, the green variable (G) denotes the pressure within the wellbore, and the blue variable (B) denotes the proppant concentration within the slurry stream.

It generally may be possible, or feasible, to manually and methodically compare individual time-series datasets, such as those that are illustrated inFIG.2. As an example, a first time-series dataset for a first completion operation may be visually compared to a second time-series dataset for second completion operation. However, it becomes challenging to manually compare more than a small number of individual time-series datasets. It also is challenging, if not impossible, to manually correlate and/or identify trends and/or data interrelations among more than the small number of individual time-series datasets.

In some instances, overlay plots may be utilized to aid in comparisons among individual time-series datasets. While such overlay plots may be effective for a small number of individual time-series datasets, they become ineffective for a large number of individual time-series datasets. This is illustrated byFIG.3, which is an illustration of an example of an overlay of the plurality of individual time-series datasets ofFIG.2. It becomes clear that it is impossible to identify any significant trends, correlations, and/or comparisons from the overlay illustrated inFIG.2.

With the above in mind,FIG.4is a flowchart depicting examples of methods100of facilitating human interpretation of a combined plurality of time-series datasets, such as the plurality of time-series datasets illustrated inFIG.2, according to the present disclosure. Methods100may include performing a completion operation at105, producing a fluid stream from a hydrocarbon well at110, and/or generating a plurality of time-series datasets at115. Methods100include obtaining the plurality of time-series datasets at120and may include scaling the plurality of time-series datasets at125and/or mapping the plurality of time-series datasets at130. Methods100also include displaying a vector map at135and may include repeating at least a portion of the methods at140, interpreting the vector map at145, and/or making an operational change at150.

The plurality of time-series datasets may be generated from and/or during an operation of the hydrocarbon well. Examples of the operation of the hydrocarbon well include a completion operation and/or a production operation and are discussed in more detail herein. The plurality of time-series datasets include at least a first time-series dataset and a second time-series dataset. In some examples, the plurality of time-series datasets further may include a third time-series dataset, or even more than three time-series datasets. The first time-series dataset may include values of a first variable at a plurality of corresponding times. The second time-series dataset may include values of a second variable at the plurality of corresponding times, and the third time-series dataset, when present, may include values of a third variable at the plurality of corresponding times.

For simplicity of discussion and illustration, the following will refer to methods100that utilizing the plurality of time-series datasets including the first time-series dataset, the second time-series dataset, and the third time-series dataset. However, and as discussed, it is within the scope of the present disclosure that methods100may be performed utilizing fewer than three time-series datasets, such as only the first time-series dataset and the second time-series dataset, and/or utilizing more than three time-series datasets, such as four time-series datasets or even more than four time-series datasets.

In some examples, the plurality of corresponding times may include and/or be a plurality of discrete corresponding times. In some such examples, the first time-series dataset, the second time-series dataset, and the third time-series dataset may include corresponding values of the first variable, the second variable, and the third variable, respectively, for at least a subset, or even for each, of the plurality of discrete corresponding times. Stated another way, values of the first variable, the second variable, and the third variable each may be collected and/or defined at the plurality of discrete corresponding times. Stated yet another way, each discrete corresponding time may have a single value of the first variable, a single value of the second variable, and a single value of the third variable associated therewith.

In some examples, the plurality of corresponding times may include and/or be a plurality of corresponding time ranges. In some such examples, the first time-series dataset, the second time-series dataset, and the third time-series dataset may include corresponding values of the first variable, the second variable, and the third variable, respectively, for and/or within at least a subset, or even each, of the plurality of corresponding time ranges. Stated another way, values of the first variable, the second variable, and the third variable each may be collected, defined, and/or associated with corresponding time ranges and/or each corresponding time range may have a single value of the first variable, a single value of the second variable, and a single value of the third variable associated therewith.

The first variable, the second variable, and/or the third variable may include and/or be any suitable variable, dependent variable, and/or independent variable that may be associated with and/or generated by the operation of the hydrocarbon well. Examples of the first variable, the second variable, and/or the third variable include one or more of a slurry flow rate of a slurry stream provided to the hydrocarbon well during a completion operation of the hydrocarbon well, a proppant concentration of a proppant in the slurry stream during the completion operation of the hydrocarbon well, a pressure generated within the wellbore when the slurry stream is provided to the hydrocarbon well during the completion operation of the hydrocarbon well, a flow resistance of the hydrocarbon well during the completion operation of the hydrocarbon well, a water production rate during production from the hydrocarbon well, a liquid hydrocarbon production rate during production from the hydrocarbon well, a gaseous hydrocarbon production rate during production from the hydrocarbon well, total production from the hydrocarbon well, and/or hydrocarbon production from the hydrocarbon well. Several of these examples of variables are discussed in more detail herein for context and illustration.

Performing the completion operation at105may include performing any suitable completion operation of and/or on the hydrocarbon well. This may include performing the completion operation to permit and/or facilitate the generating at115, when performed. Examples of the completion operation include any suitable stimulation operation and/or hydraulic fracturing operation. In a specific example, the completion operation may include providing a slurry stream to the hydrocarbon well, such as with, via, and/or utilizing slurry supply system40ofFIG.1.

The performing at105may be performed with any suitable timing and/or sequence during methods100. As examples, the performing at105may be performed prior to the producing at110, prior to and/or at least partially concurrently with the generating at115, prior to and/or at least partially concurrently with the obtaining at120, prior to and/or at least partially concurrently with the scaling at125, prior to and/or at least partially concurrently with the mapping at130, prior to and/or at least partially concurrently with the displaying at135, prior to and/or at least partially concurrently with the repeating at140, prior to and/or at least partially concurrently with the interpreting at145, and/or prior to and/or at least partially concurrently with the making at150.

Producing the fluid stream from the hydrocarbon well at110may include producing any suitable produced fluid stream from the hydrocarbon well. This may include flowing hydrocarbons from a subterranean formation and/or into a wellbore of the hydrocarbon well. Additionally or alternatively, the producing at110may include flowing the hydrocarbons to the surface region via the wellbore.

The producing at110may be performed with any suitable timing and/or sequence during methods100. As examples, the producing at110may be performed subsequent to the performing at105, prior to and/or at least partially concurrently with the generating at115, prior to and/or at least partially concurrently with the obtaining at120, prior to and/or at least partially concurrently with the scaling at125, prior to and/or at least partially concurrently with the mapping at130, prior to and/or at least partially concurrently with the displaying at135, prior to and/or at least partially concurrently with the repeating at140, prior to and/or at least partially concurrently with the interpreting at145, and/or prior to and/or at least partially concurrently with the making at150.

Generating the plurality of time-series datasets at115may include generating the plurality of time-series datasets in any suitable manner. As an example, the generating at115may include generating the plurality of time-series datasets during, as a result of, and/or responsive to the performing at105and/or the producing at110.

As a more specific example, and such as when methods100include the performing at105, the generating at115may include generating the plurality of time-series datasets during the completion operation of the hydrocarbon well. In some such examples, and when the performing at105includes providing the slurry stream to the hydrocarbon well, the generating at115may include monitoring and/or recording a slurry flow rate of the slurry stream, a proppant concentration within the slurry stream, and/or a pressure generated within the wellbore as a function of time during the providing the slurry stream. In some such examples, the first variable may include and/or be the slurry flow rate, the second variable may include and/or be the proppant concentration, and the third variable may include and/or be the pressure.

As another more specific example, and such as when methods100include the producing at110, the generating at115may include generating the plurality of time-series datasets during production from the hydrocarbon well. In some such examples, the generating at115may include monitoring and/or recording a water production rate during production from the hydrocarbon well, a liquid hydrocarbon production rate during production from the hydrocarbon well, and/or a gaseous hydrocarbon production rate during production from the hydrocarbon well. In some such examples, the first variable may include and/or be the water production rate, the second variable may include and/or be the liquid hydrocarbon production rate, and the third variable may include and/or be the gaseous hydrocarbon production rate.

The generating at115may be performed with any suitable timing and/or sequence during methods100. As examples, the generating at115may be performed at least partially concurrently with and/or as a result of the performing at105, at least partially concurrently with and/or as a result of the producing at110, prior to and/or at least partially concurrently with the obtaining at120, prior to and/or at least partially concurrently with the scaling at125, prior to and/or at least partially concurrently with the mapping at130, prior to and/or at least partially concurrently with the displaying at135, prior to and/or at least partially concurrently with the repeating at140, prior to and/or at least partially concurrently with the interpreting at145, and/or prior to and/or at least partially concurrently with the making at150.

Obtaining the plurality of time-series datasets at120may include obtaining the plurality of time-series datasets in any suitable manner. As an example, the obtaining at120may be responsive to and/or a result of the performing at105, the producing at110, and/or the generating at115. As another example, the obtaining at120may include accessing and/or downloading the plurality of time-series datasets, such as from a repository of, or from a database that includes, the plurality of time-series datasets.

Scaling the plurality of time-series datasets at125may include scaling the plurality of time-series datasets, or each time-series dataset of the plurality of time-series datasets, to produce and/or generate a plurality of scaled time-series datasets. The scaling at125may include scaling the plurality of time-series datasets in any suitable manner. As an example, the scaling at125may include scaling the plurality of time-series datasets such that values of the corresponding variable of each time-series dataset range are between a minimum scale value and a maximum scale value. Stated another way, the scaling at125may include scaling the plurality of time-series datasets such that values within each dataset fall within a predetermined, a predefined, and/or a similar value range. Stated yet another way, the scaling at125may include normalizing the plurality of time-series datasets. The scaling at125additionally or alternatively may include scaling such that a minimum value, or a minimum variable value, of each scaled time-series dataset is the minimum variable scale value, such as 0, and/or such that a maximum value, or a maximum variable value, of each scaled time-series dataset is the maximum variable scale value, such as 1.

In some examples, the scaling at125may include linearly scaling the plurality of time-series datasets to produce and/or generate the plurality of scaled time-series datasets. In a specific example, the scaling at125may include scaling the plurality of time-series datasets according to Equation (1).

where R(s) is the value of a given scaled time-series dataset of the plurality of time-series datasets at the given time, s is the value of a corresponding time-series dataset of the plurality of time-series datasets at the given time, shighis a high truncation value for the corresponding time-series dataset, and slowis a low truncation value for the corresponding time-series dataset.

In another specific example, the scaling at125may include performing the mapping to map the plurality of time-series datasets, or the corresponding variable values thereof, to a corresponding plurality of color component intensity values. Stated another way, the scaling at125may include assigning a corresponding color component intensity value to each corresponding variable value, or range of corresponding variable values, for each time-series dataset.

In some examples, the scaling at125additionally or alternatively may include filtering at least one outlier value from at least one time-series dataset and/or scaling to decrease a magnitude of the outlier value. As an example, the max function of Equation 1 assigns a value of shighto outlier values that are greater than shighand/or assigns a value of slowto outlier values that are less than slow. As another example, the plurality of time-series datasets may be mapped to a sigmoid function, or any other suitable function, to filter at least one outlier value and/or to decrease the magnitude of the outlier value.

The scaling at125may be performed with any suitable timing and/or sequence during methods100. As examples, the scaling at125may be performed subsequent to and/or at least partially concurrently with the performing at105, subsequent to and/or at least partially concurrently with the producing at110, subsequent to and/or at least partially concurrently with the generating at115, subsequent to and/or at least partially concurrently with the obtaining at120, prior to and/or at least partially concurrently with the mapping at130, prior to and/or at least partially concurrently with the displaying at135, prior to and/or at least partially concurrently with the repeating at140, prior to and/or at least partially concurrently with the interpreting at145, and/or prior to and/or at least partially concurrently with the making at150.

Mapping the plurality of time-series datasets at130may include mapping each time-series dataset of the plurality of time-series datasets to the corresponding plurality of color component intensity values. In some examples, the mapping at130may include mapping a raw, an original, and/or an unscaled time-series dataset to the corresponding plurality of color component intensity values. Additionally or alternatively, and when methods100include the scaling at125, the mapping at130may include mapping the plurality of scaled time-series datasets to the corresponding plurality of color component intensity values.

The plurality of color component intensity values may range between a minimum color component value and a maximum color component value. An example of the corresponding plurality of color component intensity values, which may be utilized with common color spaces, include integer values from 0 to 255 (e.g., 8-bit color depth), with 0 representing the minimum color component value and 255 representing the maximum color component value. However, other color component intensity values and/or ranges also are within the scope of the present disclosure, such as a greater or lower number of color component intensity values and/or ranges.

In a specific example, the mapping at130may include sequentially assigning and/or mapping a range of values for each corresponding variable of the plurality of time-series datasets and/or for each corresponding variable of the plurality of scaled time-series datasets to a corresponding value of the corresponding plurality of color component intensity values. As a simplified example, the plurality of color component intensity values may include integer values from 0 to 9 and the range of values for each corresponding variable may range from 0 to 1. In such an example, the mapping at130may include assigning a color component intensity value of 0 to values to each corresponding variable with a value from 0 to 0.1, assigning a color component intensity value of 1 to each corresponding variable with a value from 0.1 to 0.2, etc.

In another specific example, the first color component may have a corresponding plurality of discrete first color component intensity values, the second color component may have a corresponding plurality of discrete second color component intensity values, and the third color component may have a corresponding plurality of discrete third color component intensity values. In this example, the mapping at130may include assigning a corresponding discrete first color component intensity value to the first variable at each time of the plurality of corresponding times, assigning a corresponding discrete second color component intensity value to the second variable at each time of the plurality of corresponding times, and/or assigning a corresponding discrete third color component intensity value to the third variable at each time of the plurality of corresponding times.

It is within the scope of the present disclosure that the mapping at130may include mapping the plurality of time-series datasets to any suitable corresponding plurality of color component intensity values that may be utilized with any suitable color space, or plural-component color space. An example of the color space includes a two-component color space, such as a black-and-white color space. Additional examples of the color space include a three-component color space, such as an RGB (red-green-blue) three-component color space, a YUV (luma-blue projection-red projection) three-component color space, and/or a CMY (cyan-magenta-yellow) three-component color space. Further examples of the color space include a four-component color space and/or a color space with more than four components.

An example of an RGB three-component color space is illustrated inFIG.5. This RGB three-component color space is utilized herein for consistency; however, it is understood that other color spaces, including those that are disclosed herein, also may be utilized without departing from the scope of the present disclosure.

The mapping at130may be performed with any suitable timing and/or sequence during methods100. As examples, the mapping at130may be performed subsequent to and/or at least partially concurrently with the performing at105, subsequent to and/or at least partially concurrently with the producing at110, subsequent to and/or at least partially concurrently with the generating at115, subsequent to and/or at least partially concurrently with the obtaining at120, subsequent to and/or at least partially concurrently with the scaling at125, prior to and/or at least partially concurrently with the displaying at135, prior to and/or at least partially concurrently with the repeating at140, prior to and/or at least partially concurrently with the interpreting at145, and/or prior to and/or at least partially concurrently with the making at150.

Displaying the vector map at135may include displaying any suitable vector map on any suitable display. The vector map includes a time axis and a plurality of points distributed along the time axis at a plurality of corresponding times. A color of each point of the plurality of points is defined in a plural-component color space and includes a first color component at a first intensity, a second color component at a second intensity, and a third color component at a third intensity. The first intensity at a given time of the plurality of corresponding times is based, at least in part, upon a magnitude of the first variable at the given time. Similarly, the second intensity at the given time of the plurality of corresponding times is based, at least in part, upon a magnitude of the second variable at the given time. In addition, the third intensity at the given time is based, at least in part, upon a magnitude of the third variable at the given time. When methods100include the scaling at125, the first intensity, the second intensity, and the third intensity may be based upon the plurality of scaled time-series datasets. When methods100include the mapping at130, the first intensity, the second intensity, and the third intensity may be based upon the corresponding plurality of color component intensity values.

It is within the scope of the present disclosure that the displaying at135additionally or alternatively may include generating image file data that may be representative of the vector map. This may permit and/or facilitate displaying the vector map at a later point in time, transferring the vector map, storing the vector map, and/or displaying the vector map on a plurality of different displays. Examples of the image file data include conventional image file formats, such as Joint Photographic Experts Group (JPEG), Graphics Interchange Format (GIF), Tagged Image File Format (TIFF), and/or Portable Network Graphics (PNG).FIG.6is an illustration of an example of a plot90of a plurality of time-series datasets and a corresponding vector map92that may be generated utilizing methods100and/or that may be displayed during the displaying at135. More specifically, plot90ofFIG.6illustrates slurry flow rate as a function of time in red, pressure as a function of time in green, and proppant concentration as a function of time in blue. For consistency and ease of illustration, the same colors are assigned to these variables in plot90, the corresponding vector map92, and the other vector maps92that are illustrated and/or discussed herein. In the vector map, and as discussed, the intensity of each color is based upon the magnitude of the corresponding variable, with the intensity increasing with an increase in the magnitude of the corresponding variable. The various colors of the vector map at a given point in time correspond to various combinations of the colors, and corresponding intensities, of the three time-series datasets at the given point in time. As such, the vector map provides a simplified visual representation of the values of the three time-series datasets and/or of the relative magnitudes thereof.

As illustrated inFIG.6, the plurality of points in the vector map may include and/or be a plurality of rectangular and/or square points, although other shapes or indicia may be utilized without departing from the scope of the present disclosure. As also illustrated inFIG.6, the plurality of points may be adjacent one another and/or may extend along the time axis, such as to form and/or define a colored bar, which is an example of vector maps according to the present disclosure.

The displaying at135may be performed with any suitable timing and/or sequence during methods100. As examples, the displaying at135may be performed subsequent to and/or at least partially concurrently with the performing at105, subsequent to and/or at least partially concurrently with the producing at110, subsequent to and/or at least partially concurrently with the generating at115, subsequent to and/or at least partially concurrently with the obtaining at120, subsequent to and/or at least partially concurrently with the scaling at125, subsequent to and/or at least partially concurrently with the mapping at130, prior to and/or at least partially concurrently with the repeating at140, prior to and/or at least partially concurrently with the interpreting at145, and/or prior to and/or at least partially concurrently with the making at150.

Repeating at least the portion of the methods at140may include repeating any suitable step and/or steps of methods100in any suitable order, for any suitable purpose, and/or any suitable number of times. As examples, the repeating at140may include repeating the performing at105, the producing at110, the generating at115, the obtaining at120, the scaling at125, the mapping at130, and/or the displaying at 135 at least 2, at least 4, at least 6, at least 8, at least 10, at least 15, at least 20, at most 50, at most 40, at most 30, at most 20, at most 15, and/or at most 10 times.

In some examples, the operation may include and/or be a first instance of the operation, and the plurality of time-series datasets may be a first plurality of time-series datasets generated during and/or associated with the first instance of the operation. In such an example, the vector map may include and/or be a first vector map, and the repeating at140may include repeating at least the obtaining at120and the displaying at135a plurality of times to concurrently display a plurality of distinct vector maps, with each vector map of the plurality of distinct vector maps being derived from a corresponding distinct instance of the operation. This is illustrated inFIG.7, which is an illustration of an example of a plurality of vector maps that may be generated by methods100. The example ofFIG.7includes 40 distinct vector maps and includes the vector map ofFIG.6, which is indicated by the red arrows.

As a more specific example, and as discussed, a completion operation on a given hydrocarbon well may include completing a plurality of distinct stages of the hydrocarbon well. With this in mind, the repeating at140may include displaying a plurality of distinct vector maps that includes a corresponding vector map for each stage of the completion operation, as illustrated by the 40 distinct vector maps ofFIG.7.

When methods100include the repeating at140, repetition of the displaying at135may include arranging the plurality of distinct vector maps such that the plurality of distinct vector maps extends along a single time axis (i.e., the abscissa, or X, axis ofFIG.7) and/or stacking, or vertically stacking, the plurality of distinct vector maps. Such a combining methodology may permit and/or facilitate relative comparisons among the plurality of distinct instances of the operation. This may include comparisons of the durations of the plurality of distinct instances of the operation and/or comparisons of various trends with respect to the plurality of time-series datasets during the plurality of distinct instances of the operation. The results of the combining methodology facilitates viewing the data sets at a selected simultaneous arrangement.

In some examples, and when methods100include the repeating at140, each operation of the plurality of distinct instances of the operation may include an operational feature. In some such examples, the repetition of the displaying at135may include aligning the plurality of distinct vector maps such that the operational feature of each operation occurs at the same time along the time axis. Such a configuration may permit and/or facilitate relative comparisons among the plurality of distinct vector maps by aligning the plurality of distinct vector maps with respect to the operational feature. InFIG.7, a shut-in time of the completion operations utilized to generate vector maps92are aligned along the time axis.

In some examples, the repeating at140additionally or alternatively may include repeating at least the obtaining at120and the displaying at135to display a desired, a theoretical, and/or a planned vector map for the operation of the hydrocarbon well. Stated another way, and prior to performing the operation, values of variables included in the plurality of time-series datasets may be estimated and/or initial values may be established. With this in mind, displaying the desired, theoretical, and/or planned vector map may permit and/or facilitate comparisons between ideal and/or predicted values of the plurality of time-series datasets and actual values of the plurality of time-series datasets obtained while performing the operation of the hydrocarbon well.

In some examples, the repeating at140additionally or alternatively may include repeating at least the obtaining at120and the displaying at135to display a deviation, or a difference, vector map that illustrates differences between the desired, theoretical, and/or planned vector map and the vector map generated from the plurality of time-series datasets obtained while performing the operation of the hydrocarbon well. Such a configuration may permit and/or facilitate identification of operational regimes that differ from the desired, theoretical, and/or planned operational regimes.

In some examples, methods100, the displaying at135, and/or repetition of the displaying at135may include displaying at least one additional parameter, which may be associated with each of the plurality of distinct instances of the operation of the hydrocarbon well. The at least one additional parameter may be displayed as part of and/or may be associated with each vector map of the plurality of distinct vector maps.

An example of the at least one additional parameter includes post shut-in data. In a specific example, the at least one additional parameter may include and/or be a production rate associated with each of the plurality of distinct instances of the operation of the hydrocarbon well. This example is illustrated inFIG.8, which illustrates post shut-in data96to the right of shut-in time94. In the specific example ofFIG.8, vector maps92correspond to various completion stages of a hydrocarbon well, and post shut-in data96corresponds to production rates obtained from the various completion stages of the hydrocarbon well. Such methods may permit and/or facilitate identification of trends in post shut-in data96, such as production rates, that may be caused and/or obtained by, for example, variations in slurry flow rate, pressure, and/or proppant concentration during completion of the various stages of the hydrocarbon well and/or that may vary with position within the hydrocarbon well.

In some examples, the hydrocarbon well may include and/or be a first hydrocarbon well. In some such examples, the operation may be a first operation of the first hydrocarbon well, the plurality of time-series datasets may include and/or be a first plurality of time-series datasets generated during the first operation of the first hydrocarbon well, and/or the vector map may include and/or be a first vector map. In such examples, the repeating at140may include repeating at least the displaying at135a plurality of times to concurrently display a plurality of distinct vector maps for a plurality of distinct instances of the operation of a plurality of distinct hydrocarbon wells. Stated another way, the repeating at140may include repeating the displaying at135to display a corresponding vector map for each hydrocarbon well of the plurality of distinct hydrocarbon wells. Such methods may permit and/or facilitate comparison of operational parameters among the plurality of distinct hydrocarbon wells.

This is illustrated inFIG.9, which illustrates a plurality of vector maps obtained during completion operations of a plurality of distinct hydrocarbon wells, which are indicated as well1, well2, well3, well4, well5, and well6. The visualization illustrated inFIG.9may provide distinct benefits that may permit an operator of the hydrocarbon well to visually identify trends that otherwise would be difficult, or even impossible, to identify.

As an example, and as indicated by the circle labeled (1), well2utilized relatively higher pressures (more green in the vector maps) for the first few (deeper) completion stages and relatively lower pressures thereafter. It may be possible to correlate these higher pressures to formation geology and/or obtained production ranges.

As another example, and as indicated by the circle labeled (2), well3has a very short completion stage. It is possible that the data associated with this completion stage are corrupt and/or that there was an issue with this completion stage. As such, it may be appropriate to consider excluding this completion stage from subsequent analyses.

As another example, and as indicated by the circle labeled (3), well6had relatively higher slurry flow rates for several of the early completion stages. It may be possible to correlate these higher slurry flow rates to changes in production rates and/or completion operation duration.

In some examples, methods100, the displaying at135, and/or repetition of the displaying at135further may include displaying overall well data, such as an overall production rate from the hydrocarbon well. This is illustrated inFIG.8at98. Such methods may permit and/or facilitate comparisons of production rates among a plurality of distinct hydrocarbon wells.

The repeating at140may be performed with any suitable timing and/or sequence during methods100. As examples, the repeating at140may be performed subsequent to and/or at least partially concurrently with the performing at105, subsequent to and/or at least partially concurrently with the producing at110, subsequent to and/or at least partially concurrently with the generating at115, subsequent to and/or at least partially concurrently with the obtaining at120, subsequent to and/or at least partially concurrently with the scaling at125, subsequent to and/or at least partially concurrently with the mapping at130, subsequent to and/or at least partially concurrently with the displaying at135, subsequent to, prior to, and/or at least partially concurrently with the interpreting at145, and/or subsequent to, prior to, and/or at least partially concurrently with the making at150.

Interpreting the vector map at145may include interpreting the vector map to determine at least one property of the operation. Examples of the at least one property of the operation include a completion efficiency of the hydrocarbon well, a stage-to-stage completion efficiency of the hydrocarbon well, a vendor-to-vendor completion efficiency of the hydrocarbon well, a production rate from the hydrocarbon well, and/or a cost effectiveness of the hydrocarbon well. Methods100and/or the interpreting at145additionally or alternatively may include correlating the vector map with a geology of a subterranean formation within which the hydrocarbon well extends.

The interpreting at145may be performed with any suitable timing and/or sequence during methods100. As examples, the interpreting at145may be performed subsequent to and/or at least partially concurrently with the performing at105, subsequent to and/or at least partially concurrently with the producing at110, subsequent to and/or at least partially concurrently with the generating at115, subsequent to and/or at least partially concurrently with the obtaining at120, subsequent to and/or at least partially concurrently with the scaling at125, subsequent to and/or at least partially concurrently with the mapping at130, subsequent to and/or at least partially concurrently with the displaying at135, subsequent to, prior to, and/or at least partially concurrently with the repeating at140, and/or subsequent to, prior to, and/or at least partially concurrently with the making at150.

Making the operational change at150may include making any suitable operational change based, at least in part, on the vector map. Examples of the at least one operational change include a change in at least one parameter of a subsequent operation performed on the hydrocarbon well and/or a change in at least one parameter of a subsequent operation performed on a different hydrocarbon well.

The making at150may be performed with any suitable timing and/or sequence during methods100. As examples, the making at150may be performed subsequent to and/or at least partially concurrently with the performing at105, subsequent to and/or at least partially concurrently with the producing at110, subsequent to and/or at least partially concurrently with the generating at115, subsequent to and/or at least partially concurrently with the obtaining at120, subsequent to and/or at least partially concurrently with the scaling at125, subsequent to and/or at least partially concurrently with the mapping at130, subsequent to and/or at least partially concurrently with the displaying at135, subsequent to, prior to, and/or at least partially concurrently with the repeating at140, and/or subsequent to, prior to, responsive to, and/or at least partially concurrently with the interpreting at145.

In the present disclosure, several of the illustrative, non-exclusive examples have been discussed and/or presented in the context of flow diagrams, or flow charts, in which the methods are shown and described as a series of blocks, or steps. Unless specifically set forth in the accompanying description, it is within the scope of the present disclosure that the order of the blocks may vary from the illustrated order in the flow diagram, including with two or more of the blocks (or steps) occurring in a different order and/or concurrently. It is also within the scope of the present disclosure that the blocks, or steps, may be implemented as logic, which also may be described as implementing the blocks, or steps, as logics. In some applications, the blocks, or steps, may represent expressions and/or actions to be performed by functionally equivalent circuits or other logic devices. The illustrated blocks may, but are not required to, represent executable instructions that cause a computer, processor, and/or other logic device to respond, to perform an action, to change states, to generate an output or display, and/or to make decisions.

INDUSTRIAL APPLICABILITY

The systems and methods disclosed herein are applicable to the hydrocarbon well drilling, hydrocarbon well completion, and hydrocarbon production industries.