US20190153840A1

US20190153840A1 - Systems and methods for detecting and alerting anomalous well completion conditions

Info

Publication number: US20190153840A1
Application number: US15/819,064
Authority: US
Inventors: Jonathan Michael Bacon; Kevin I. Sutherland; Kyle R. Sneed
Original assignee: Chevron USA Inc
Current assignee: Chevron USA Inc
Priority date: 2017-11-21
Filing date: 2017-11-21
Publication date: 2019-05-23

Abstract

A system and method acquire raw data from fracturing equipment that includes at least two fracturing related parameters measured in real time during a completions operation and conditioning the raw data by removing outlier data values and/or filtering out noise. The conditioned data is processed to generate output values and generating an alarm based on the output values to indicate an abnormal fracturing condition. Processing the conditioned data includes determining incremental changes in data values and whether the incremental changes are an increase, decrease or no change for each of the at least two fracturing related parameters. The incremental changes are summed over a user-defined time period for each of the at least two fracturing related parameters and compared to a threshold for the user-defined time period for each of the at least two fracturing related parameters.

Description

FIELD OF THE INVENTION

The field of the invention relates to systems and methods for detecting and alerting anomalous or abnormal well completion conditions in wells in subterranean formations. The field of the invention further relates to systems and methods for detecting and alerting anomalous conditions in well fracturing.

BACKGROUND

During completing of wells in unconventional reservoirs such as shales and tight sands, hydraulically-induced fractures are connected with the wellbore. Fracturing requires careful monitoring of various parameters to ensure the completions operation is successful. Operators of fracturing equipment analyze and interpret multiple data streams in real time and must make time critical decisions based on that analysis. In many cases, an anomalous or abnormal event (also referred to as an anomaly) will occur that requires an immediate corrective action. A condition referred to in the oil and gas industry as a “screen out” is an example of an anomalous event in which fluid flow is undesirably restricted during pumping of fracturing fluid. One cause of a screen out is near-wellbore fluid loss resulting in the dehydration of fracturing solids carried in the fracturing fluid, e.g. proppant, and consequently blocking the flow path of the fluid.
In most existing systems, fracturing operations are monitored at the well site by the fracturing engineer and well site managers. In some existing systems, real-time supervisors will also monitor the fracturing operations at an offsite location. At the fracturing site, engineers and operations personnel monitor one operation at a time. At the offsite location, a real-time supervisor may monitor up to three or four operations at a time, each of which follow a different operational procedure. Because of this, the real-time supervisor must monitor multiple displays to ensure all operations are being performed efficiently and without anomalous events.
When real-time supervisors or other personnel detect an anomaly that requires a corrective action, the supervisor will consult with the fracturing site personnel on the appropriate response. The response time to detect and correct anomalous events is critical, and while the real-time supervisor and fracturing site personnel may be highly experienced with these operations, the numerous amounts of data sets and variables analyzed can often lead to anomalous events going undetected. Furthermore, some patterns take time to become apparent to the fracturing site or off-site personnel, leaving little time to respond and implement a corrective action, potentially resulting in a serious adverse condition.
Processes and systems to more easily detect such anomalous events would be desirable.

SUMMARY

In one aspect, a method of monitoring a completions operation in which fluid communication is established between a formation and a wellbore is provided. The method includes acquiring data from a data server that receives the data from sensors associated with fracturing equipment used to inject fluid into a formation via the wellbore in a completions operation. The data include at least one fracturing related parameter measured in real time during the completions operation. A data processor processes the data to generate output values for use in alarm generation. The data processing includes determining incremental changes in data values of the data and whether the incremental changes are an increase, decrease or no change for each of the at least one fracturing related parameter; summing the incremental changes over a user-defined time period for each of the fracturing related parameters; determining and indicating whether the sum of the incremental changes exceeds a threshold for the user-defined time period for each of the fracturing related parameters; and determining and indicating as the output values whether the result of the determining and indicating is associated with an abnormal fracturing condition as defined by the user. An alarm generator generates an alarm based on the output values to indicate the abnormal fracturing condition.
In another aspect, a system for monitoring a completions operation in which fluid communication is established between a formation and a wellbore is provided. The system includes a source of data received from sensors associated with fracturing equipment used to inject fluid into a formation via the wellbore in a completions operation wherein the data includes at least one fracturing related parameter measured in real time by sensors during the completions operation; a data processor for processing the data to generate output values for use in alarm generation; and an alarm generator to generate an alarm based on the output values to indicate an abnormal fracturing condition.
These and other aspects, objects, features, and embodiments will be apparent from the following description and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a method of monitoring fracturing conditions according to an example embodiment;

FIG. 2 illustrates a block diagram of a system for monitoring fracturing conditions according to an example embodiment;

FIG. 3 illustrates details of the system of FIG. 2 according to an example embodiment;

FIG. 4 illustrates a plot of output values of the data conditioner of FIG. 3 according to an example embodiment;

FIG. 5 illustrates a plot of output values of the data processor of FIG. 3 according to an example embodiment;

FIGS. 6A-6C illustrate a flow chart showing signal processing logic used by the data processor of FIGS. 2 and 3; and

FIG. 7 illustrates a flow chart showing generation of a screenout alarm based on outputs of the data processor of FIGS. 2 and 3 and user provided inputs.

The drawings illustrate only example embodiments and are therefore not to be considered limiting in scope. The elements and features shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the example embodiments. Additionally, certain dimensions or placements may be exaggerated to help visually convey such principles. In the drawings, reference numerals designate like or corresponding, but not necessarily identical, elements.

DETAILED DESCRIPTION

Methods and systems for real-time remote monitoring of a well completion operation, e.g., a fracturing operation will be described. In general, early detection of abnormal fracturing conditions is important in oil/gas completions operations such as fracturing in which fluid communication is established between a formation and a wellbore by injecting fluid, also referred to as fracturing fluid or “frac fluid,” into the formation via the wellbore. Fracturing fluid containing a proppant is pumped into a wellbore from a surface facility. Early detection of abnormal fracturing conditions may allow timely corrective actions to be taken before the occurrence of unwanted events or anomalies. A real-time data stream may be analyzed to alert a fracturing engineer to a potential anomaly. For example, an alert may warn a fracturing engineer about one or more parameters that are in an abnormal range in view of one or more other parameters well before an anomaly occurs. Alerts provided to a fracturing engineer may lead to earlier corrective action such as, for example, a change in fluid additive concentration to avoid a screen out, or reduction in flow rate to avoid fracturing equipment failure. In general, alarms that indicate abnormal fracturing conditions based on trend analysis of fracturing parameters and causal relationships among fracturing parameters may provide a valuable tool to a fracturing engineer.
In methods and systems disclosed, a computer-based system acquires real-time data at a well site. The method and system are executed by a computer according to instructions. Such computer-executable instructions may include programs, routines, objects, components, data structures, and computer software technologies that can be used to perform particular tasks and process abstract data types. Software instructions may be coded in different languages for application in a variety of computing platforms and environments. The scope of the present methods and systems are not limited to any particular computer software technology. The present methods and systems may be practiced using any one or combination of hardware and software configurations, including but not limited to a system having single and/or multiple processor computers, hand-held devices, tablet devices, programmable consumer electronics, mini-computers, mainframe computers, and the like. The methods and systems may also be practiced in distributed computing environments where tasks are performed by servers or other processing devices that are linked through one or more data communications network. In a distributed computing environment, program modules may be in both local and remote computer storage media including memory storage devices. Non-transitory processor readable media for use with a computer processor, such as CDs, pre-recorded disks or other equivalent devices, may include a program means recorded thereon for directing the computer processor to facilitate the implementation of the present methods and systems. Such devices and articles of manufacture also fall within the scope of the present invention.
Examples of the data used in the logic include surface treating pressure (also referred to as treating pressure), flow rate, and chemical concentration of the frac fluid. Treating pressure is the hydraulic pressure at the surface due to the flow of fluid. Flow rate is the volume of fluid pumped into the wellbore per unit time. Table 1 lists examples of data used in the logic by the computer-based system and acquired as real-time data at the well site. Table 2 lists additional optional examples of data used in the logic by the computer-based system and acquired as real-time data at the well site.
The computer-based system filters the real-time data to remove noise and processes the data to generate output values.
The output values include incremental changes in the real-time data.
A pre-defined threshold for the output values is input into the computer-based system by a user. If an output value exceeds the threshold, the system will generate an alarm.
In addition, the computer-based system compares multiple independent data sets and, based on logic, determines if an alarm is warranted.
Examples of alarms to be generated include: increase in treating pressure (assuming no change in other factors that would explain the increase), decrease in treating pressure (assuming no change in other factors that would explain the decrease), and surface equipment issues (unexplained decrease in multiple data sets).
By notifying offsite supervisors of these events in a timely manner, a corrective action can be put in place before the occurrence of an anomalous event, e.g. a screen out, equipment failure, etc.
As used herein, “fracturing equipment” refers to any of various equipment used in fracturing including slurry blenders, high-pressure, high-volume fracturing pumps (e.g., triplex pumps), chemical additive units to monitor addition of chemical compounds, fracturing tanks, proppant storage units, flexible hoses, and sensors for flow rate, fluid density, and treating pressure.

TABLE 1

Property	Description	Units

Treating Pressure	Surface pressure on well currently being	psi
	fractured, read between a missile (flow
	control unit) and a wellhead
Slurry Rate	Flow rate of frac fluid with proppant	bpm
Clean Rate	Fresh water flow rate without proppant	bpm
Actual Stage Slurry	The actual volume of slurry pumped in the	bbl
Volume	current interval
Actual Stage Clean	The total volume of fresh water pumped in	bbl
Volume	the current interval
Surface Proppant	Proppant concentration at surface	ppa
Concentration
Total Stage	Total proppant pumped for the current	lbs
Proppant Volume	interval
Job Proppant in	Total proppant pumped into the formation	lbs
Formation	for current stage
Bottomhole	Proppant concentration at the perforations	ppa
Proppant
Concentration
Intermediate	Annulus pressure between the production	psi
Casing Pressure	and intermediate casing
Surface Casing	Annulus pressure between the intermediate	psi
Pressure	and surface casing
Job Pump Time	Total pump time from start of job to	hh:mm
	current time
Friction Reducer	Additive added to fluids to reduce wellbore	g/Mgal
(FR) Concentration	friction while pumping
Gel Concentration	Additive used to increase viscosity, reduce	lbs/Mgal
	friction and control fluid loss
Diverter	Additive to temporarily plug open	g/Mgal
	perforations and allow fluid to flow into
	other perforations
Target Stage	Planned fluid density of interval	ppg
Density
Blender Density	Fluid density at the blender	ppg
Wellhead pressure	Hydraulic pressure observed at the	psi
	wellhead

TABLE 2

Property	Description	Units

Target Stage Slurry	Pre-job planned slurry volume of the	bbl
Volume	current interval
Slurry Left in Stage	Target Slurry Volume—Actual Slurry	bbl
	Volume
Target Stage Clean	Pre-job planned fresh water volume	bbl
Volume	of the current interval
Clean Left in Stage	Target Clean Volume—Actual Clean	bbl
	Volume
Time Until Slurry	Calculated time remaining until each
Hits Perfs	slurry concentration reaches the	hh:mm
	perforations
Current Slurry	Total volume of each slurry	bbl
Volume in Wellbore	concentration in the wellbore
Calculated Bottom	Surface Pressure + Hydrostatic	psi
Hole Pressure	Pressure—Total Friction
Adjacent Well	Production casing pressure of nearby	psi
Surface Pressure	offline wells
Adjacent Well	Intermediate casing pressure of nearby	psi
Annulus Pressure	offline wells

Now referring to the drawings, FIG. 1 illustrates a method 100 of monitoring fracturing conditions according to an example embodiment. The method 100 is used to generate one or more alarms based on raw data that may be generated by fracturing equipment used to inject fluid into a formation via the wellbore through the use of sensors. In the method 100, functions may be implemented to determine data trends in individual data streams or data sets of the raw data representing different fracturing related parameters.
In some example embodiments, the method 100 includes acquiring raw data at step 102. To illustrate, the raw data may include several parameters (e.g., different stream/sets of data representing different fracturing related parameters) that are measured in real time during fracturing. Some of the parameters may be related to each other such that a change in one parameter is linked to a change in another parameter. Examples of parameters that may be measured include surface pressure of the fracturing fluid, flow rate of the fracturing fluid and/or chemical concentration of a chemical compound in the fracturing fluid. The raw data may include parallel streams of data representing the different parameters. Alternatively, the raw data may include serial streams of data representing the different parameters. In some example embodiments, the raw data may be received/acquired from a data server that acquires/receives the raw data from fracturing equipment (e.g., sensors) that measure and/or determine the different parameters.
At step 104, the method 100 includes conditioning the raw data to generate conditioned data. Step 104 may be performed on some data streams (i.e., sets of data) of the raw data while other data streams (i.e., sets of data) of the raw data may be unaffected by conditioning the raw data at step 104. Conditioning of individual data streams or sets of data may be performed using signal processing methods, further described below.
In some example embodiments, generating the conditioned data includes performing an outlier removal process based on a median deviation of the raw data. In general, performing outlier removal removes unwanted and/or abnormal spikes in the raw data. To perform outlier removal at step 104, a median value of the raw data over a pre-defined moving window may first be determined. If a particular data value of the raw data is greater than the median value plus an outlier threshold value (e.g., user provided threshold value), the particular data value may be replaced with the median value.
In some example embodiments, noise removal is performed at step 104. For example, noise removal may be performed on the output of the outlier removal process. Alternatively, noise removal may be performed on the raw data without performing the outlier removal process. To illustrate, treating the raw data as signal streams, noise removal may be accomplished by performing a first order lag filtering on one or more signal streams to remove high frequency noise from the signal streams.
For example, a first order exponential filter may be used on a signal stream (before or after outlier removal) to pass the low frequency components and reducing the amplitude of signals with higher frequencies. The output of the filter depends on previous output value, current input value, the filter constant, and the time interval between two input values.
In some example embodiments, a deadband process is also performed at step 104 to reduce unwanted variations in the data by outputting only step changes. The deadband process may be performed on the output of the outlier removal process or the noise removal process or on the raw data.
In some example embodiments, one or more of the outlier removal, noise removal and deadband processes may be omitted at step 104. For example, one or more of the processes may be omitted for sets of data corresponding to some fracturing parameters while two or all of the processes are performed on data sets corresponding to other fracturing parameters. To illustrate, a deadband process may be performed without performing the outlier removal and the noise removal processes on some sets of data while all three processes may be performed at step 104 on other sets of data. In general, the last one of the processes performed in series at step 104 outputs the conditioned data.
At step 106, the method 100 includes processing the conditioned data generated at step 104 to generate output values for use in alarm generation. To illustrate, data trend and variation of the conditioned data over varying ranges of time may be analyzed to identify increasing, decreasing, and constant trends.
In some example embodiments, processing the conditioned data at step 106 may include performing a value change process that includes analyzing for incremental changes (increasing/decreasing/no change) over ranges of time. The value change process includes determining incremental changes in data values of the conditioned data and determining whether a sum of the incremental changes exceeds a threshold. For example, the value change process may be performed on a set of data values corresponding to Treating Pressure. To illustrate, the value change process tracks incremental changes in a data stream (i.e., in a set of data values) of the conditioned data and determines if the total change is beyond a user defined threshold. If the total change is beyond the threshold, and has been beyond the threshold for a time period (e.g., a time period provided by the user), the value change process indicates a change in value in the data values. The type of change in value, increase or decrease, is specified by the manner in which the threshold is exceeded.
In some example embodiments, processing the conditioned data at step 106 may include performing a lag time lookup process on causally related fracturing parameters based on the corresponding sets of data. To illustrate, a lag time look-up formula may be used to determine the look-back time or lag time for a set of data values corresponding to a particular fracturing parameter. For example, the lag time lookup process may be used to look back on the value changes of the surface proppant concentration parameter determined by performing the value change process described above.
At step 110, the method 100 further includes generating one or more alarms based on the outputs generated at step 106 to indicate one or more abnormal fracturing conditions. For example, the alarms may be generated at step 110 by performing logical operations (such as true, false, greater than, less than, etc.) using outputs of the data processing operations at step 106, as well as other inputs such as end-user specified thresholds and data ranges. The alarms generated at step 110 will be transmitted in real time and displayed so that early warning of abnormal conditions can be provided to a fracturing engineer.
As described above, the method 100 may be performed to provide the ability to create and/or display output alarms for abnormal fracturing conditions based on trend analysis and causal relationships of basic fracturing parameters subject to unexpected external forces. Such alarms can reliably alert fracturing personnel of abnormal conditions.
FIG. 2 illustrates a block diagram of a system 200 for monitoring fracturing conditions according to an example embodiment. The system 200 includes a raw data source 202. The raw data source 202 provides raw data related to a fracturing operation at the well site. The raw data source 202 may be a real-time data source such as fracturing equipment. For example, the raw data source 202 may include multiple sensors that measure, for example, fracturing parameters such as surface pressure of the fluid, flow rate of the fluid and/or chemical concentration of at least one compound in the fluid. Each of the fracturing parameters may be represented in the raw data by distinct data streams or one or more distinct sets of data. In some example embodiments, the raw data source 202 may include a data server that acquires/receives the raw data from fracturing equipment such as sensors.
In some example embodiments, the system 200 includes a data conditioner 204. For example, the data conditioner 204 may be a signal processor that processes the signal representing the raw data from the raw data source 202. The data conditioner may perform the processes described above with respect to step 104 of the method 100 of FIG. 1. For example, the data conditioner 204 may remove outlier data values, filter out noise and perform a deadband operation as described with respect to step 104 of FIG. 1.
In some example embodiments, the system 200 includes a data processor 206. For example, the data processor 206 may be a microprocessor or a microcontroller. The data processor 206 may perform the processes described above with respect to step 106 of the method 100 of FIG. 1. For example, the data processor 206 may perform the value change process to analyze for incremental changes (increasing/ decreasing/no change) of data values over ranges of time. In some example embodiments, the data processor 206 may also perform the lag time lookup process described above.
In some example embodiments, the system 200 includes an alarm generator 210. The alarm generator 210 outputs one or more alarms to indicate one or more abnormal fracturing conditions. For example, the alarm generator 210 may be used to implement alarm generation described above with respect to step 110 of the method 100. The alarms generated by the alarm generator 210 may be transmitted in real time or displayed so that early warning of abnormal conditions existing downhole can be provided to a fracturing engineer. Such alarms can reliably alert fracturing personnel of abnormal conditions as compared to alarms that are based on a single fracturing parameter exceeding a threshold.
In some example embodiments, one or more of the components of the system 200 may be implemented in a single device. For example, the alarm generator 210 may be integrated into the data processor 206. Further, the data conditioner 204, the data processor 206, and the alarm generator 210 may be implemented using hardware (e.g., FPGA), software, or a combination of hardware and software.
FIG. 3 illustrates details of the system 200 of FIG. 2 according to an example embodiment. As illustrated in FIG. 2, the raw data source 202 may include a database server 302 that provides raw data to components of the system 200. For example, the database server 302 may acquire/receive the raw data from fracturing equipment such as sensors. As described above, the raw data includes data streams or sets of data values that correspond to distinct fracturing parameters such as treating pressure, flow rate, gel or friction reducer concentrations and downhole or surface proppant concentrations
The data conditioner 204 may receive the raw data from the raw data source 202 and perform conditioning of the raw data. In some example embodiments, the data conditioner 204 may be a signal processing device. As illustrated in FIG. 3, the data conditioner 204 may include an outlier removal module 304, a filter (i.e., noise removal) module 306, and a deadband module 308. Although the outlier removal module 304, the filter module 306, and the deadband module 308 are shown serially connected in FIG. 3, in alternative embodiments, one or more of the modules 304, 306, 308 may be omitted or bypassed.
In some example embodiments, the outlier removal module 304 performs the outlier removal process described with respect to step 104 of the method 100 to remove unwanted and/or abnormal spikes in the raw data. To perform outlier removal at step 104, a median value of the raw data over a pre-defined moving window may first be determined. If a particular data value of the raw data is greater than the median value plus an outlier threshold value (e.g., user provided threshold value), the particular data value may be replaced with the median value.
In some example embodiments, the filter module 306 performs the noise removal process described with respect to step 104 of the method 100. To illustrate, the filter module 306 may be a first order exponential filter that removes high frequency noise from signals corresponding to data streams of the raw data representing different fracturing parameters. The output of the filter depends on previous output value, current input value, the filter constant, and the time interval between two input values. The operation of the filter module 306 may be described by Equation (1) below.
$\begin{matrix} {output}_{n} = (1 - α) . {input}_{n} + α . {output}_{n - 1} Where : α = \exp (- \frac{Δ time}{FilterConstant}) & Equation (1) \end{matrix}$
In Equation (1), outputn equals the current output value; inputn equals the current input value; and output_n−1equals the previous output value.
In some example embodiments, the deadband module 308 reduces unwanted variations in the data by outputting only step changes. The deadband module 308 may operate on the output of the noise removal process as shown in FIG. 3. In alternative embodiments, the deadband module 308 may operate on the output of the outlier removal module 304 or the on the raw data. In some example embodiments, the deadband module 308 may be operated on all sets of data (of the raw data) corresponding to different fracturing parameters.
In some example embodiments, to reduce unwanted variations in a filtered set of data values corresponding to a particular fracturing parameter (e.g., Treating Pressure), the deadband module 308 determines the median value of the filtered data over a pre-defined moving window and if a data value of the filtered set of data values falls within the median value +/− (i.e., plus or minus) a deadband threshold, the particular parameter is considered constant, and the output is set at the previous output value. If the data value of the filtered set of data values exceeds the deadband threshold, a change in trend of the data values of the particular filtered set of data values is considered to have occurred, and the output is set at the current median. The deadband module 308 may operate on other sets of data corresponding to other fracturing parameters represented in the raw data in a similar manner.
FIG. 4 illustrates a plot of the input raw data and the output values of the data conditioner 204 according to an example embodiment, e.g., the Outlier Removal module 304 output, the Filter module 306 output, and the Deadband module 308 output. FIG. 4 is a plot of Treating Pressure vs. time. Referring to FIGS. 3 and 4, multiple spikes are present in the curve representing a set of data values of the raw data that correspond to a particular fracturing parameter (e.g., Treating Pressure) as shown in FIG. 4. As illustrated in FIG. 4, the plot output of the filter module 306 that operates on the output of the outlier removal module 304 does not have the spikes and is less noisy than the curve of the set of data vales of the raw data. Further, the plot of the output of the deadband module 308 maintains a relatively flat profile until a step rate change occurs in the output of the filter module 306.
In some example embodiments, the data processor 206 may receive the output of the data conditioner 204 and generate outputs that are used in alarm generation by the alarm generator 210. In particular, the data processor 206 may receive the output of the deadband module 308. As illustrated in FIG. 3, the data processor 206 may include a value change module 312.
In some example embodiments, the value change module 312 of the data processor 206 analyzes incremental changes (increasing/decreasing/no change) over ranges of time for Treating Pressure, Flow Rate, Friction Reducer, Gel, Downhole Proppant, and Surface Proppant Concentration parameters represented by sets of data values in the output data from the data conditioner 204. The value change module 312 may perform the value change process of step 106 of the method 100 to track the incremental changes of a set of data values corresponding to a fracturing parameter and to determine if the total change is beyond a user defined threshold.
In some example embodiments, the value change module 312 performs a series of calculations and value comparisons to analyze incremental changes. If the total change is beyond the threshold, and has been beyond the threshold for a period defined by the user, the value change module 312 outputs a change in value. The type of change in value, i.e., increase or decrease, is specified by the manner in which the threshold has been exceeded.
In some example embodiments, the inputs provided to the value change module 312 are a set of data values corresponding to a particular fracturing parameter, Onset Window, and Value Change Threshold provided by a user. The value change module 312 may generate/store outputs including Value Change Intermediate and Value Change Final markers as Increase (e.g., 1), Decrease (e.g., (−1)), No Change (e.g., 0) and Timestamps associated with all stored outputs.
During operation, the value change module 312 determines the incremental change in the data values corresponding to a particular fracturing parameter by calculating the difference (V-difference) between current data value and a previous data value of the data values corresponding to the particular fracturing parameter. To determine the total change in the data values, the value change module 312 increments the total change (Deadband Difference) by V-difference if a previous Value Change Final marker indicates No Change, and resets Deadband Difference to zero if a previous Value Change Final marker indicates “Increase” or “Decrease. The value change module 312 also checks if Deadband Difference (the total change in the data values) has reached a threshold. To illustrate, if Deadband Difference>=Value Change Threshold, Value Change Intermediate is set to “Increase.” If Deadband Difference <=−1×Value Change Threshold, Value Change Intermediate is set to “Decrease.”
If a value change has been detected as indicated by Value Change Intermediate, the value change module 312 determines whether the same change has been detected continuously over a time window defined by Onset Window. For example, if the current timestamp is 07:05:30, the Onset Window is equal to 30 seconds and Value Change Intermediate equals “Increase,” the value change module 312 checks whether Value Change Intermediate equals “Increase” for all timestamps between 07:05:00 and 07:05:30, inclusive and outputs “Increase” to indicate so. A similar operation is performed when Value Change Intermediate equals “Decrease.” In both cases, if Value Change Intermediate has not been a constant value in the Onset Window, the value change module 312 will not output a Value Change Final marker indicating a change in value.
FIG. 5 illustrates a plot of input Deadband values from Deadband module 308 and output values of the value change module 312 of the data processor 206 of FIG. 3 according to an example embodiment. Referring to FIGS. 3 and 5, a curve representing data values corresponding to Treating Pressure parameter provided to the value change module 312 and a curve representing output values of the value change module 312 indicating change in value determined as described above are shown. Treating Pressure vs. time is shown on the primary (left) axis, and Value Change (unitless) is plotted vs. time on the secondary (right) axis. In FIG. 5, a ‘1’ indicates that the cumulative change in Value of Treating Pressure over the end-user specified time interval has increased above the end-user specified threshold. A ‘−1’ indicates that the cumulative change in Value of Treating Pressure over the end-user specified time interval has decreased below the end-user specified threshold. A ‘0’ indicates that the cumulative change in Value of Treating Pressure over the end-user specified time does not exceed the threshold.
The plots in FIGS. 4 and 5 were created using historical raw treating pressure data taken on an operating well site during an unconventional hydraulic fracturing operation in west Texas. The data was truncated for ease of viewing and while both figures utilize the same raw data, the plots display different time ranges. The plots were created using the signal processing methods described herein using Microsoft® Excel® (available from Microsoft Corporation, Redmond, Wash.).
In some example embodiments, the data processor 206 includes a lag time lookup module 318 to align causally related fracturing parameters based on their corresponding sets of data values. To illustrate, a lag time look-up formula may be used to determine the look-back time or lag time for a set of data values corresponding to a particular fracturing parameter. For example, the lag time lookup module 318 may be used to look back on value changes of the Surface Proppant Concentration parameter determined by the value change module 312 described above.
To illustrate with respect to Surface Proppant Concentration, the lag time lookup module 318 may calculate “Lag Time” based on current Flow Rate indicated by the sets of data values in the raw data and conditioned by the data conditioner module 204. For example, the lag time lookup module 318 may calculate “Lag Time” according to Equation (2).
$\begin{matrix} Lag Time (s) = (\frac{Perf Depth (ft) \times Wellbore {ID (in)}^{2}}{61, 764 \times Flow Rate (bpm)}) & Equation (2) \end{matrix}$
In Equation (2), the depth of the perforations and wellbore inner diameter are defined by the user unless provided in a set of data values in the raw data.
In some example embodiments, the alarm generator 210 may generate a screenout alarm, equipment failure alarm, or adjacent well communication alarm based on the outputs of the data processor 206. As illustrated in FIG. 3, some alarms are related to one or more fracturing parameters and other alarms are related to one or more other or overlapping fracturing parameters. As an example, a screenout alarm can indicate an increase in treating pressure that occurs without a corresponding increase in flow rate or decrease in friction reducer and/or decrease in gel concentration. As another example, an equipment failure alarm can indicate a sharp decrease or loss of treating pressure that occurs without a corresponding decrease in flow rate. As another example, an adjacent well communication alarm can indicate a slight decrease in treating pressure and a corresponding increase in surface pressure (e.g. production casing pressure, intermediate casing pressure or surface casing pressure) on nearby monitored wells. As illustrated in FIG. 3, a module 322 of the alarm generator 210 generates the screenout alarm based on a combination of one or more outputs from the lag time lookup module 318, and one or more outputs from the value change module 312.
In some example embodiments, one of the modules 312 or 318 may be omitted or may operate on different fracturing parameters than shown in FIG. 3. Further, causal relationships between fracturing parameters other than described above may be used in the generation of alarms.
FIGS. 6A-6C illustrate a signal processing flowchart 400 illustrating the logic of the outlier removal module 304, the filter module 306 and the deadband module 308 of the data conditioner 204 and the value change module 312 of the data processor 206. The methods below are executed in the order described. However, not all methods need to be applied to every data set. Also, the flowchart is a continuous loop and is run for every data point in the set.
When raw data is received from the frac site, it is generally too noisy to calculate discrete trends. To resolve this, various signal processing methods can be employed to smooth the data. The first phase of signal processing involves removing any unwanted or abnormal spikes in the raw data. Referring to FIG. 6A, in step 401, the median value (Spike Median) of the raw data over a pre-defined moving time window (Spike Window) is calculated. In step 402, the raw data point is compared to the Spike Median and user provided threshold value (Spike Threshold). If the raw data point falls within the Spike Median plus or minus (+/−) the Spike Threshold, in step 403 this data point (No Outliers) remains the raw value. If the raw data point falls outside that window, in step 404 No Outliers is replaced with the Spike Median. Next, in step 405, referencing Equation 1, the No Outliers data is smoothed via a first-order filter equation (Filter Constant) that removes high frequency noise. The result is a data point (Filtered) that can begin the second phase of processing.
The second phase of processing aims to further reduce unwanted variations by outputting only step changes in the data. In step 406, the median value (Deadband Median) of the Filtered data over a pre-defined, moving time window (Deadband Window) is calculated. Referring to FIG. 6B, in step 407, if the Filtered value falls within the Deadband Median plus or minus (+/−) the user provided threshold (Deadband Threshold), no step change has occurred and in step 408, the output (Deadband) is set equal to the previous Deadband data point. If the Filtered value falls outside that window, in step 409, a step change has occurred and the Deadband is set equal to the Deadband Median.
The final phase of processing involves producing markers indicative of an increasing or decreasing trend per the cumulative differences between data points. This final signal can then be used to generate alarms. In step 410, checks if the previous (Value Change Final) marker described later in this section is equal to positive or negative one (1) or (−1). If so, in step 411, the data point (Deadband Difference) is set equal to 0. Otherwise, in step 412, the Deadband Difference is equal to the previous Deadband Difference plus (+) the difference between the current and previous Deadband values. Next, in step 413, the absolute value of the Deadband Difference is compared to the user provided threshold (Value Change Threshold). If the Deadband Difference is greater than or equal to (>=) the Value Change Threshold in step 414, the (Value Change Intermediate) marker is assigned a value of 1 if the Deadband Difference is positive, and in step 415, a value of −1 if the Deadband Difference is negative. Otherwise, in step 416, the Value Change Intermediate marker is set equal to 0. Referring to FIG. 6C, in step 417. if all previous Value Change Intermediate markers over a pre-defined, moving time window (Onset Window) are equal to 1, in step 418 a (Value Change Final) marker is set equal to 1. In step 419. if all previous Value Change Intermediate markers over the Onset Window are equal to −1, in step 420 the Value Change Final marker is set equal to −1. Otherwise, in step 421, the Value Change Final marker is set to 0.
A flowchart 500 illustrating the logic of the screenout alarms generation based on outputs of the modules 312 and 318 of the data processor 206 and user provided inputs is shown in FIG. 7 according to an example embodiment. The primary data sets evaluated in the Screenout alarm include Treating Pressure, Flow Rate, Friction Reducer concentration, Gel concentration, Surface Proppant concentration and Downhole Proppant concentration. Each data set above is evaluated in real time to determine whether an anomaly exists and, if so, generate an alarm to notify the frac site personnel.
The first step in the logic, step 501, is to identify a Treating Pressure (TP) increase that is greater than the user provided threshold. If true, Flow Rate (FLOW) is evaluated in step 502 to determine if it has stayed increased, decreased, or stayed constant over a defined time window. If FLOW has increased, a screenout alarm will not be generated as the system will consider the TP increase as a normal response to the increase in FLOW. Conversely, if FLOW has decreased or stayed constant more diagnostics must be run to determine the root cause of the TP increase. Given all other data sets stay constant, a constant or decreasing FLOW should reduce the Treating Pressure. Therefore, a TP increase that is greater than the pre-defined threshold with a simultaneous decrease of constant FLOW may indicate an abnormal condition.
Next, in steps 503 and 504, the logic evaluates if the Friction Reducer concentration (FR) and/or Gel concentration (GEL), respectively, has increased over the defined time window. If so, the TP increase is considered an abnormal event and a Screenout Alarm is generated ( Screenout Alarm # 1, 2, 3, 6, 7 or 8) in step 506. With respect to changes in TP, FR and GEL are the dominant data sets over Surface Proppant or Downhole Proppant concentrations. Therefore, if FR or GEL increase, assuming constant or decreasing FLOW, TP should decrease regardless of Surface Proppant or Downhole Proppant concentration trends. Conversely, if FR and/or GEL decrease a screenout alarm will not be generated as the system will consider the TP increase as a normal response to the decrease in either data set.
Finally, if both FR and GEL have stayed constant the logic evaluates the Downhole Proppant (DHP) and Surface Proppant (SP) concentration changes over the time window in step 505. Referencing FIG. 3, note that the SP data set may pass through the lag time lookup module 318 in the data processor 206 prior to being evaluated in step 505 as shown in FIG. 7. If DHP and/or SP has stayed constant or decreased, the Treating Pressure increase is considered an abnormal event and a Screenout Alarm is generated ( Screenout Alarm # 4, 5, 9 or 10) in step 506. Conversely, an increase in DHP and/or SP could explain the increase in TP, and consequently the system will not generate an alarm for this time window.
In FIG. 7, a number of screenout alarms are generated based on causal relationships between fracturing parameters, namely, treating pressure (TP), flow rate (FLOW), friction reducer concentration (FR), gel concentration (GEL), downhole proppant concentration (DHP) and surface proppant concentration (SP). Ten of the possible screenout alarms that may be generated, all of which indicate an increase in TP above a user defined threshold, are shown in FIG. 7. Screenout Alarm # 1 indicates a decrease in FLOW and increase in both FR and GEL. Screenout Alarm # 2 indicates a decrease in FLOW, an increase in FR, and no change in GEL. Screenout Alarm # 3 indicates a decrease in FLOW, no change in FR, and an increase in GEL. Screenout Alarm # 4 indicates a decrease in FLOW, no change in FR, no change in GEL, and no change in DHP and/or SP. Screenout Alarm # 5 indicates a decrease in FLOW, no change in FR, no change in GEL, and a decrease in DHP and/or SP. Screenout Alarm # 6 indicates no change in FLOW and an increase in both FR and GEL. Screenout Alarm # 7 indicates no change in FLOW, an increase in FR, and no change in GEL. Screenout Alarm # 8 indicates no change in FLOW, no change in FR, and an increase in GEL. Screenout Alarm # 9 indicates no change in FLOW, no change in FR, no change in GEL, and no change in DHP and/or SP. Screenout Alarm # 10 indicates no change in FLOW, no change in GEL, no change in FR, and a decrease in DHP and/or SP.
In general, the systems and methods described above with respect to FIGS. 1-7 provide alerts about abnormal fracturing conditions based on trends and relationships between different fracturing parameters. The generated alarms may result in prevention of fracturing related incidents. The removal of outlier data values and noise in sets of data values corresponding to fracturing parameters allows for generation of reliable alarms. The apparatus, system, and method reduce subjectivity in the generation of alarms and provide for consistency in the generation of alarms. The generated alarms may be provided to fracturing engineers at well sites or may be provided to monitoring personnel in various forms including via a display.
The systems and methods advantageously address a need for real-time alerts of anomalous events during hydraulic fracturing operations that may otherwise go unnoticed, potentially resulting in an event e.g., nonproductive time or a premature failure. Nonproductive time can be reduced during frac jobs by notifying personnel monitoring these operations of an abnormal event. The timely notification will allow personnel to apply a corrective action prior to an abnormal event occurring.
Although some embodiments have been described herein in detail, the descriptions are by way of example. The features of the embodiments described herein are representative and, in alternative embodiments, certain features, elements, and/or steps may be added or omitted. Additionally, modifications to aspects of the embodiments described herein may be made by those skilled in the art without departing from the spirit and scope of the following claims, the scope of which are to be accorded the broadest interpretation so as to encompass modifications and equivalent structures.

Claims

What is claimed is:

1. A method of monitoring a completions operation in which fluid communication is established between a formation and a wellbore, the method comprising:

a. acquiring data from a data server that receives the data from sensors associated with fracturing equipment used to inject fluid into a formation via the wellbore in a completions operation, wherein the data comprise at least one fracturing related parameter measured in real time during the completions operation;

b. processing, by a data processor, the data to generate output values for use in alarm generation, wherein processing the data comprises:

i. determining incremental changes in data values of the data and whether the incremental changes are an increase, decrease or no change for each of the at least one fracturing related parameter;

ii. summing the incremental changes over a user-defined time period for each of the fracturing related parameters;

iii. determining and indicating whether the sum of the incremental changes exceeds a threshold for the user-defined time period for each of the fracturing related parameters; and

iv. determining and indicating as the output values whether the result of step (b)(iii) is associated with an abnormal fracturing condition as defined by the user; and

c. generating, by an alarm generator, an alarm based on the output values to indicate the abnormal fracturing condition.

2. The method of claim 1 wherein step (b)(iv) further comprises determining whether the result of step (b)(iii) is caused by a fracturing condition for which corrective action is not needed; and if so, not generating the alarm based on the output values to indicate the abnormal fracturing condition.

3. The method of claim 1 wherein the at least one fracturing related parameter comprises at least two fracturing related parameters.

4. The method of claim 1 wherein the sum of the incremental changes is determined to exceed the threshold for the user-defined time period when the sum of the incremental changes exceeds the threshold continuously over the user-defined time period for each of the fracturing related parameters.

5. The method of claim 1 wherein the fracturing equipment comprises proppant fracturing equipment used to inject fluid containing proppant material into the formation.

6. The method of claim 5 wherein the fracturing equipment is selected from the group consisting of tanks, pits, pumps, high pressure lines, manifolds and combinations thereof

7. The method of claim 1 wherein the abnormal fracturing condition comprises a screen out, a fracturing equipment failure, and/or adjacent well communication.

8. The method of claim 1 wherein the sensors associated with the fracturing equipment measure surface pressure of the fluid, flow rate of the fluid and/or chemical concentration of at least one compound in the fluid.

9. The method of claim 1 wherein the data comprise data sets representing surface pressure of the fluid, flow rate of the fluid and/or chemical concentration of at least one compound in the fluid.

10. The method of claim 1 wherein the alarm indicates an increase in surface pressure of the fluid exceeding a surface pressure threshold without a corresponding increase in flow rate of the fluid.

11. The method of claim 10 wherein the alarm further indicates the increase in surface pressure of the fluid exceeding the surface pressure threshold without a corresponding decrease in friction reducer and/or gel concentration of the fluid.

12. The method of claim 11 wherein the alarm further indicates the increase in surface pressure of the fluid exceeding the surface pressure threshold without a corresponding increase in downhole and/or surface proppant concentration of the fluid.

13. The method of claim 1, wherein the at least two fracturing related parameters measured in real time during the completion operation are causally related.

14. The method of claim 13, further comprising compensating for lag time between causally related fracturing related parameters.

15. A system for monitoring a completion operation in which fluid communication is established between a formation and a wellbore, the system comprising:

a. a data source comprising data received from sensors associated with fracturing equipment used to inject fluid into a formation via the wellbore in a hydraulic fracturing operation wherein the data includes at least one fracturing related parameter measured in real time by sensors during the completion operation;

b. a data processor for processing the data to generate output values for use in alarm generation, the data processor configured with instructions to:

i. determine incremental changes in data values of the data and whether the incremental changes are an increase, decrease or no change for each of the fracturing related parameters;

ii. sum the incremental changes over a user-defined time period for each of the fracturing related parameters;

iii. determine and indicate whether a sum of the incremental changes exceeds a threshold for a user-defined time period for each of the fracturing related parameters; and

iv. determine and indicate as the output values whether the result of step (b)(iii) is associated with an abnormal fracturing condition as defined by the user; and

c. an alarm generator to generate an alarm based on the output values to indicate an abnormal fracturing condition.

16. The system of claim 15 wherein the at least one fracturing related parameter comprises at least two fracturing related parameters.

17. The system of claim 15 wherein the sum of the incremental changes is determined to exceed the threshold for the user-defined time period when the sum of the incremental changes exceeds the threshold continuously over the user-defined time period for each of the fracturing related parameters.

18. The system of claim 15 wherein the fracturing equipment comprises proppant fracturing equipment used to inject fluid containing proppant material into the formation.

19. The system of claim 18 wherein the fracturing equipment is selected from the group consisting of tanks, pits, pumps, high pressure lines, manifolds and combinations thereof

20. The system of claim 15 wherein the abnormal fracturing condition comprises a screen out, a fracturing equipment failure and/or adjacent well communication.

21. The system of claim 15 wherein the sensors associated with the fracturing equipment sense surface pressure of the fluid, flow rate of the fluid and/or chemical concentration of at least one compound in the fluid.

22. The system of claim 15 wherein the data comprise data sets representing surface pressure of the fluid, flow rate of the fluid and/or chemical concentration of at least one compound in the fluid.

23. The system of claim 15 wherein the alarm indicates an increase in surface pressure of the fluid exceeding a surface pressure threshold without a corresponding increase in flow rate of the fluid.

24. The system of claim 23 wherein the alarm further indicates the increase in surface pressure of the fluid exceeding the surface pressure threshold without a corresponding decrease in friction reducer and/or gel concentration of the fluid.

25. The system of claim 24 wherein the alarm further indicates the increase in surface pressure of the fluid exceeding the surface pressure threshold without a corresponding increase in downhole proppant and/or surface proppant concentration of the fluid.