Patent Application
20170096889
The present disclosure relates generally to process automation, and more particularly but not by limitation, to real-time detection of stress patterns and equipment failures based on sensor data.
In many industries, automated processes are now used for fabrication of products, monitoring operation of systems, designing systems, interacting machinery with other objects and/or the like. In such automated industrial processes, there is a broad latitude of issues that may affect the process. These issues may cause a halt and/or break down of the automated industrial process, may degrade the operation of the automated industrial process, may change the background, environment and/or the like the automated industrial process is working in and so may change how the automated industrial process works, what the automated industrial process achieves, the goal of the automated industrial process and/or the like.
One or more of the broad latitude of issues that may affect the automated industrial process may arise during the automated industrial process causing real time changes to the operation of the automated industrial process. To mitigate such issues, forward-looking models of the automated industrial process may be analyzed and used to control the automated industrial process. Such models may be determined from results from prior processes, theoretically, experimentally and/or the like. Mitigation of such issues may also be achieved by obtaining data from the automated industrial process and/or the environment in which the automated industrial process occurs and retroactively identifying the existence of an issue.
Merely by way of example, in the hydrocarbon industry, the process of drilling into a hydrocarbon reservoir may be impeded by a wide variety of problems and may include monitoring/interpretation of a considerable amount of data. Accurate measurements of downhole conditions, downhole equipment properties, geological properties, rock properties, drilling equipment properties, fluid properties, surface equipment properties and/or the like may be analyzed by a drilling crew to minimize drilling risks, to make determinations as to how to optimize the drilling procedure given the data and/or to detect/predict the likelihood of a problem/decrease in drilling efficiency and/or the like.
Similarly, in hydrocarbon exploration, hydrocarbon extraction, hydrocarbon production, hydrocarbon transportation and/or the like many conditions may be sensed and data gathered to provide for optimizing and/or preventing/mitigating issues/problems concerning the exploration, production and or transportation of hydrocarbons. Hydrocarbons are a lifeblood of the modern industrial society, as such, vast amounts of hydrocarbons are being prospected, retrieved and transported on a daily basis. Associated with this industry are an enormous amount of sensors gathering enumerable amounts of data relevant to the exploration, production and or transportation of hydrocarbons.
To provide for safe and efficient exploration, production and or transportation of hydrocarbons this data may be processed. While computers may be used to process the data, it is often difficult to accurately process the incoming data for real-time control of the hydrocarbon processes. As such, human operators are commonly used to control the hydrocarbon processes and to make decisions on optimizing, preventing risks, identifying faults and/or the like based on interpretation of the raw/processed data. However, optimization of a hydrocarbon process and/or mitigation and detection of issues/problems by a human controller may often be degraded by fatigue, high workload, lack of experience, the difficulty in manually analyzing complex data and/or the like. Furthermore, noisy data may have some impact on a human observer's ability to take note of or understand the meaning occurrences reflected in the data.
The detection of occurrences reflected in the data goes beyond detection of issues and problems. Accurate analysis of operating conditions may allow for an operator to operate the industrial process at near optimal conditions. For example, in the hydrocarbon industry, the bit-response to changes in parameters such as drill-bit rotational speed and weight-on-bit (WOB) while drilling into a hydrocarbon reservoir is very much affected by changes in the lithological environment of drilling operations. Accurate and real-time knowledge of a transition from one environment to another, e.g., one formation to another, and real-time analysis of how such environmental conditions impact the effect that parameter changes are likely to have on bit-response may greatly improve the expected rate of penetration (ROP).
Similarly, the constraints that limit the range of the drilling parameters may change as the drilling environment changes. These constraints, e.g., the rate at which cuttings are removed by the drilling fluids, may limit the maximum permissible drilling parameter values. Without accurate knowledge of these changes in the constraints, a driller may not be fully aware of where the constraints lie with respect to the ideal parameter settings and for the sake of erring on the side of caution, which is natural considering the dire consequences of drilling equipment failures and drilling accidents, a driller may operate the drilling process at parameters far removed the actual optimal parameters. Considering that drilling, like many other processes associated with the production and transport of hydrocarbons is an extremely costly procedure, the operation of the drilling system at less than optimal parameters can be extremely costly.
Similarly, accurate measurement of the direction (Toolface) and curvature (Dogleg-Severity (DLS)) of a borehole is helpful for a driller to accurately direct a drilling process to a target. Measurements of these properties are typically taken at rather infrequent intervals (e.g., every 30 to 90 feet) while the drill-bit is off bottom and the drill string is stationary. However, modern drilling equipment may provide for taking directional measurements continuously while drilling. Unfortunately, the measurements obtained while-drilling are generally very noisy and difficult for a driller to interpret because of the noise in the data.
Furthermore, the noise in the data tends to be amplified in any direct computation of the Dogleg-Severity and Toolface from the continuous surveys and the results are generally of such low quality to be of little value to the drillers. As a result, the while-drilling data is often not used in computation of Dogleg-Severity, Toolface and/or the like and instead the infrequent measurements, which require the drilling process to be halted while the measurements are taken, are often still used to determine drilling trajectory and/or the like.
In the hydrocarbon industry, as in other industries, event detection systems have generally depended upon people, such as drilling personnel, to manage processes and to identify occurrences of events, such as a change in a rig state. Examples of rig state detection in drilling may be found in the following references: “The MDS System: Computers Transform Drilling”, Bourgois, Burgess, Rike, Unsworth, Oilfield Review Vol. 2, No. 1, 1990, pp.4-15; and “Managing Drilling Risk” Aldred et al., Oilfield Review, Summer 1999, pp. 219.
With regard to the hydrocarbon industry, some very limited techniques have been used for detecting a certain type of event, i.e., possible rig states, such as “in slips”, “not in slips”, “tripping in” or “tripping out”. These systems take a small set of rig states, where each rig state is an intentional drilling state, and use probability analysis to retroactively determine which of the set of intentional drilling states the rig has moved into. Probabilistic rig state detection is described in U.S. Pat. No. 7,128,167, the entirety of which is hereby incorporated by reference for all purposes. A system and method for online automation is described in U.S. patent application Ser. No. 13/062,782, now U.S. Pat. No. 8,838,426, the entirety of which is hereby incorporated by reference for all purposes.
Electrical Submersible Pumps (ESPs) are of common use in the Oil and Gas industry. The physicality of the ESP behavior in regard to operational conditions is well documented and understood. One of the main reasons for pump failure is to operate the pump for too long under stressful conditions that are often the result of human error. For instance, problems may occur when a valve downstream of an ESP is closed while the pump is still operating (for any reason). Such actions produce a stress condition for the pump as doing so would lead to operating the pump at zero efficiency with no cooling fluid flowing past the motor. The lack of fluid flow can therefore potentially quickly lead to burning failure of the ESP if left unanswered. This specific phenomenon is oftentimes referred to as “deadhead condition”. Similarly, “gas ingestion,” a situation in which a pump is attempting to pump gas rather than liquid, and “low-flow,” a situation in which the pump is not producing adequate flow, produce stress on the pumping equipment that may lead to equipment failure.
These stress patterns (e.g., deadhead, gas ingestion, and low-flow) have known signatures on the different measurements taken downhole or at the surface of an ESP-equipped well (bottom hole gauge pressure and temperature, surface well head pressure and temperature, etc.). Hitherto, the typical way of detecting deadhead (and other stress conditions and downhole equipment failures) is through a surveillance engineer cognitively recognizing a signature on the different channels of a monitoring system (parameter A increasing, while B is decreasing etc.). However, with a myriad of data-streams arriving quickly and simultaneously, it is often very difficult for the surveillance engineer to make the connection between the data values from the sensors and the potential stress or failure condition.
While the different ESP stress patterns signatures are known, some prior art systems detect stress patterns programmatically. One prior art technology uses fuzzy logic; another applies linear regression to the relevant operating properties (taking into account the last X measurement points) to combine the relevant operating property values into a stress condition corresponding to a known pattern. This approach is most of the time done in a deterministic way; in other words, stress conditions are recognized if they meet the signature programmed in the employed expert system. One thought is that today in the industry a focus is attached to the detection of failure events (broken shaft, gas lock or other) while much less is done on the actual stress conditions leading to a failure.
In the hydrocarbon industry there are ever more and better sensors for sensing data related to the exploration, extraction, production and/or transportation of the hydrocarbons, for example, in the artificial lift domain, which uses electrical submersible pumps or progressive cavity pumps. To better control/automate processes related to the exploration, extraction, production and/or transportation of the hydrocarbons and/or to better process/interpret the data for human controllers/operators of the processes related to the exploration, extraction, production and/or transportation of the hydrocarbons the sensed data associated with the processes may be quickly and effectively handled.
Furthermore, there is still a desire to improve the automation of the detection of these stress patterns and failure conditions so as to quickly detect stress patterns so that equipment failure may be avoided or to mitigate the cost of equipment failure.
Embodiments of the present disclosure provide systems and methods for real-time/online interpretation/processing of data associated with a hydrocarbon related procedure to provide for real-time automation/control of the procedure. In an embodiment of the present disclosure the data is segmented and the segments/changepoints between segments are analyzed so that the data can be processed and provide for the operation/control of the hydrocarbon related procedure.
In one aspect the technology disclosed herein includes a method for detecting equipment failures or stress conditions that may result in equipment failures in a process in the hydrocarbon industry, where the hydrocarbon-industry-process is subject to a change in a plurality of operating conditions each monitored by at least one sensor providing a plurality of input data streams, comprising segmenting the input data streams such that each segment of data points is modeled using a simple mathematical model, using the segmentations and statistical parameters associated with the segmentations and the underlying data to compute probabilities associated with at least one high-level inquiry in regard to the input streams thereby computing probabilities for inquiry answers, and inputting the high-level inquiry probabilities into a reasoning engine and operating the reasoning engine to determine the probability of an equipment event. The method may advantageously be applied to artificial lift operations employing electrical submersible pumps (ESP) or progressive cavity pump.
In another aspect the technology disclosed herein includes a hydrocarbon process control system comprising at least one sensor measuring an operating property of a hydrocarbon process controlled by the control system, a signal processing module for segmenting an input stream from the at least one sensor and for computing probabilities of answers to at least one high-level inquiry in regard to the input stream from the at least one sensor, and an expert system connected to the signal processing module and operable to receive the probabilities for the answers to the at least one high-level inquiry and operable to compute therefrom probabilities of at least one equipment event. Such a control system may advantageously be applied to artificial lift operations employing electrical submersible pumps (ESP) or progressive cavity pump.
The present disclosure is described in conjunction with the appended figures.
In the appended figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components or by appending the reference label with a letter. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label or appended letter.
In the following detailed description, reference is made to the accompanying drawings that show, by way of illustration, specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure. It is to be understood that the various embodiments of the disclosure, although different, are not necessarily mutually exclusive. For example, a particular feature, structure, or characteristic described herein in connection with one embodiment may be implemented within other embodiments without departing from the spirit and scope of the disclosure. In addition, it is to be understood that the location or arrangement of individual elements within each disclosed embodiment may be modified without departing from the spirit and scope of the disclosure. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present disclosure is defined only by the appended claims, appropriately interpreted, along with the full range of equivalents to which the claims are entitled. In the drawings, like numerals refer to the same or similar functionality throughout the several views.
It should also be noted that in the description provided herein, computer software is described as performing certain tasks. For example, we may state that a changepoint detector module performs a segmentation of a data stream by following a described methodology. That, of course, is intended to mean that a central processing unit executing the instructions included in the changepoint detector (or equivalent instructions) would perform the segmentation by appropriately manipulating data and data structures stored in memory and secondary storage devices controlled by the central processing unit. Furthermore, while the description provides for embodiments with particular arrangements of computer processors and peripheral devices, there is virtually no limit to alternative arrangements, for example, multiple processors, distributed computing environments, web-based computing. All such alternatives are to be considered equivalent to those described and claimed herein.
It should also be noted that in the development of any such actual embodiment, numerous decisions specific to circumstance may be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.
In this disclosure, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term “computer-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and various other mediums capable of storing, containing or carrying instruction(s) and/or data.
A technology is presented herein that automates the detection of signal signatures that are indicative of equipment stress and failure events. This technology uses a segmentation algorithm to segment data series and to model the data series. The results from the segmentation are used to answer several high-level inquiries, such as tendencies, noise level, and correlation between signals. Each high-level inquiry has associated therewith several answers. Probabilities for each of the answers is computed and the probabilities are input into a reasoning engine, for example, a Bayesian belief network, for determining the probabilities of equipment events, such as stress patterns and equipment failure.
The segmentation algorithm is described herein in the context of a drilling operation. However, it is applied in some embodiments to hydrocarbon extraction and production, in particular, to artificial lift using electric submersible pumps and progressive cavity pumps.
Accordingly, to set the stage for the explanation of the segmentation algorithm,
The bottomhole assembly 56 contains a number of devices including various subassemblies. According to an embodiment of the present disclosure, measurement-while-drilling (MWD) subassemblies may be included in subassemblies 62. Examples of typical MWD measurements include direction, inclination, survey data, downhole pressure (inside the drill pipe, and outside or annular pressure), resistivity, density, and porosity. The subassemblies 62 may also include is a subassembly for measuring torque and weight on bit.
The subassemblies 62 may generate signals related to the measurements made by the subassemblies 62. The signals from the subassemblies 62 may be processed in processor 66. After processing, the information from processor 66 may be communicated to communication assembly 64. The communication assembly 64 may comprise a pulser, a signal processor, an acoustic processor and/or the like. The communication assembly 64 converts the information from processor 66 into signals that may be communicated as pressure pulses in the drilling fluid, as signals for communication through an optic fibre, a wire and/or the like, or signals for wireless or acoustic communication and/or the like. Embodiments of the present disclosure may be used with any type of sensor associated with the hydrocarbon industry and with any type of telemetry system used with the sensor for communicating data from the sensor to the online changepoint detector, according to one embodiment of the present disclosure.
The subassemblies in the bottomhole assembly 56 can also include a turbine or motor for providing power for rotating and steering drill bit 54. In different embodiments, other telemetry systems, such as wired pipe, fiber optic systems, acoustic systems, wireless communication systems and/or the like may be used to transmit data to the surface system.
The drilling rig 12 includes a derrick 68 and hoisting system, a rotating system, and a mud circulation system. The hoisting system which suspends the drill string 58, includes draw works 70, fast line 71, crown block 75, drilling line 79, traveling block and hook 72, swivel 74, and deadline 77. The rotating system includes kelly 76, rotary table 88, and engines (not shown). The rotating system imparts a rotational force on the drill string 58 as is well known in the art. Although a system with a kelly and rotary table is shown in
The mud circulation system pumps drilling fluid down the central opening in the drill string. The drilling fluid is often called mud, and it is typically a mixture of water or diesel fuel, special clays, and other chemicals. The drilling mud is stored in mud pit 78. The drilling mud is drawn in to mud pumps (not shown), which pump the mud through stand pipe 86 and into the kelly 76 through swivel 74 which contains a rotating seal.
The mud passes through drill string 58 and through drill bit 54. As the teeth of the drill bit grind and gouges the earth formation into cuttings the mud is ejected out of openings or nozzles in the bit with great speed and pressure. These jets of mud lift the cuttings off the bottom of the hole and away from the bit 54, and up towards the surface in the annular space between drill string 58 and the wall of borehole 46.
At the surface the mud and cuttings leave the well through a side outlet in blowout preventer 99 and through mud return line (not shown). Blowout preventer 99 comprises a pressure control device and a rotary seal. The mud return line feeds the mud into separator (not shown) which separates the mud from the cuttings. From the separator, the mud is returned to mud pit 78 for storage and re-use.
Various sensors are placed on the drilling rig 10 to take measurement of the drilling equipment. In particular hookload is measured by hookload sensor 94 mounted on deadline 77, block position and the related block velocity are measured by block sensor 95 which is part of the draw works 70. Surface torque is measured by a sensor on the rotary table 88. Standpipe pressure is measured by pressure sensor 92, located on standpipe 86. Additional sensors may be used to detect whether the drill bit 54 is on bottom. Signals from these measurements are communicated to a central surface processor 96. In addition, mud pulses traveling up the drillstring are detected by pressure sensor 92.
Pressure sensor 92 comprises a transducer that converts the mud pressure into electronic signals. The pressure sensor 92 is connected to surface processor 96 that converts the signal from the pressure signal into digital form, stores and demodulates the digital signal into useable MWD data. According to various embodiments described above, surface processor 96 is programmed to automatically detect the most likely rig state based on the various input channels described. Processor 96 is also programmed to carry out the automated event detection as described above. Processor 96 may transmit the rig state and/or event detection information to user interface system 97 which is designed to warn the drilling personnel of undesirable events and/or suggest activity to the drilling personnel to avoid undesirable events, as described above. In other embodiments, interface system 97 may output a status of drilling operations to a user, which may be a software application, a processor and/or the like, and the user may manage the drilling operations using the status.
Processor 96 may be further programmed, as described below, to interpret the data collected by the various sensors provided to provide an interpretation in terms of activities that may have occurred in producing the collected data. Such interpretation may be used to understand the activities of a driller, to automate particular tasks of a driller, to provide suggested course of action such as parameter setting, and to provide training for drillers.
In the hydrocarbon industry it is often desirable to automate, semi-automate and/or the like operations to remove, mitigate human error, to increase speed and/or efficiency, allow for remote operation or control, lessen communication obstacles and/or the like. Moreover, in the hydrocarbon industry sensors are commonly deployed to gather data to provide for monitoring and control of the systems related to hydrocarbon capture and/or the like.
In the process of drilling a borehole a plurality of sensors are used to monitor the drilling process—including the functioning of the drilling components, the state of drilling fluids or the like in the borehole, the drilling trajectory and/or the like—characterize the earth formation around or in front of the location being drilled, monitor properties of a hydrocarbon reservoir or water reservoir proximal to the borehole or drilling location and/or the like.
To analyze the multitude of data that may be sensed during the drilling process, averaging or the like has often been used to make statistical assumptions from the data. Such averaging analysis may involve sampling sensed data periodically and then statistically analyzing the periodic data, which is in effect a looking backwards type analysis. Averaging may also involve taking frequent or continuous data and making assessments from averages/trends in the data.
Most analysis of data captured in the hydrocarbon industry is moving window analysis, i.e., a window of data is analyzed using the same assumptions/as a whole without consideration as to whether a change has occurred requiring separate analysis of different portions of the window of data. If small data windows are selected to try and avoid/mitigate the effect of changes on the data being analyzed, the small windows often give rise to large amounts of “noise” in the data. To avoid the moving window problem, Kalman filters have been used, however such filters can smooth out effects of changes, especially abrupt changes, on the data, and may provide for incorrect analysis of substantially steady state data in which changes are not occurring. In embodiments of the present disclosure, real-time analysis of the data is provided by identifying and/or processing changepoints in the data.
Data collected by various sensors in industrial processes are often very noisy. Such noise may cause real-time human interpretation of the data near impossible. Furthermore, calculations based on individual datapoints may amplify the effect of the noise.
In
As may be seen in
In a first phase 501, with low WOB applied, very low depth of cut is achieved. At low WOB most of the interaction between the bit 54 and rock occurs at the wear flats on the cutters. Neither the rock surface nor the wear flat will be perfectly smooth, so as depth of cut increases the rock beneath the contact area will fail and the contact area will enlarge. This continues until a critical depth of cut where the failed rock fully conforms to the geometry of the wear flats and the contact area grows no larger. Next, a second phase 503 corresponds to an intermediate amount of WOB. In this phase 503, beyond a critical depth of cut, any increase in WOB translates into pure cutting action.
The bit incrementally behaves as a perfectly sharp bit until the cutters are completely buried in the rock and the founder point is reached. The third phase 505 is similar to the first phase 501 in that little is gained from additional WOB. The response past the founder point depends on how quickly the excess WOB is applied. Applied rapidly, the uncut rock ahead of the cutters will contact with the matrix body of the bit and act in a similar manner to the wear flats in Phase I, so depth of cut will increase slightly with increasing WOB. Applied slowly, the cuttings may become trapped between the matrix and the uncut rock, so depth of cut may decrease with increasing WOB. Drillers may operate near the top of the second phase with the optimal depth of cut achieved without wasting additional WOB.
Depth of cut per revolution can be estimated by dividing ROP by RPM, so real-time drilling data can be plotted in the three dimensional {WOB, bit torque and depth of cut} space as illustrated in
A straight line in three dimensions has four unknown parameters, two slopes and the intersection with the x-y plane, i.e., WOB-torque plane in this case. These parameters could be estimated with a least squares fit to a temporal or spatial sliding window, e.g., last five minutes or last ten feet of data, but this would provide very poor fits in the vicinity of formation boundaries. For example, in
The PDC bit models have successfully been applied in the field by manual inspection of the data and breaking it up into homogeneous segments, e.g., in
Consider now again
In embodiments of the present disclosure, online data analysis may be provided by treating incoming data as being composed of segments between which are changepoints. The changepoints may be identified by the data analysis to provide for detection in changes in the automated industrial process. In certain aspects, a plurality of sensors or the like may provide a plurality of data channels that may be segmented into homogeneous segments and data fusion may be used to cross-correlate, compare, contrast or the like, changepoints in the incoming data to provide for management of the automated industrial procedure.
In an embodiment of the present disclosure, the data may be analyzed in real-time to provide for real-time detection, rather than retrospective, detection of the changepoint. This real-time detection of the changepoint may be referred to as online analysis/detection. In an embodiment of the present disclosure, the data from one or more sensors may be fitted to an appropriate model and from analysis of the incoming data with regard to the model changepoints may be identified. The model may be derived theoretically, from experimentation, from analysis of previous operations and/or the like.
As such, in an embodiment of the present disclosure, data from an automated industrial process may be analyzed in an online process using changepoint modeling. The changepoint models divide a heterogeneous signal, in an embodiment of the present disclosure the signal being data from one or more sources associated with the hydrocarbon related process, into a sequence of homogeneous segments. The discontinuities between segments are referred to as changepoints.
Merely by way of example, an online changepoint detector in accordance with an embodiment of the present disclosure, may model the data in each homogeneous segment as a linear model, such as a ramp or step, with additive Gaussian noise. Such models are useful when the data has a linear relationship to the index. In some embodiments, more complex models may be employed, e.g., exponential, polynomial and trigonometric functions. As each new sample (set of data) is received, the algorithm outputs an updated estimate of the parameters of the underlying signal, e.g., the mean height of steps, the mean gradient of ramps and the mean offset of ramps, and additionally the parameters of the additive noise (for zero-mean Gaussian noise, the parameter is the standard deviation or the variance, but for more general noise distributions other parameters such as skewness or kurtosis may also be estimated).
If so desired, a changepoint may be designated where the noise parameters are found to have changed. In some embodiments of the present disclosure, the tails of a distribution are may be considered in the analysis, as when analyzing the risk of an event occurring the tails of the distribution may provide a better analytical tool than the mean of the distribution. In an embodiment of the present disclosure, the changepoint detector may be used to determine a probability that the height/gradient/offset of the sample is above/below a specific threshold.
A basic output of the changepoint detector may be a collection of lists of changepoint times and a probability for each list. The most probable list is thus the most probable segmentation of the data according to the choice of models: G1, . . . , Gj.
The segmentation of the signal may be described using a tree structure (see
R 1 0 0 0 0 0 2 0 0
where this would indicate that the first six samples were generated by a step and that the remaining four samples were generated by a ramp.
Over time the tree grows and it is searched using a collection of particles each occupying a distinct leaf node. The number of particles may be chosen by the user/operator and around 20-100 is may be sufficient, however other amounts of particles may be used in different aspects of the present disclosure. Associated with a particle is a weight, which can be interpreted as the probability that the segmentation indicated by the path from the particle to the root (as in the example above) is the correct segmentation. The objective of the algorithm is to concentrate the particles on leaves that mean the particle weights will be large.
The segmentations are initialized by establishing a root node R 701. Next a data point is received from one or more input streams 703. In response the segmentation process spawns child segmentations 705, that reflect three different alternatives, namely, a continuation of the previous segment, a new segment with a first model, or a new segment with a second model (while we are in this example describing an embodiment with two models, ramp and step, in some embodiments additional models may be included). In an embodiment of the present disclosure, illustrated and described herein, the alternative models are ramp and step functions. As the root node does not represent any model, the first generation in the tree, reflecting the first data point, may start a new segment which is either a ramp, which is represented in the tree as 1, or a step, which is represented in the tree as 2.
In the example given above, the particle R 1 0 0 0 0 0 2 0 0 would produce three new child nodes with corresponding particles:
R 1 0 0 0 0 0 2 0 0 0
R 1 0 0 0 0 0 2 0 0 1
R 1 0 0 0 0 0 2 0 0 2
The first of which indicates a continuation of the step segment that begins with the 7th data point, the second, a new ramp, and the third, a new step.
Models are then created by fitting the data in the new segments to the designated models for the segments and models corresponding to existing segments are refit 706. For example, if a new ramp segment is to be created for a new child particle, the data in the segment is fit to that ramp. Naturally, when a new segment is created, the corresponding model that is assigned is merely a function that puts the model value through the new data point. However, for existing segments in which the segment encompasses a plurality of data points, the model parameters, e.g., the parameters defining the gradient and offset of a ramp, are re-evaluated. Some form of linear regression technique may be used to determine the linear function to be used to model the data in the segment as a ramp or step.
The segmentations produced are next evaluated, 707, using Bayesian Model Selection or the like to calculate weights indicative of how good a fit each segmentation is for the underlying data.
After the segmentations, creation of model functions, and corresponding models have been evaluated, i.e., having had weights assigned thereto, the tree is pruned by removing some particles from future consideration and to keep the particle population size manageable 709. The weights of the remaining particles are normalized 711.
Having evaluated the segmentations of the input data stream, the segmentations and corresponding models may be used in a process control program or in a further data analysis program 713. The use of the segmentations and corresponding models may take several forms. For example, the remaining segmentations may each be used to evaluate the input data in the calculation of a quantity used to compare against a threshold value for the purpose of alerting of a condition to which some corrective action should be taken. In such a scenario, a weighted average (weighted by the weights associated with each segmentation) may be computed to determine the probability that the condition has or has not occurred. This probability may either be used to trigger an action or suggest an action, or as input into further condition analysis programs.
The input data is processed by the CPU 350 according to instructions of a segmentation module 907 to produce segmentations 909 of the data as described herein. These segmentations contain segments defined by intervals of an index of the data stream, and models associated with those segments. The segments are fed into a calculation module to provide a result from the changepoint detector 901 that in turn is an input to the process control program 903. The result may be a probability of an event having occurred or some other interpretation of the input data (e.g., toolface or dogleg severity), or even a recommended action (e.g., suggested change in drillbit rotational speed or weight on bit to obtain better rate of penetration).
A more detailed view of
As noted in the discussion of
Consider by way of example again the inclination 401a and azimuth 403a input streams from
As the above paragraphs illustrate, there are many processes relating to the drilling of a hydrocarbon well or operation of any other hydrocarbon related procedure in which data that is indicative of operating environment is subject to difficult interpretation due to noise or other factors, yet where that data and changes in the operating environment that the data reflects may have some effect on how an operator of the drilling of the hydrocarbon well or operation of the hydrocarbon related procedure would set parameters for optimal process performance or where the such data, if modeled accurately, may be very useful in automation of aspects of the creation/operation of the hydrocarbon well.
We now turn to three examples of the use of the changepoint detector 901 in conjunction with a control program 903.
In a first example, the changepoint detector 901 is used to determine kicks encountered in a drilling operation. In the process of drilling a wellbore, a drilling fluid called mud is pumped down the central opening in the drill pipe and passes through nozzles in the drill bit. The mud then returns to the surface in the annular space between the drill pipe and borehole wall and is returned to the mud pit, ready for pumping downhole again. Sensors measure the volume of mud in the pit and the volumetric flow rate of mud entering and exiting the well. An unscheduled influx of formation fluids into the wellbore is called a kick and is potentially dangerous. The kick may be detected by observing that flow-out is greater than flow-in and that the pit volume has increased.
Next the calculation module 911 uses the segmentations to calculate a desired probability value, 103. In the present example, that probability is the probability of the ramp of the pit volume data exceeds a given threshold, namely, for the purposes of the example, 0.001 m3/s. That result is obtained by calculating the gradient from the models corresponding to each active segmentation, 105, and computing a weighted average over those results based on the weight associated with each segmentation. If one of the possible segmentations under consideration represented a continuation of the model from t=800 which has a very low ramp or even a step, once the volume data starts increasing at t=1300 (and similarly at t=1700) that model would be a poor fit and have a very low weight associated with it. Therefore, at t=1300, the weighted average calculation would give the segmentation that includes a ramp beginning at t=1280 a very large weight and that segmentation would have a high influence on the weighted average calculation and the final result.
In
The changepoint detector of
To take the additional information available from drilling process into account, the output from the changepoint detector may be fed into additional analysis software for fusing the changepoint detector output with such additional information. For example, the changepoint detector output may be one input to a Bayesian Belief Network used to combine that output with detection of changes in rig state, i.e., the current state of the drilling rig.
As described previously, for example in conjunction with
In accordance with some embodiments of the present disclosure, each of the plurality of the changepoint detectors 901 may process for the segment(s) with positive gradient the probability that the influx volume is greater than a threshold volume T. In
The two continuous probabilities p(vol>T) 121a and 121b may be entered into a BBN 123, specifically into a Pit Gain node 131 and an Excess Flow node 133. In an embodiment of the present disclosure, a condition Well Flowing node 135 may describe the conditional probabilities of an existence of more fluid exiting the wellbore being drilled in the automatic drilling process than entering the wellbore. Such a condition occurring in the drilling process may cause PitGain and ExcessFlow signatures in the surface channels. The Well Flowing node output 135 may be a result of a change in the drilling process, i.e., a recent change in rig state, node 137. For example, the circulation of fluid in the wellbore may not be at a steady-state due, for example to switching pumps on/off or moving the drilling pipe during the drilling process. Deliberate changes in the drilling process, such as changing pump rates, moving the drill pipe, changing drilling speed and/or the like may be referred as rig states. Detection of change of rig state is described in U.S. Pat. No 7,128,167, System and Method for Rig State Detection, to Jonathan Dunlop, et al., issued Oct. 31, 2006.
In an embodiment of the present disclosure, a rig state detector 345 may be coupled with the drilling process system. The rig state detector 345 may receive data from the components of the drilling system, the wellbore, the surrounding formation and/or the like and may input a probability of recent change in rig state 137 to the changepoint detectors. In this way, the changepoint detectors 901 may determine when a detected changepoint results from the recent change in rig state 137. For example, in
As depicted in
In an embodiment of the present disclosure, the online determination of the kick 353 may cause an output of an alarm for manual intervention in the drilling process, may cause a control processor to change the automated drilling process and/or the like, for example, the detection of a kick 353 may be reported on a control console connected to the central surface processor 96. In certain aspects, data concerning the wellbore, the formation surrounding the wellbore, such as permeable formation in open hole with pore pressure greater than ECD may be input to the changepoint detector and may allow for greater accuracy in detection of the kick 353. In some aspects of the present disclosure, if fluid is being transferred into the active mud pit 78, data concerning such a transfer or addition 356 may be provided to the changepoint detector as it may cause the Pit Gain 330 but not Excess Flow 335. In such aspects of the present disclosure, by inputting the transfer or addition 356 to the changepoint detector(s), mistaken detection of the Kick 353 may be avoided.
In
If a kick is suspected a flow check is performed, whereby the mud pumps are stopped and any subsequent flow-out can definitively confirm a kick. To control a kick, the drillstring is lifted until a tool joint is just above the drill floor and then valves called blowout preventers are then used to shut-in the well. The influx is then circulated to the surface safely before drilling can resume. Small influxes are generally quicker and more simple to control, so early detection and shut-in is extremely desireable. Automating the above process should consistently minimize the non-productive time.
Other processes the present disclosure may be applied to in the hydrocarbon industry include: stuck pipe, lost circulation, drill bit stick-slip, plugged drill bit nozzles, drill bit nozzle washout, over- or under-sized gauge hole, drill bit wear, mud motor performance loss, drilling-induced formation fractures, ballooning, poor hole cleaning, pipe washout, destructive vibration, accidental sidetracking, twist-off onset, trajectory control of steerable assemblies, rate-of-penetration optimization, tool failure diagnostics and/or the like.
Turning now to a second example use of the changepoint detector 901, namely the application thereof to optimize the rate-of-penetration in drilling processes.
Consider again
Projecting the three dimensional fit onto the WOB-depth of cut plane gives a linear equation linking WOB, RPM and ROP. This can be rearranged to give ROP as a function of WOB and RPM, as shown by the contours in
The coefficients of the bit/rock model allow various constraints to the drilling process to be expressed as a function of WOB and RPM and superimposed on the ROP contours as is illustrated in
the ROP at which cuttings are being generated too fast to be cleaned from the annulus, 141,
the WOB that will generate excessive torque for the top drive, 143,
the WOB that will generate excessive torque for the drill pipe, 144,
the WOB that exceeds the drill bit specification for maximum weight on bit, 145,
the RPM that causes excessive vibration of the derrick, 147.
The region 149 below these constraints is the safe operating envelope. The WOB and RPM that generate the maximum ROP within the safe operating envelope may be sought and communicated to the driller. In some embodiments, the WOB and RPM may be passed automatically to an autodriller or surface control system.
Examination of the boundaries of the safe operating window 149 reveal that the highest ROP within the safe operating window may be found at the intersection of the hole cleaning plot 141 and the top drive torque plot 143, referred to herein as the optimal parameters 151. For the sake of example, consider the drilling operation current RPM and WOB being located at 80 rpm and 15 klbf (153), respectively, with an ROP of approximately 18 ft/hr. The ROP at the optimal parameter combination 151, on the other hand, is approximately 90. Thus, a driller increasing the RPM and WOB in the direction of the optimal parameters would improve the ROP. In an embodiment, an ROP optimizer suggests an intermediate combination of RPM and WOB, e.g., the parameter combination approximately ½ the distance 155 between the current parameter combination 153 and the optimal combination 151.
The data that defines the ROP contours and the parameters for the safe operating window are continuously reported from sensors on the drilling apparatus. These sensors may either be located at the surface or in the drill string. If located at the surface, some filtering and preprocessing may be used to translate the measured values to corresponding actual values encountered by the drillbit and drillstring.
The continuous stream of data is modeled using the PDC model of
The data is segmented using the changepoint detector 901 and fit to appropriate linear models corresponding to each segment in the manner discussed hereinabove. The different colors illustrated in the various graphs 161 through 167 represent different segments, respectively. By examining the plots against depth index of graphs 161 it will be appreciated that in this example, blue represents the first segment, red, the second, and green, the current segment. As will be appreciated from the depth of cut versus WOB graph 163, the linear relationship expected between these from the PDC model has changed dramatically in the course of the drilling operation corresponding to the data points plotted in
The safe operating envelope and drilling contours window 169 contains a display of the safe operating envelope 149, the current parameters 153, the optimal parameters 151 and recommended parameters 155 corresponding to the current segmentation model.
The graphic user's interface 157 may be reported on a control console connected to the central surface processor 96.
Having determined the best segmentation and the models for the current segment these models are used to determine the ROP contours corresponding to the PDC model fit to the data points in the current segment and the safe operating envelope corresponding to the drilling constraints corresponding to the current segment, 173.
The ROP contours and safe operating envelope are used to determine the optimal ROP contour inside the safe operating envelope and the WOB and RPM that correspond to that optimal ROP contour, 175.
A mud motor or turbine is sometimes added to the bottomhole assembly 56 that converts hydraulic power from the mud into rotary mechanical power. With such an assembly, bit RPM is function of surface RPM and mud flow rate, and consequently, the optimum ROP is a function of surface RPM, WOB and flow rate; the algorithm corresponding algorithm therefore suggests these three drilling parameters to the driller. The relationship between flow rate and the RPM of the shaft of the motor/turbine is established by experimentation and published by most vendors. In some embodiments by measuring rotor speed downhole, this relationship may be inferred in real-time. Given either of these relationships, the algorithm above can be extended to give an equation of ROP as a function of surface RPM, WOB and flow rate. Useful extra constraints to add are:
A recommended set of new drilling parameters, e.g., RPM and WOB, that move the current parameters towards the optimal parameters is provided, 177, either to a human operator or to an automated drilling apparatus.
The above-described technology for optimizing rate-of-penetration is applicable to other structures and parameters. In some embodiments the technique is applied to roller cone bits using appropriate models for modeling the drilling response of a roller cone bit. In some embodiments, the above-described mechanisms are applied to drilling processes that include additional cutting structures to the bit, such as reamers, under-reamers or hole openers by including a downhole measurement of WOB and torque behind the drill bit. In some embodiments, a second set of measurements behind the additional cutting structure is included.
In some embodiments, a bit wear model could be added to allow the bit run to reach the casing point without tripping for a new bit.
Turning now to a third example of the use of a changepoint detector 901 in the realm of industrial automation, namely, in directional drilling of wells into targeted subterranean formations. Calculation of wellbore curvature (also known as dogleg severity (“DLS”)) and direction (also known as toolface) are very useful in the field of Directional Drilling. The directional driller uses curvature and direction to predict whether or not a target will be intersected. In an embodiment of the disclosure, curvature and direction estimates are provided continuously during a drilling operation on the order of, e.g., every ¼ foot, every ½ foot, every foot, etc., to allow a driller the opportunity to take corrective action during the drilling operation if the wellbore is deviating off plan. The directional driller thus is able to evaluate deflection tool performance using higher resolution curvature and direction estimates.
The curvature and direction can be used to determine formation effects on directional drilling. In particular, if the changepoint detector indicates a changepoint at a formation bed boundary, the new formation will have a different directional tendency from the previous formation. The resultant curvature and direction can be used to study and evaluate the effects of surface driving parameters such as weight on bit and rpm on directional performance. A detailed understanding of how current deflection tools deviate a well can be used to engineer future tools. Finally, a continuous curvature and direction of the curvature may be used in autonomous and semi-autonomous directional drilling control systems.
Segmentation 184 results in a number of different segmentations of the input azimuth and inclination data. Each is associated with a particle in a tertiary tree as illustrated in
To calculate the azimuth and inclination values at a depth location MD2, using a segmentation p, the following formula is used:
DLS
p
=A COS(COS(I2−I1)−SIN(I1)*SIN(I2)*(1.0−COS(A2−A1))/(MD2−MD1)) (1)
y=COS(A2−A1)*SIN(I2)*SIN(I1) (2)
GTF
p
=A COS(COS(I1)*y−COS(I2))/(SIN(I1)*SIN(A COS(y))) (3)
where:
Weighted averages are then calculated from the per-segmentation calculated values for dogleg severity and toolface, 189, using the following formulas:
where Segmentations is the set of all active segmentations,
Weightp is the weight associated with a particular segmentation p.
The resulting dogleg severity (“DLS”) and toolface (“TF”) values are then reported to a directional driller who may use these values to assess the effect of surface driven parameters such a weight-on-bit and RPM on the directional drilling process, 191. The driller may then adjust these parameters to improve the trajectory of the wellbore with respect to a desired target. In some embodiments, the resulting dogleg severity (“DLS”) and toolface (“TF”) values are input into an automated drilling system that automatically adjusts the surface driven parameters based on these values to improve the wellbore trajectory with respect to a desired target. The resulting dogleg severity (“DLS”) and toolface (“TF”) values may be reported on a control console connected to the central surface processor 96.
As discussed hereinabove, in some embodiments, the changepoint detector output may be fed into analysis software, for example, in the form of a Bayesian Belief Network (BBN). By way of example, in one embodiment, the combination of a changepoint detector, a signal processing system, and an expert system are used to analyze sensor data for artificial lift operations using electrical submersible pumps (ESPs) or progressive cavity pumps (PCPs). Such pumps are used in many oil fileds to improve oil production. Downhole and surface gauges as well as sensor data provided from control equipment may be used in such a system to assess pump performance to detect equipment failures or stress conditions that may lead to equipment failures. The amount and complexity of the data from artificial lift operations using ESP or PCP easily overwhelm a human operator who may therefore miss such failure or potential failure events in the mass of data.
The minute-by-minute measurements made by these pump systems provide information about the pump behavior and performance (e.g., pump efficiency), as well as indications of impending problems (e.g., upthrust versus downthrust, gas ingestion, mechanical imbalance). However, using the available data in an efficient and effective way to assess pump performance and problems is a challenge because the volume of high-frequency data overwhelms the capacity of most direct methods based on human observation of and response to the data.
The pump assembly 241 also contains a downhole monitoring tool 255 which includes any combination of pressure, temperature and accelerometers for measuring intake pressure, motor temperature and motor vibration (in x, y, and z axis), respectively, as well as a discharge pressure sensor 257 which measures pump discharge pressure. At the wellhead, surface sensors 259 are provided for measuring wellhead pressure and wellhead temperature. These measurements are provided as time-indexed data streams to the control system 245. The foregoing sensors and physical properties for which measurements are taken are provided as examples. Other sensors measuring other physical properties may also be included in addition to or as alternative to these examples.
Herein, ESP failures, i.e., equipment breakdown of some sort, and ESP Stress Conditions, which may ultimately lead to equipment breakdown, are referred to equipment events.
ESP Failures. An ESP may suffer from many types of failures, for example, related to the pump 253, to the motor 247, or to a sensor 255, 257, 259. Timely failure detection is very desireable so as to allow an operator to take appropriate actions to correct the failure and to prevent the failure from causing additional problems. The main failures are downhole mechanical failures and gauge faults.
ESP Stress Patterns. The second type of equipment event is stress pattern or stress condition. Stress patterns are very interesting events to detect, as they can anticipate downhole mechanical failures. By removing early stages of stress patterns, a failure may be prevented, thus increasing the life expectancy of the pump and avoiding other costly operation delays. Some stress patterns are low flow, deadhead, and gas ingestion.
Low flow. An ESP running below the minimum speed induces a low flow (
Deadhead. Deadheads are any restrictions above the ESP 241. Two cases are possible: the restriction can be before or after the wellhead. The wellhead pressure is a parameter to determine if the restriction is before or after the wellhead. In those two cases, motor temperature increases because there is no fluid flow to cool the motor, resulting in motor damage. Without corrective action, the ESP would shut down by the constraint threshold on the motor temperature.
Gas ingestion. Gas issues occur when fluid level drawdown approaches the pump intake and intake pressure is lower than the bubblepoint. It is very difficult for a pump to evacuate gas as impellers have less effect on gas than on liquid. The volume of this bubble of gas can change with time. Therefore, the volume for fluid is changing irregularly and consequently less fluid would pass through the pump. This induces unstable intake pressure, unstable average current, reduction of the flow rate, and unstable motor temperature.
Typically the control system 245 is a computerized system having a processor, data stores for storing data and programs, and user interface devices such as to allow a user to receive diagnostic information from the control system and to allow the user to enter input parameters to the control system. Programs such as the signal processing module 301 and the expert system 305 may be stored in the control system 245 data stores and provide instructions to the control system 245 processor to receive and manipulate data streams from the sensors connected to the control system.
Segmentation. The input streams from the sensors 255, 257, and 259, as well as input from the variable frequency drive 243 are input into the signal-processing module 301, 421. The data streams are segmented (as described herein above), 423.
In a typical segmentation, linear models are used to model the input data either as steps or as ramps. In a general sense:
y=G*0 (1)
with:
θ: the parameter vector for the current segment
Based on equation (1), many models can be created simply by which variables are included in the matrix G. For example, if the regressed variable is time t, we have:
Another possible model is cross-variable regression: G=[1 x], where x is another operations property. For example, a correlation may be made between intake pressure and average current draw, i.e., y is intake pressure and x is average amps.
High-Level Questions. The segmentation may be used to determine specific information derived from the underlying data: general trends, noise level, convergence of operations properties, etc. This high-level information is referred to herein as high-level questions. The high-level questions are answered from the segmentation and statistical descriptors of the distribution of data values for all segmentations of the data stream, e.g., variance, mean, standard deviation. Thus, following the segmentation of the data streams, the high-level questions are answered, 425. In some embodiments, there are three fundamental high-level questions: Basic Tendency, Noise Level, and Correlation Between Properties.
Basic Tendency. The basic tendency question is a determination of the direction in which an operations property is tending. In an embodiment, the tendency is classified into five categories: decrease strongly, decrease, steady, increase, and increase strongly. The signal processing module 301 produces probability estimates for each of these categories.
Threshold levels are set to reflect where a sample would fit with respect to the reference distribution, 523. The thresholds are typically set as a percentage of the reference level. To segment tendency into the five categories, two threshold levels are set above and below the reference, e.g.,
Thrld=10%·Ref
ThrldStrg=40%·Ref
Thus, if the reference level Ref is changed, the thresholds are similarly changed. The percentages may be set to reflect the level of detail that is desired. For example, a broken shaft creates a substantial variation. Thus, to detect a broken shaft, the threshold would be set very high (e.g., 40% of Ref for increase strongly). On the other hand, to highlight pump wear, the threshold would be set relatively low (e.g., 5% of Ref for the increase category).
Next, probabilities for the tendency categories (decrease strongly, decrease, steady, increase, increase strongly) are computed, 525, according to the following formulas:
p(decrease strongly)=p(yn<Ref−ThrsldStrg)
p(decrease)=p(Ref−ThrsldStrg<yn<Ref−Thrsld)
p(steady)=p(Ref−Thrsld<yn<Ref+Thrsld)
p(increase)=p(Ref+Thrsld<yn<Ref+ThrsldStrg)
p(decrease strongly)=p(Ref+ThrsldStrg<yn)
An example distribution for yn is illustrated in
As discussed above, a reference period is selected and a probability distribution is determined for that time series 521. This is oftentimes a stable period of suitable duration. Then the current statistical model of the signal is compared to the corresponding reference to determine if the signal is above, below, correlated to the reference etc. d, 525. Implied in this is that when any changes are made, a new stable period may be reached in which the data input is in a “reference state.” One example of that is whenever the frequency is changed for an ESP. After a frequency change it takes some time to reach a new reference state. During that period the changepoint detector is limited because it does not have the knowledge of the reference to compare the signal to. In one embodiment, to compensate for this, the signal processing module 301 estimates the new reference based on known physics laws or mathematical approximations. That allows the control system to continue to operate until a new reference is determined by the changepoint detector.
Noise Level. The noise level question, which measures the variation in the signal, is determined from comparison of signal instability against two thresholds, ThrldUns and ThrldUnsHigh. The thresholds depend on signal features that are expected to remain stable. Furthermore, sensor resolution should be taken into consideration when setting noise thresholds. Thus, three levels are defined for the answers to the noise level question: stable, unstable, and highly unstable. These are determined as follows:
p(stable)=p(yn<ThrldUns)
p(unstable)=p(ThrldUns<yn<ThrldUnsHigh)
p(highly unstable)=p(ThrldUnsHigh<yn)
Correlation Between Operations Properties. The correlation question is an inspection of the correlation between two properties that are being measured (or derived from measurements) by the sensors 255, 257, 259, or provided by the variable frequency controller 243. In an embodiment, the correlation is a linear relationship between the two quantities. However, other mathematical relationships are possible as well as the involvement of multiple operations properties in the calculation.
For purpose of illustration, consider the linear relationship between to properties X and Y that are monitored, then the model is:
Y=a·x+b
The coefficients in the above equation fall within some distribution, e.g., a normal distribution N(a0,σa2) with a mean ao and a standard deviation σa2:
a˜N(a0,σa2); b˜N(a0,σa2)
The correlation-between-properties question deals with comparing the coefficients against some thresholds thereby producing probability values for categories of correlation, e.g., negative correlation, positive correlation and no correlation:
p(negative correlation)=p(a<−Threshold)
p(positive correlation)=p(a>Threshold)
p(no correlation)=p(−Threshold<a<Threshold)
The Threshold may be set separately for each channel. The foregoing approach allows an analysis of dependencies between operations properties. If the correlation drops between a cause and a consequence, that occurrence reveals that the causal link is broken.
Thus, the output from the correlation question is the probability values for each of the categories negative correlation, positive correlation and no correlation.
To summarize, at the conclusion of the a computation of probabilities for the high-level questions for each possible answer, i.e., the categories for the basic tendency, the noise level, or the correlation between properties questions, has a probability value associated therewith. These probability values for each question add to 1.0. For example, for the basic tendency illustrated in
p(decrease strongly)=0.28
p(decrease)=0.46
p(steady)=0.13
p(increase)=0.11
p(decrease strongly)=0.02
Not all of the high-level questions have to be used in a control system for detecting EFS failures of stress conditions.
The probability values for the various answers to the high-level questions are input (
Most physical phenomena can be observed using a certain number of variables and some specific conditions associated to it categorized with corresponding signatures. For instance in the particular world of ESPs, operating in deadhead condition will decrease the electrical current drawn by its motor, increase the pressure at the outlet of the pump as well as the differential pressure across it (among other things, this is just given here to simplify as an example). This can be programmed in a Bayesian network to map the statistical dependencies between a stress pattern (output node; e.g., deadhead) and measured variables (input nodes; e.g., for deadhead: current, discharge pressure and pump pressure differential). Such a network outputs a strong probability of deadhead when the probabilities for current to decrease and pressures to increase are high, but that deadhead probability would drop if any of the given operating properties behaves differently (drops or remains steady for instance).
In Bayesian inference, a prior probability distribution, often called simply the prior, is a probability distribution representing knowledge or belief about an unknown quantity a priori, that is, before any data have been observed P(A). Thus, certain probabilities may be associated with the physical properties of an EFS operation and the likelihood that certain equipment events are occurring. However, if a particular condition is observed or a combination of particular conditions are observed, those conditions impact the probabilities of particular equipment events.
As discussed above, a number of operating properties are monitored and modeled using the segmentation algorithms and the high-level questions. The probabilities for each high-level question and their combined impact on probabilities for equipment events are modeled in the BBN.
A Bayesian belief network reproduces different states of a structure and explains how those states are connected by probabilities. In an embodiment, this kind of network is used to model an uncertain reality and to take intelligent decisions that maximize the chances of a desirable outcome.
Many physical phenomena interact, and for many, there is extensive domain knowledge about their possible behavior. The Bayesian theory is a suitable framework to take advantage of these priors, i.e., domain knowledge in regard to interaction between operating properties and equipment events. As provided in An Introduction to Bayesian Networks, Jensen, F. V., (1996), Springer-Verlag, ISBN 978-0387682815, incorporated herein by reference, “[c]ontrary to most other expert system techniques, a good deal of theoretical insight as well as practical experience is required in order to exploit the opportunities provided by Bayesian Network.” In the case of ESPs, for example, adding physical priors inside the Bayesian network relies on domain knowledge in the field of ESPs.
A Bayesian network is:
Furthermore, nodes are linked. For example, a parameter may be sprinkler_has_been_on. That would also cause wet grass, thereby causing the impact of wet grass on the conclusion that rainy weather has occurred to decrease. Conversely, if rainy weather has been observed, that would decrease the probability of sprinkler_has_been_on as sprinklers are often not deployed during rainy days.
Because some situations can be complicated, in some embodiments intermediary nodes are used to manage different levels of abstraction.
Each of the nodes in the BBN are linked through conditional probability tables. The conditional probability tables may be manually populated when domain knowledge provides sufficient information for doing so. For instance someone could record continuously the temperature, pressure etc. in a specific place and records whenever it rains. Processing this measured data with a changepoint detector may provide the input channels for a Bayesian network. Once the signals are segmented, it is possible to compare the signals to what is defined as a standard condition (for example the average statistical distribution of each parameter during a day, which can by the way be determined in parallel in a changepoint detector). The comparison is performed between the modeled value distribution (and not the raw value) and the standard value distribution to give a statistical input (for instance the measured value is statistically 10% higher than the standard value). Then a Bayesian network may be built to draw the dependencies between different input nodes (here the measured parameters) and output nodes (here the probability for rain event to happen which will be deterministic in that example). It is then possible to train the designed network using these inputs/output. Once the process is complete, the input of new data will allow the probability of oncoming rain to be inferred.
In some embodiments, conditional probability tables may be constructed mathematically. Consider the example of
with
DP for Discharge Pressure
The foregoing equation that pump worn (PW) and □ ImpellerInactive are two aggravating factors for the decrese in discharge pressure. Furthermore, is a more serious factor than PW since it has twice the weight and makes the discharge pressure decrease normally and strongly. Moreover, W0 is an additional parameter measuring the accuracy of the equations; for example, in this case, Wimp/W0 measures the influence of the impellers-inactive event on discharge pressure.
The following table illustrates the combination of answers of high-level questions based on the raw data that would indicate DeadHead1 (DH1), DeadHead2 (DH2), and DeadHead3 (DH3), respectively:
At any given time during the operation of an ESP 241, the signal processing system 301 accepts input from the sensors 255-250 and the variable frequency controller 243 and answers the high-level questions, thereby producing probability values for various conditions 809a-809i. For example, in the example of
The underlying effort is to train the Bayesian network to fulfill the posteriori probabilities tables. It can be done either manually or automatically if sufficient training data is available. For example, the Netica program from Norsys Software Corporation allows for probability tables to be determined based on collected data in the form of tab-delimited data files.
In the example above, one can see that the probability for a DH2 is 99.7% while the other stress patterns probabilities are close to 0. The case is presented in a deterministic way (100% probabilities as an input) but it works similarly with an actual probability from the distribution determined with the changepoint detector.
From the foregoing it will be apparent that a technology has been presented herein that provides for a mechanism for real-time or near real-time determination of changes in industrial processes in a manner that allows operators of such processes, which operators may be human controllers, processors, drivers, control systems and/or the like to make note of/detect events in the operation of a hydrocarbon associated procedure, take corrective action if necessary, change operation of the procedure if desired and/or optimally operate the processes in light of the changes in the operating environment, status of the system performing the procedure and/or the like. The technology presented provides for a mechanism that is noise tolerant, that may be readily applied to a variety of hydrocarbon associated processes, and that is computationally inexpensive.
The solutions presented may either be used to recommend courses of action to operators of industrial processes or as input in process automation systems. While the techniques herein are described primarily in the context of exploration for subterranean hydrocarbon resources through drilling, the techniques are applicable to other hydrocarbon related processes, for example, the exploration for water, transport of hydrocarbons, modeling of production data from hydrocarbon wells and/or the like.
In the foregoing description, for the purposes of illustration, various methods and/or procedures were described in a particular order. It should be appreciated that in alternate embodiments, the methods and/or procedures may be performed in an order different than that described.
It should also be appreciated that the methods described above may be performed by hardware components and/or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions, to perform the methods. These machine-executable instructions may be stored on one or more machine readable media, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable media suitable for storing electronic instructions. Merely by way of example, some embodiments of the disclosure provide software programs, which may be executed on one or more computers, for performing the methods and/or procedures described above. In particular embodiments, for example, there may be a plurality of software components configured to execute on various hardware devices. In some embodiments, the methods may be performed by a combination of hardware and software.
Hence, while detailed descriptions of one or more embodiments of the disclosure have been given above, various alternatives, modifications, and equivalents will be apparent to those skilled in the art without varying from the spirit of the disclosure. Moreover, except where clearly inappropriate or otherwise expressly noted, it should be assumed that the features, devices and/or components of different embodiments can be substituted and/or combined. Thus, the above description should not be taken as limiting the scope of the disclosure, which is defined by the appended claims.
The present document is based on and claims priority to U.S. Provisional Application Ser. No. 61/972,075, filed on Mar. 28, 2014, which is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2015/022867 | 3/27/2015 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 61972075 | Mar 2014 | US |
