CN114846220B - Automatic kick and loss detection - Google Patents

Abstract

A method for monitoring and controlling a mud flow system in a drilling rig, comprising measuring a working mud volume in a working mud pit and a non-working mud volume in a non-working mud pit, modeling the modeled working mud volume in the working mud pit, determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume, detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance, and automatically detecting downhole increases and losses based on the mud volume balance.

Description

Automatic kick and loss detection

Cross Reference to Related Applications

The present application claims priority from U.S. provisional patent application serial No. 62/929064 filed on 10/31 in 2019, the entire contents of which are incorporated herein by reference.

Background

Downhole fluid increase and loss detection provides data related to the safety and integrity of drilling operations. One way to achieve this is by monitoring the flow and using a flow model to infer the downhole increase and loss conditions. However, such monitoring and modeling can be expensive to implement, as it may rely on measurements that are not readily available. Other methods of monitoring fluid increase and loss rely on pit volume measurements (i.e., mud volume/level in active pits) or flow paddle measurements. While pit volume methods are generally easier to deploy, they may provide a kick (kick) detection capability that is less efficient and less reliable. In particular, there may be a relatively long delay between the downhole mud addition/loss event and the responsive change in pit mud level. This delay occurs in part because the surface equipment acts as a buffer between the pit and the well, slowing the pulses of mud flow, ultimately changing the pit mud level.

Furthermore, pit volume monitoring may be relatively inflexible. For example, a pump down may be fingerprinted and used as a baseline reference to infer whether the subsequently observed change in pit mud volume is normal. If the flow conditions in the subsequent time period are different from those in the comparative sample, a reliable conclusion of the state of failure cannot be drawn from the change in mud volume. Furthermore, this technique relies on periods of interest that actually reflect normal operation; if an abnormal operation for a period of time is considered a baseline (baseline) or normal operation, then a subsequent exception may be missed, or normal operation may be falsely marked as an exception.

Further, pit volume monitoring techniques may interpret normal surface events, such as transitions between pits, as downhole increases or losses, resulting in false determinations of downhole events (e.g., false kick alarms). To address such surface events, current practice requires operators at the drilling site to record real-time comments on mud logging reports after they are discussed with mud engineers when they become aware of, for example, a shift. Thus, the operator may deactivate the add alarm upon confirming that a transition to the work system has occurred. However, this practice relies on human users to observe and manually enter error-free information into the log in a timely manner. Furthermore, the operator's reaction may require time because the operator may be responsible for multiple other tasks simultaneously, resulting in delays that may result in long false kick alert periods.

Disclosure of Invention

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

Embodiments of the present disclosure provide a method for monitoring and controlling a mud flow system in a drilling rig, comprising measuring a working mud volume in a working mud pit and a non-working mud volume in a non-working mud pit, modeling the modeled working mud volume in the working mud pit, determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume, detecting mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance, and automatically detecting downhole increases and losses based on the mud volume balance.

Embodiments of the present disclosure also provide a computing system including one or more processors and a storage system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include measuring a volume of working mud in the working mud pit and a volume of non-working mud in the non-working mud pit, modeling a modeled working mud volume in the working mud pit, determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume, detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance, and automatically detecting downhole increases and losses based on the mud volume balance.

Embodiments of the present disclosure also provide a non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations. The operations include measuring a volume of working mud in the working mud pit and a volume of non-working mud in the non-working mud pit, modeling a modeled working mud volume in the working mud pit, determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume, detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance, and automatically detecting downhole increases and losses based on the mud volume balance.

Drawings

The invention will be better understood from a reading of the following detailed description taken in conjunction with the drawings. It is emphasized that, in accordance with the standard practice in the industry, various features are not drawn to scale. In fact, the dimensions of the various features may be arbitrarily increased or reduced for clarity of discussion.

FIG. 1 shows a schematic diagram of an example of a drilling system according to an embodiment.

FIG. 2 illustrates a control block diagram of a mud system of a drilling system according to an embodiment.

FIG. 3 illustrates a conceptual diagram of a mud system according to an embodiment.

FIG. 4 illustrates a flow chart of a process for modeling mud flow in a mud system according to an embodiment.

FIG. 5 illustrates a graph of flow and working volume (based on mud volume in a work pit) according to an embodiment.

Fig. 6 shows a conceptual diagram of an embodiment of a mud system that includes a downlink.

FIG. 7 shows a graph of non-working mud volume, flow, working mud volume and mud volume balance as a function of time according to an embodiment.

FIG. 8 illustrates a flow chart of a process for detecting and accounting for mud diversion according to an embodiment.

FIG. 9 illustrates a flow chart of a method for controlling a mud system according to an embodiment.

FIG. 10 shows a graph of measured and theoretical (calculated/modeled) working mud volumes, as well as real-time and adjusted mud volume balances, according to an embodiment.

FIG. 11 illustrates a schematic diagram of a computing system, according to an embodiment.

Detailed Description

Illustrative examples of the subject matter claimed below will now be disclosed. In the interest of clarity, not all features of an actual implementation are described in this specification. It will be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions may be made to achieve the developers' 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, even if complex and time-consuming, would be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

Furthermore, as used herein, the article "a" is intended to have its ordinary meaning in the patent art, i.e., "one or more" herein, the term "about" when applied to a value generally means within the tolerance of the apparatus used to produce the value, or in some examples, means plus or minus 10%, or plus or minus 5%, or plus or minus 1%, unless otherwise explicitly stated. Furthermore, the term "substantially" as used herein refers to, for example, a majority, or almost all, or an amount ranging from about 51% to about 100%. Moreover, the examples herein are illustrative only and are for discussion purposes and not intended to be limiting. Any use of the term "or" is meant to be non-exclusive, e.g., "a or B" means A, B or both a and B

FIG. 1 shows a schematic diagram of an example of a drilling system 100 according to an embodiment. The drilling system 100 may be disposed at a wellsite, which may be an onshore or offshore wellsite, and the drilling system 100 may include any combination of the various elements described herein.

The drilling system 100 may form a borehole 11 in a subterranean formation by rotary drilling using a drill string 12 suspended within the borehole 11. The drilling system 100 may include a platform and derrick assembly 10 positioned over a borehole 11. The platform and derrick assembly 10 may include drilling equipment such as a rotary table 16, kelly 17, hooks 18, and/or rotary joints 19. The drill string 12 may be rotated by a rotary table 16, the rotary table 16 engaging a kelly 17 at an upper end of the drill string 12. The drill string 12 may be suspended from a hook 18 attached to the traveling block by a kelly 17 and a swivel 19, which allows the drill string 12 to rotate relative to the hook 18. In another embodiment, a top drive system may be utilized in place of the rotary table 16 and/or kelly 17 to rotate the drill string 12 from the surface above the borehole 11. The drill string 12 may be assembled from a plurality of sections 125, and the sections 125 may be or include tubing and/or drill collars.

The drilling system 100 may also include a BHA120 connected to the lower end of the drill string 12. BHA120 may include logging while drilling (hereinafter "LWD") tool 130, measurement while drilling (hereinafter "MWD") tool 140, motor 150, drill bit 122, or combinations thereof. Drilling system 100 may also include a drilling fluid or "mud" 26 stored in a work pit 27A formed at the wellsite. It should be appreciated that the pit may be a structure dug into the ground, for example, suitably lined to prevent seepage. In other embodiments, the wells may be separate containment structures, such as cans or other containers.

The pump 29 of the drilling system 100 may deliver the mud 26 from the work pit 27A to the interior of the drill string 12, extending through ports in the rotary joint 19 to the borehole 11, which may cause the mud 26 to flow downward through the drill string 12 and the BHA 120, as indicated by arrow 8. Mud 26 may be discharged through ports in the drill bit 122 and then circulated upwardly through the annular space between the exterior of the drill string 12 and the wall of the borehole 11, as indicated by arrow 9. The mud 26 may lubricate the drill bit 122 and/or may carry formation cuttings to the surface adjacent the borehole 11. Mud 26 may be returned to the pit 27A for cleaning and recirculation.

Drilling system 100 may also include one or more non-working pits 27B. Non-work pit 27B may contain a reserve of slurry 26, which may be supplied ("diverted") to work pit 27A periodically, on demand, etc. For example, formation permeability, surface loss, and/or downhole events may slowly decrease the amount of mud 26 in the work pit 27A, so mud 26 from non-work pit 27B may be supplied to the work pit 27A (e.g., via one or more transfer pumps) to re-supply the work pit 27A. Further, in some cases, the composition of the slurry 26 may be changed by transferring the slurry 26 from the non-work pit 27B to the work pit 27A.

The drilling system 100 may include one or more vibrators 155. Vibrator 155 may receive mud that has been circulated up from borehole 11 and may remove large cuttings therefrom. Vibrator 155 may also remove a portion of drilling mud 26, for example, as a film on drill cuttings. From vibrator 155, mud 26 may be returned to work pit 27A, or may be otherwise conditioned and prepared for recirculation through borehole 11.

In some embodiments, drilling system 100 may also include a downlink 160. The downlink 160 may form part of a flow path that bypasses the borehole 11 and drill string 12 and returns drilling mud from the pump 29 directly to the working pit 27A. The downlink 160 may be used for bi-directional communication with various aspects of the BHA 120.

Turning now to the processing methods and controls of at least a portion of the drilling system 100, and in particular the mud system thereof, embodiments of the present disclosure may be used in conjunction with two techniques for mud flow monitoring and control. In particular, these embodiments can model mud flow through surface equipment and automatically identify and track mud transfer between pits.

Modeling the surface equipment helps make predictions that take into account transient mud volumes. Transient mud volumes can be observed when mud flow changes, for example during connection (e.g., because the pump is stopped and started). The model may employ a dynamic (on the fly) recalibration strategy that allows the model to adapt to changing mud properties. This recalibration is controlled to avoid training the model with abnormal condition data. The automatic recalibration process works in parallel with the process of detecting mud volume balance anomalies in real time using the model. The prediction is then compared to the measured volume to determine the mud volume balance, from which an increase or loss of mud can be determined.

Automatic detection of mud transfer between pits may identify periods of change in mud level measured therein by monitoring non-working mud pits and applying a segmentation algorithm. Based on the change in mud volume balance, a change in mud volume trend in the non-working pit can be identified and can be correlated with the transition into or out of the working pit. The measured working volume may be automatically adjusted in real time in response to the detected transfer between the non-working pit and the working pit. As the term is used herein, "real-time" refers to something that occurs without delay that is easily perceived by a human user, with the goal of "real-time" without any significant delay.

FIG. 2 illustrates a control block diagram of a mud control system 200, according to an embodiment. The mud control system 200 may be implemented using one or more computing devices of a computing system that may be local to the drilling system 100 (e.g., physical components of the drilling system 100) or located remotely from the drilling system 100 and in communication therewith via, for example, an internet connection.

As shown at 202, the mud control system 200 may make real-time measurements of mud in the drilling system 100, particularly in both active and inactive pits and pumps, as described above with reference to fig. 1. In particular, the volume of mud in the pit can be measured as a function of time. These measurements may be used to operatively control a physical mud system (i.e., the mud-treating components of the drilling system 100). For example, the measurements may be provided to the transient flow modeling module 204. The transient flow modeling module 204 may model mud flow (including losses) during transient phases of flow in the drilling system 100, such as when the pump is first turned on or after it is turned off.

The output of the transient flow modeling module 204 may be provided to a mud volume balance module 206 and used to calculate a mud volume balance. The mud volume balance may be an output of a model configured to model the mud flow in the mud system, thereby predicting the fluid level in the work pit. In particular, mud volume balance is the difference between the mud volume measured in the work pit and the mud volume in the work pit as predicted by the model. The work pit may be a convenient place to plan the mud volume because the mud volume in the work pit (and non-work pits, discussed below) may be easily measured to provide a calibrated measurement for the model in which the mud volume balance module 206 operates. The model may take into account various components of the mud flow system, increases and losses in fluid, time delays, etc., and plan for the desired amount of mud in the work pit.

Referring now to FIG. 3, a conceptual diagram of a mud system 300 of the drilling system 100 according to an embodiment is shown. For a further understanding of the operation of the transient flow stream modeling module 204 and the mud volume balancing module 206 as part of the mud control system 200 of FIG. 2, reference will be made to this view of the mud system 300.

As the mud exits the well 302, it includes cuttings suspended therein. The slurry flows through a flow line 304 (e.g., a pipe) and to a vibrator 306. Vibrator 306 provides a screen that filters the mud so that cuttings 307 are separated from the mud and the "clean" mud is returned to one or more work pits 308. The mud is then pumped out of the work pit 308 and returned to the well 302, starting a new cycle. At the shaker 306, some of the mud 309 is also removed or "lost" with the drill cuttings 307, for example, as a mud film around the drill cuttings 307.

The mud volume in the working pit 308 may peak (rise or fall) when the pump is stopped and started, for example, due to a cushioning effect in the surface equipment (e.g., at the vibrator 306). This behavior can theoretically be reproduced by modeling vibrator 306 as a permeable medium.

During steady state pump flow, the mud volume in the work pit 308 decreases due to cuttings and mud losses at the shaker 306. Cuttings losses may be inferred from the cuttings stream, which may be measured at the outlet of shaker 306. However, the mud loss associated with a given amount of cuttings flow may vary based on several factors, and thus may be calibrated.

Thus, to calculate the theoretical mud volume in the work pit 308 at a given point in time, the losses during the transient and steady state flow periods can be modeled. To this end, two coefficients can be estimated: a surface loss coefficient β (e.g., from vibrator 306) and a permeability coefficient k (representing the subterranean formation through which well 304 extends). For example, the permeability coefficient k primarily affects the mud volume in the work pit 308 during transient flow periods, such as pump start-up and pump shut-down. On the other hand, during steady-state flow periods, the surface loss coefficient β affects the mud volume in the work pit 308.

Thus, the calibration strategy can also be divided into two phases. Referring now to FIG. 4, a flow diagram of a calibration process 400 is shown, according to an embodiment. The process 400 may include calculating a permeability coefficient during a transient period of a mud flow, as at 402. In particular, the permeability coefficient k may be calibrated during transient periods. Pump activation may be selected to provide a transient period. While pump stop may also provide a transient flow period, and thus some embodiments may use pump stop as a transient flow period, there may be a higher risk of abnormal operation (e.g., kick) during pump stop.

For the second phase of calibration, as at 404, a steady-state flow period may be selected to calibrate the surface loss coefficient β. The steady state flow period may be experienced after the pump is started (e.g., after the duration of time that mud begins to flow) and before the pump is stopped. For calibration purposes, a first steady-state flow period may be selected, i.e., between a first pump start and a first pump stop, as it may present the least risk of abnormal operation affecting the mud flow; however, in other embodiments, other periods of steady-state flow may be selected. Thus, in at least some cases, the surface loss coefficient β may not be recalibrated during the second steady-state flow period (e.g., after the second pump is started and before the second pump is stopped).

The mud flow in the mud system 300 may then be modeled based at least in part on the calculated surface mud loss coefficient beta and the permeability coefficient k, as at 406. For example, to calculate these coefficients, thereby producing an accurate mud flow model, the modules 204, 206 may begin by taking into account the conservation of mass equations in the vibrator 306.

The volumetric flow rate of the cleaning slurry exiting the vibrator 306 may be expressed using Darcy's law. That is, vibrator 306 may behave similarly to a porous medium having a given permeability, area, and thickness. The height of the cleaning mud accumulated on the shaker screen is denoted as Δh.

As a first approximation, flow line effects may be ignored. For example, assume that the flow line is primarily due to delays in mud flow caused by wave propagation. Some damping effects may also occur, but their effects may be included in the modeling of the porosity of the shaker screen. Damping and porosity effects can be approximated by a first order system. Thus, the flow rate at the outlet of the flow line can be approximated. Another approximation is that there is a small variation in mud density in each section.

Mass conservation equations in the work pit can then be determined. The mud at the well outlet is a two-phase medium with clean mud and cuttings, which may allow the determination of mud density as a function of time. Further, the drill cuttings may be considered to have a substantially constant density, so the remaining unknowns are the density of the cleaning mud, which may be related differently to the difference between the mud density and the density of the removed drill cuttings.

The volume of the shale shaker is not typically measured, but its calculation is intermediate data useful for the final calculation of the working volume. Although the calculated vibrator volume may not be compared to the actual measurement value for verification, some physical conditions may be used to control its calculation. For example, its global behavior conforms to a first order system.

From a physical point of view, the vibrator volume at pump start-up is easier to solve than at pump stop. If the time between the previous pump stop and pump start is long enough, the vibrator volume can be considered near zero at the beginning of the pump start because the slurry above the vibrator screen may have been discharged through the vibrator screen when the pump was off. Thus, in practice, for start-up of the pump, the initial condition of the vibrator may be known, e.g. the vibrator volume is stationary.

The calculated vibrator volume at the end of pump stop may not reach zero, but tends towards zero. Specifically, the calculated vibrator volume follows a first order response that progressively approaches a steady state value (zero when the pump is stopped). Thus, at the beginning of the next pump start, the initially calculated vibrator volume is not zero (but near zero) even if the time between the last pump stop and pump start is sufficiently long (depending on the time constant τ).

In addition, the first order model may introduce error accumulation at other times. Thus, process 400 may include determining when to recalibrate either or both coefficients, as at 408. The calculation of the working volume is based on an iterative method. Thus, to reduce error propagation, a recalibration or adjustment of the global shaker constant K may be performed during each pump start, and if the pump is shut down long enough, the shaker volume may be reset to zero at the beginning of each pump start. If the permeability coefficient k is calibrated once, the theoretical mud volume in the working pit will deviate from the measured volume due to the first-order assumption.

FIG. 5 shows a graph of working volume (i.e., volume of mud in a work pit) and flow in a mud system, both as a function of time, according to an embodiment. In this figure, there are three transient flow calibration periods caused by pump activation. These sections are labeled as sections 501, 502, 503 and represent transient flow regimes in the mud system. The permeability coefficient k may be calibrated during each of the sections 501-503. In contrast, under normal conditions, the surface loss coefficient β may not change because it relates to mud loss in surface equipment and filtered cuttings, as described above. During the calibration period, the permeability coefficient k may be calculated to ensure a fit between the measured working volume and the calculated volume. Furthermore, the calibration of the coefficients may occur after the first calibration of the global vibrator constant k

Since the ground loss factor β is related to the equipment configuration, it may be constant under normal conditions. However, when one or more screens of shaker 306 are plugged, the surface loss factor β may change as this reduces cuttings filtration and increases mud buffering above shaker 306. Furthermore, as cuttings flow rate changes, the surface loss coefficient β may change, as cuttings flow rate changes may result in changes in mud coating conditions. Cuttings flow changes may be automatically identified by a mud volume balance model and mud loss factor recalibration may be automatically triggered without any operator input. However, shaker screen blockage may be difficult to predict. Thus, the operator may still need to take precautions to clean the filter or manually recalibrate the floor loss factor β.

Fig. 6 illustrates another embodiment of a mud system 300. In this embodiment, the mud system 300 includes a downlink 600, such as that shown and discussed above with respect to FIG. 1, among other components discussed above. The provision of the downlink 600 may result in a modified mud volume model. In particular, where downlink 600 is included, the volume of the pit may be expressed at least in part as a function of the amount of traffic diverted by downlink 600.

Returning to FIG. 2, the transient flow modeling module 204 is used to calibrate the mud volume balance module 206, as described above. As shown, the mud control system 200 can also include a diversion identification module 208 and a diversion compensation module 210. The measurements made at 202 may be provided to the diversion compensation module 210 and the diversion identification module 208, and the diversion compensation module 210 and the diversion identification module 208 may modify the mud volume balance calculated in the module 206.

The transfer identification module 208 may be implemented by monitoring the non-work pit and the change in mud volume in the work pit, for example, using a change point or any suitable segmentation algorithm. The change point algorithm may be set with an appropriate threshold, for example 1 cubic meter, to eliminate false positives caused by noise. Furthermore, if the segment length is greater than twice the pit volume noise, then a "segment", e.g., a change in volume, may be considered "significant".

Thus, the transfer identification module 208 may generally be passive, monitoring the mud level in the pit until a drop or rise or both are detected, e.g., by the mud volume balance of the reference module 206. In this regard, it is determined whether the decrease or increase affects the fluid level in the other pit, for example, by examining the mud volume in each pit to determine a corresponding change in fluid level. For example, if the mud level in one non-working pit drops, a transfer to another non-working pit may result in a corresponding increase in the level in the other non-working pit, and such a transfer may not affect the working system. In contrast, mud transfer from a non-working mud pit to a working mud pit may be marked by a decrease in mud volume in at least one non-working pit, an increase in volume in a working pit, and may affect mud volume in a mud flow system.

Slurry volume balance can be used for cross-check transfer. The mud volume balance is calculated as the difference between the measured working volume and the theoretical working volume. As described above, the mud volume balance compensates for transient effects and has a more stable character than the originally measured working volume, for example in a working pit. Thus, when a transition to a pit occurs during a pump flow change, it may be difficult to identify based on observing the mud volume in the pit alone. However, it is much easier to observe the transfer on the mud volume balance. Thus, mud balance is used as a reference for transfer crossover inspection.

Fig. 7 shows a graph of the volume of mud in a non-working pit 701, the mud flow 702 (e.g., by a pump), the mud volume in a working pit 703, and the mud balance 704 (the difference between the actual and theoretical mud volumes in the working pit) over a normal period of time. It can be seen that the mud volume in the non-working pit 701 can be relatively stable until a diversion event indicated at 705. Transfer event 705 is represented by a measured decrease in fluid volume in the non-working well; but this may not represent an increase in mud volume in the mud system unless there is a corresponding delay in the increase in liquid volume in the working pit.

Further, as shown in the plot of the mud flow 702, the mud flow 702 may be unstable at transition event 705. At 702, the pump has been turned off and on, turned off again, and is in a transient phase at the beginning of transition event 705. Thus, as can be seen from graph 703, the liquid level has changed, but it is unclear whether this is caused by transient flow in the pump or by fluid transfer from a non-working pit, and as seen earlier in time, transient flow has caused an increase in the liquid level in the working pit. However, mud volume balancing eliminates at least some of the effects of transient flow from the working pit volume. If the mud volume balance deviates beyond a threshold, as indicated by a relatively sharp rise during transfer event 705, it represents a mud addition event to the work pit. Together with the reduction in the amount of mud in the non-working pit, it can be inferred that a diversion has occurred, rather than that a downhole mud delta event (kick) has occurred. Thus, there is a two-factor test to determine the presence of a transfer and to distinguish the transfer from a downhole increase/loss event: a change in mud volume in the non-working pit, and an increase in mud volume balance in the working pit.

The mud volume in the pit can then be adjusted to compensate for the transfer. This is illustrated by the summation between the transfer compensation module 210 and the mud volume balancing module 206 in fig. 2. Compensating for the use of slurry volume balance changes. In fact, the mud volume balance is not affected by transient effects, such as pump flow changes, cuttings recovery impact at the shaker, and surface losses. Thus, a change in mud balance during the transfer process may be representative of the amount of mud transferred from or to another pit.

Fig. 8 illustrates a flow chart of a process 800 for detecting a transition in a mud system (e.g., mud system 300) as part of the operation of the mud control system (e.g., mud control system 200) according to an embodiment. Process 800 may include monitoring a mud volume in a non-working pit, as at 802. For example, a change point analysis or other segmentation technique may be applied thereto, e.g., in a continuous manner.

At some point, the monitoring operation at 802 may indicate a change in mud level in one of the non-operational pits (or non-operational pits if mud system 300 includes a single non-operational pit), as at 804. As discussed above, the change in mud level may exceed a threshold, for example, to account for noise in the measurement. The change in mud level may be an increase or decrease in mud level in a non-working pit. Thus, the mud level in the non-working pit is the first trigger to determine if a transition has occurred. If such a change in mud level in a non-working pit does not precede a change in mud level in a working pit, the change in mud level in the working pit can be attributed to a downhole increase/loss event or another event that is not caused by a transfer.

Once a change in mud level in a non-working pit is identified at 804, process 800 may continue with determining if there is a corresponding change in mud level in another non-working pit, as at 806. For example, in some cases, there may be more than one non-working well, and there may be reasons to transfer fluid between these non-working wells, e.g., change composition, balance level, etc. Thus, if the volume in one non-working pit changes, the process 800 checks to see if the volume of mud in another non-working mud pit accounts for this change, which would keep the mud outside the working system, so as not to affect the working mud volume. If another non-job pit mud level changes to account for the first pit change, process 800 proceeds to 808 where the boundary of the non-job pit (baseline level) changes and process 800 returns to monitoring the mud level in the job pit at 802.

If there is no corresponding change in the other non-working pits (or if there is no other non-working pit, or if the change in the non-working pit does not fully account for the change in the non-working pit identified at 802), process 800 may continue to determine if there is a corresponding change in the mud volume of the working pit, as at 810. As described above, this may be evaluated based on mud volume balance, i.e., comparing the predicted mud volume in the work pit to a measured value, rather than or in addition to comparing the original volume change in the work pit. If there is no corresponding change in the level of the work pit detected at 810, then some other event may occur in the system or well, which may be addressed separately.

If there is a corresponding change in the working pit (as evidenced, for example, by mud volume balance), it is determined that mud transfer from the non-working mud pit to the working mud pit has occurred. Thus, a kick alarm may not be appropriate. Thus, if the recoil alarm has been activated, it may be deactivated, as at 812, or not activated. The process 800 may then continue with changing the boundary 814 of the working mud volume to bring the modeled mud volume back to agreement with the measured mud volume (e.g., recalibrating the model so that the mud balance is zero or near zero).

Fig. 9 illustrates a flow chart of a method 900 for monitoring and/or controlling a mud system of a drilling rig according to an embodiment. The method 900 may include pumping mud in a mud system, as at 902. Further, the method 900 may include modeling mud loss during transient flow periods, as at 904. Transient flow periods may occur immediately after pump start-up and shut-down. In an embodiment, the loss of mud during transient flow may be primarily due to formation permeability, and such loss may be modeled as described above, for example using the permeability coefficient k.

Further, the method 900 may include modeling mud loss during the steady-state flow period, as at 906. Mud loss during steady-state flow periods may be primarily due to surface mud losses, such as mud loss from vibrators and drill cuttings. In some embodiments, mud loss during steady-state flow periods may be modeled based on the surface loss coefficient β, as discussed above.

Method 900 may also include monitoring (e.g., continuously or periodically measuring) the mud volume level in the active and inactive pits of the mud system, as at 908. A work pit refers to a pit through which mud circulates during normal pumping operations. The non-working pit may store a slurry reserve and slurry may be transferred from the non-working pit to the working pit for use in a slurry system. Thus, during normal pumping operations, fluid does not continually circulate through the non-working well and into/out of the well.

During operation of the mud system, the mud loss is based in part on other factors, such as mud flow, surface equipment, downlink operation, etc. Method 900 may include calculating a slurry balance of the work pit, as at 910. The slurry balance of the work pit may be the difference between the measured slurry volume in the work pit and the model predicted slurry volume.

The method 900 may (re) calibrate the model of one or both of the transient losses and/or steady state losses periodically, as at 912. Calibration of these losses is discussed above. In some embodiments, steady state losses may be calibrated during a first steady state flow period and then recalibrated when surface or flow conditions change, such as when shaker screens are plugged. Transient losses may be recalibrated after a first pump start, or after each pump start, etc.

Method 900 may also include detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of mud volume and mud balance in the non-working mud pit, as at 914. As discussed above, the detection of the transfer may be a two-part (at least) determination. First, a change in mud volume in a non-working pit is detected. If there is no change in mud volume in the non-working pit, there is no transfer to/from the non-working pit, so any change in mud volume in the working pit may be due to other conditions, such as an increase or loss of downhole mud.

Once a change in mud volume in one non-working pit is detected, a determination is made as to whether there is a corresponding change in mud volume in the other non-working pit (indicating no transfer between non-working pit and working pit) or whether there is a corresponding change in mud volume in the working pit. However, as discussed above, if mud is circulated into and out of the well, the mud volume in the work pit may not be static, e.g., transient flow conditions may make it difficult to identify the identification of changes in the work pit volume.

Thus, method 900 may base the detection of mud displacement on a deviation of the mud volume balance from a certain amount, e.g., corresponding to (or substantially the same as) a change in the mud volume of a non-working pit. If the deviation in mud volume balance corresponds to a change in mud volume in a non-working pit, the method 900 may determine that a shift has occurred, rather than a downhole increase/loss event, and any kick alarms or the like may be deactivated (or not activated). Further, method 900 may include adjusting the model (e.g., modeled mud volume in the work pit) to account for the transfer, as at 916. Thus, mud volume balancing may be prepared to form the basis for detection of downhole increase/loss events, for example, by accurately modeling "normal" mud loss in the system (e.g., by vibrator or based on formation permeability) and taking into account shifts in mud volume balancing in order to allow for differentiation of downhole mud loss/increase from normal operation and shifts.

Fig. 10 illustrates two graphs 1000 and 1002 showing the operation of the method 900, according to an embodiment. In the first graph 1000, the measured working mud volume 1004 (i.e., the volume circulated through the mud system, such as the volume measured at a work pit) is compared to the theoretical (calculated on the model) working mud volume 1006. In the second graph 1002, a real-time mud volume balance (difference between measured and working mud volumes) 1008 and an "adjusted" mud volume balance 1010 are shown, which takes into account the shift.

As can be seen, the trend of the theoretical volume 1006 in the first graph 1000 is generally decreasing. The measured mud volume 1004 tracks this until event 1020 occurs. Event 1020 causes measured mud volume 1004 to increase sharply beyond theoretical volume 1006. Typically, this indicates an increase in downhole mud (e.g., a kick), which may be a dangerous situation, or mud transfer from one or more non-working pits to a working pit, which is not a dangerous situation.

The second graph 1002 shows how the detection of event 1020 affects mud volume balance. As expected from the difference between lines 1004, 1006, the mud volume balance begins to peak at event 1020.

In response to event 1020, in at least some embodiments, an alert may be activated, and at least one task of method 900 may determine whether the alert is legitimate (e.g., a kick has occurred/is occurring). To this end, the method 900 determines whether there is also a transfer, for example, by referencing mud volume balance and non-working multi-volumes, as discussed above. The diversion determination may occur in parallel with the monitoring of mud volume or may occur in response to an alarm being activated. The transfer determination is discussed in detail above. If a transition is determined, the alarm may be released.

In another embodiment, the alarm is not activated immediately in response to detecting event 1020. Conversely, a flag or alert may be set in response to event 1020 and method 900 may determine whether an alarm should be activated. To do so, the method 900 may check for the occurrence of a transfer, as discussed above. If a transition occurs, the method 900 suppresses the activation of the alarm, otherwise the alarm is activated.

If a shift is determined, a mud model in the work system may be updated, which may be used to "revise" the mud balance to account for the shift in mud. As can be seen in the second graph 1002, the real-time mud balance 1008 is adjusted to near zero, reflecting that the mud model accurately predicts the working mud volume, now taking into account the shift.

The revision may be prospective from the perspective of the user. For example, there may be a delay or buffer in delivering mud measurements to the user so that the transition can be detected and accommodated in the model and the mud volume balance corrected before the user receives the measurements. Or when determining mud displacement, the mud volume balance may be modified in a retrospective manner. In either case, if a transition is detected, the alarm may be initially activated and then deactivated, or it may be decided whether to activate or deactivate such an alarm prior to activation based on whether a transition is detected.

Thus, it can be seen that the present system and method has several practical applications. For example, a kick alarm that is automatically initiated in response to an increase in mud balance and/or an increase in pit volume may be quickly and operationally verified or identified as erroneous and deactivated. In particular, embodiments of the present disclosure may make robust determinations that take into account mud loss in the surface and well, as well as fluid transfer between non-working and working pits. This may facilitate control and operation of a mud system for circulating mud through the well, for example, via pumping mud from the work pit into the well and back into the work pit.

In some embodiments, the methods of the present disclosure may be performed by a computing system. Fig. 11 illustrates an example of such a computing system 1100 in accordance with some embodiments. The computing system 1100 may include a computer or computer system 1101A, which may be a stand-alone computer system 1101A or an arrangement of distributed computer systems. The computer system 1101A includes one or more analysis modules 1102 configured to perform various tasks, such as one or more of the methods disclosed herein, according to some embodiments. To perform these various tasks, the analysis module 1102 executes independently or in concert with one or more processors 1104, the processor(s) 1104 being connected to one or more storage media 1106. The processor(s) 1104 are also connected to a network interface 1107 to allow the computer system 1101A to communicate with one or more additional computer systems and/or computing systems, such as 1101B, 1101C, and/or 1101D, over a data network 1109 (note that the computer systems 1101B, 1101C, and/or 1101D may or may not share the same architecture with the computer system 1101A, and may be located in different physical locations, e.g., the computer systems 1101A and 1101B may be located in a processing facility while communicating with one or more computers 1101C and/or 1101D located in one or more data centers, and/or in different countries on different continents).

A processor may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

The storage medium 1106 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of fig. 11, the storage medium 1106 is depicted as being within the computer system 1101A, in some embodiments the storage medium 1106 can be distributed within multiple internal and/or external housings of the computing system 1101A and/or across computing system 1101A and/or additional computing systems. The storage medium 1106 may include one or more different forms of memory, including semiconductor memory devices such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM) and flash memory, magnetic disks (e.g., fixed, floppy, and removable disks), other magnetic media (including magnetic tape), optical media (e.g., compact Disks (CD) or Digital Video Disks (DVD), optical media (e.g., optical disks (DVD), optical disks (e.g., optical disks) or optical disks (optical disks),A magnetic disk or other type of optical storage device, or other type of storage apparatus. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or can be provided on multiple computer-readable or machine-readable storage media distributed in a large system with potentially multiple nodes. Such a computer-readable or machine-readable storage medium or media is considered to be part of an article (or article of manufacture). The article of manufacture or article of manufacture may refer to any manufactured single component or multiple components. One or more storage media may reside in a machine executing machine-readable instructions or at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In some embodiments, the computing system 1100 includes one or more mud control modules 1108. In the example of computing system 1100, computer system 1101A includes a mud control module 1108. In some embodiments, a single mud control module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, multiple mud control modules may be used to perform some aspects of the methods herein.

It should be appreciated that computing system 1100 is only one example of a computing system and that computing system 1100 may have more or fewer components than shown, additional components not depicted in the example embodiment of fig. 11 may be combined, and/or computing system 1100 may have a different configuration or arrangement of components depicted in fig. 11. The various components shown in fig. 11 may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.

Furthermore, steps in the processing methods described herein may be implemented by running one or more functional modules in an information processing apparatus, such as a general purpose processor or a dedicated chip such as ASIC, FPGA, PLD or other suitable device. Such modules, combinations of such modules, and/or combinations thereof with general purpose hardware are included within the scope of this disclosure.

The computational interpretation, model, and/or other interpretation assistance may be refined in an iterative manner; this concept applies to the methods discussed herein. This may include using feedback loops performed based on algorithms, such as at a computing device (e.g., computing system 1100, fig. 11), and/or through manual control by a user, the user may determine whether a given step, action, template, model, or set of curves has become sufficiently accurate for evaluating the subsurface three-dimensional geological formation under consideration.

The foregoing description, for purposes of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. Furthermore, the order in which the elements of the methods described herein are illustrated and described may be rearranged and/or two or more elements may occur concurrently. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims (18)

1. A method for monitoring and controlling a mud flow system in a drilling rig, comprising:

measuring the working mud volume in the working mud pit and the non-working mud volume in the non-working mud pit;

Modeling a modeled working mud volume in the working mud pit;

Determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume;

detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance; and

Downhole increase and loss are automatically detected based on mud volume balance,

Wherein modeling the modeled working mud volume includes:

Determining a permeability loss coefficient during the transient flow period; and

The ground loss factor during the steady-state period is determined,

Wherein the modeled working mud volume is modeled based on a combination of the permeability loss coefficient and the surface loss coefficient.

2. The method of claim 1, further comprising, in response to detecting the shift, modifying a mud volume balance to account for the shift.

3. The method of claim 1, wherein the permeability loss coefficient is related to a flow of mud into or out of a subterranean formation, and wherein the surface loss coefficient is related at least in part to a flow of mud out of a vibrator of a drilling system.

4. The method of claim 1, further comprising recalibrating the surface loss factor during a steady-state flow period after the first pump start and before the first pump stop, wherein the surface loss factor is not recalibrated after the first pump stop and before the second pump stop.

5. The method of claim 1, further comprising recalibrating the permeability loss factor during pump start-up before a steady-state flow period is reached after pump start-up.

6. The method of claim 1, wherein detecting the transfer of mud comprises:

determining that the non-working mud volume has changed by more than a threshold amount; and

In response to determining that the non-working mud volume has changed, determining that the mud volume balance has changed to compensate for the non-working mud volume change.

7. The method of claim 6, wherein detecting the transfer of mud further comprises determining that a mud volume in another non-operational mud pit has not changed to compensate for the change in non-operational mud volume, wherein determining that a mud volume balance has changed to compensate for the change in non-operational mud volume is also responsive to determining that a mud volume in another non-operational mud pit has not changed to compensate.

8. The method of claim 1, further comprising deactivating or suppressing activation of a kick alarm in response to detecting a diversion of mud.

9. The method of claim 1, further comprising pumping mud into a well using the mud flow system, wherein the mud is circulated through the working mud pit.

10. A computing system, comprising:

One or more processors; and

A memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations comprising:

measuring the working mud volume in the working mud pit and the non-working mud volume in the non-working mud pit;

Modeling a modeled working mud volume in the working mud pit;

Determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume;

detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance; and

Downhole increase and loss are automatically detected based on mud volume balance,

Wherein modeling the modeled working mud volume includes:

Determining a permeability loss coefficient during the transient flow period; and

The ground loss factor during the steady-state period is determined,

Wherein the modeled working mud volume is modeled based on a combination of the permeability loss coefficient and the surface loss coefficient.

11. The computing system of claim 10, wherein the operations further comprise, in response to detecting a shift, modifying a mud volume balance to account for the shift.

12. The computing system of claim 10, wherein the permeability loss coefficient relates to a flow of mud into or out of a subterranean formation, and wherein the surface loss coefficient relates at least in part to a flow of mud out of a vibrator of the drilling system.

13. The computing system of claim 10, wherein the operations further comprise recalibrating the surface loss factor during steady-state flow periods after a first pump start and before a first pump stop, wherein the surface loss factor is not recalibrated after a first pump stop and before a second pump stop.

14. The computing system of claim 10, wherein the operations further comprise recalibrating the permeability loss factor during pump start-up before a steady-state flow period is reached after pump start-up.

15. The computing system of claim 10, wherein detecting a diversion of mud comprises:

determining that the non-working mud volume has changed by more than a threshold amount; and

In response to determining that the non-working mud volume has changed, determining that the mud volume balance has changed to compensate for the non-working mud volume change.

16. The computing system of claim 15, wherein detecting the transfer of mud further comprises determining that a mud volume in another non-operational mud pit has not changed to compensate for the change in non-operational mud volume, wherein determining that a mud volume balance has changed to compensate for the change in non-operational mud volume is also responsive to determining that a mud volume in another non-operational mud pit has not changed to compensate.

17. A computing system, comprising:

One or more processors; and

A memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations comprising:

measuring the working mud volume in the working mud pit and the non-working mud volume in the non-working mud pit;

Modeling a modeled working mud volume in the working mud pit;

Determining a mud volume balance by calculating a difference between the measured value of the working mud volume and the modeled working mud volume;

detecting a mud transfer from the non-working mud pit to the working mud pit based on a combination of a change in the measured value of the non-working mud volume in the non-working mud pit and a change in the mud volume balance; and

Downhole increase and loss are automatically detected based on mud volume balance,

Wherein modeling the modeled working mud volume includes:

Determining a permeability loss coefficient during the transient flow period; and

The ground loss factor during the steady-state period is determined,

Wherein the modeled working mud volume is modeled based on a combination of the permeability loss coefficient and the surface loss coefficient.

18. The computing system of claim 17, wherein the operations further comprise, in response to detecting the shift, modifying the mud volume balance to account for the shift.