CN114846220A

CN114846220A - Automatic kick and loss detection

Info

Publication number: CN114846220A
Application number: CN202080090190.4A
Authority: CN
Inventors: A.拉丰
Original assignee: Schlumberger Technology Corp
Current assignee: Schlumberger Technology Corp
Priority date: 2019-10-31
Filing date: 2020-10-27
Publication date: 2022-08-02
Anticipated expiration: 2040-10-27
Also published as: US20220397008A1; EP4051865A1; CN114846220B; WO2021087509A1; EP4051865A4

Abstract

A method for monitoring and controlling a mud flow system in a drilling rig includes 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 a 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

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

Background

Downhole fluid gain and loss detection provides data related to the safety and integrity of drilling operations. One way to accomplish this is by monitoring the flow rate and using a flow rate model to infer 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 gain and loss rely on pit volume measurements (i.e. mud volume/level in the working (active) pit) or flow paddle measurements. While pit volume methods are generally easier to deploy, the kick (kick) detection capability they provide may be less efficient and less reliable. In particular, there may be a relatively long delay between a downhole mud increase/loss event and a responsive change in the pit mud level. This delay occurs in part because surface equipment acts as a buffer between the pit and the well, slowing the pulse of mud flow, eventually changing the level of the pit mud.

In addition, pit volume monitoring may be relatively inflexible. For example, the pump shutdown may be fingerprinted and used as a baseline reference to infer whether the active pit mud volume changes subsequently observed are normal. If the flow conditions in the subsequent time period differ from the flow conditions in the control sample, no reliable loss of state conclusions can be drawn from the mud volume change. Furthermore, this technique relies on cycles 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 subsequent abnormalities may be missed, or normal work may be falsely flagged as abnormal.

Further, the pit volume monitoring technique may interpret normal surface events, such as transfers between pits, as downhole increases or losses, resulting in erroneous 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 the mud logging reports after they have discussed with the mud engineers, when they are aware of, for example, a transfer. Thus, the operator may disarm the add alarm upon confirming that the transfer to the work system occurred. However, this practice relies on human users to make observations and manually enter error-free information into the log in a timely manner. Furthermore, the operator's reaction may take time, as the operator may be responsible for multiple other tasks simultaneously, resulting in delays, which may result in long false kick alarm 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, including 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 a 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 computing system comprising one or more processors and a storage 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. The operations include 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 a measured value of the working mud volume and the modeled working mud volume, detecting a transfer of mud 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 a downhole increase and loss 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 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 a measured value of the working mud volume and the modeled working mud volume, detecting a transfer of mud 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 a downhole increase and loss based on the mud volume balance.

Drawings

The invention is better understood from the following detailed description when read in conjunction with the accompanying 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 one embodiment.

FIG. 4 shows a flow diagram of a process for modeling mud flow in a mud system, according to an embodiment.

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

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

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

FIG. 8 shows a flow diagram of a process for detecting and accounting for mud shifting according to an embodiment.

FIG. 9 shows a flow diagram of a method for controlling a mud system, according to an embodiment.

FIG. 10 illustrates a graph of measured and theoretical (calculated/modeled) working mud volumes, and real-time and adjusted mud volume balance, 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 of course 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.

Further, as used herein, the articles "a" and "an" are intended to have their ordinary meaning in the patent arts, i.e., "one or more" as used herein, the term "about" when applied to a value generally means within the tolerance of the equipment used to produce the value, or in some examples, plus or minus 10%, or plus or minus 5%, or plus or minus 1%, unless expressly stated otherwise. Further, the term "substantially" as used herein refers to an amount such as a majority, or almost all, or a range of about 51% to about 100%. Furthermore, the examples herein are merely illustrative and are for purposes of discussion and not limitation. 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. Platform and derrick assembly 10 may include drilling equipment such as a rotary table 16, kelly 17, hook 18, and/or rotary union 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 a traveling block by a kelly 17 and 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 segments 125, and the segments 125 may be or include pipes and/or drill collars.

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

Pump 29 of drilling system 100 may deliver mud 26 from work pit 27A to the interior of drill string 12, extending through a port in rotary union 19 to borehole 11, which may cause mud 26 to flow downward through drill string 12 and BHA120, as indicated by arrow 8. The 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 arrows 9. The mud 26 may lubricate the drill bit 122 and/or may carry formation cuttings to the surface adjacent the borehole 11. The mud 26 may be returned to the work pit 27A for cleaning and recirculation.

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

The drilling system 100 may include one or more vibrators 155. The shaker 155 may receive mud that has been circulated up the borehole 11 and may remove large cuttings therefrom. The shaker 155 may also remove a portion of the drilling mud 26, for example, as a film on the drill cuttings. From the shaker 155, the mud 26 may be returned to the work pit 27A, or may be otherwise conditioned and prepared for recirculation through the 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. Downlink 160 may be used for bi-directional communication with various aspects of BHA 120.

Turning now to the processing and control of at least a portion of the drilling system 100, and in particular its mud system, embodiments of the present disclosure may combine two techniques for mud flow monitoring and control. In particular, the embodiments may model the mud flow through surface equipment and automatically identify and track mud transfers between pits.

Modeling surface equipment helps to make predictions that take into account transient mud volumes. Transient mud volumes may be observed when mud flow changes, such as during a 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 using the model to detect mud volume balance anomalies in real time. This 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.

Automated detection of mud transfer between pits can be achieved by monitoring non-working mud pits and applying a segmentation algorithm to identify periods of variation in measured mud levels therein. Based on changes in mud volume balance, changes in mud volume trends in non-work pits may be identified and correlated to transitions into or out of the work pit. The measured working volume may be automatically adjusted in real time in response to a detected transfer between a non-work pit and a work pit. As used herein, the term "real-time" refers to what is readily perceived by a human user to occur without delay, with the goal of "real-time" without any noticeable delay.

FIG. 2 illustrates a control block diagram of a mud control system 200 according to one embodiment. The mud control system 200 may be implemented using one or more computing devices of a computing system that may be located locally 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 working and non-working pits and pumps, as described above with reference to fig. 1. In particular, the volume of mud in the pit may be measured as a function of time. These measurements may be used to operatively control the physical mud system (i.e., the mud processing 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 a transient phase 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 a mud flow in the mud system to predict a fluid level in the work pit. In particular, the mud volume balance is the difference between the mud volume measured in the work pit and the mud volume predicted by the model while in the work pit. 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, as discussed below) may be easily measured, thereby providing a calibration measurement for the model in which the mud volume balancing module 206 operates. The model may take into account various components of the mud flow system, fluid addition and loss, time delays, etc., and plan for the expected 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 is shown, according to an embodiment. For 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 drill cuttings suspended therein. The mud flows through a flow line 304 (e.g., pipe) and to a vibrator 306. The shaker 306 provides a screen that filters the mud so that the 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 working pit 308 and back into 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, e.g., as a mud film around the drill cuttings 307.

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

During steady state pump flow, the mud volume in the working pit 308 decreases due to cuttings and mud loss at the shaker 306. Cuttings loss may be inferred from the cuttings flow, which may be measured at the outlet of the shaker 306. However, the mud loss associated with a given amount of drill 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 flow period and the steady state flow period may be modeled. To this end, two coefficients may be estimated: a surface loss coefficient β (e.g., from vibrator 306) and a permeability coefficient k (representing the subsurface formation through which well 304 extends). For example, the permeability coefficient k primarily affects the mud volume in the working pit 308 during transient flow periods, such as pump start-up and pump shut-down. On the other hand, during the steady state flow period, the surface loss factor β affects the mud volume in the working 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 the mud flow, as at 402. In particular, the permeability coefficient k may be calibrated during the transient period. Pump start-up may be selected to provide a transient period. While pump stops may also provide transient flow periods, and thus some embodiments may use pump stops as transient flow periods, there may be a higher risk of abnormal operation (e.g., a kick) during a pump stop.

For the second phase of the calibration, as at 404, the steady-state flow period may be selected to calibrate the ground loss coefficient β. The steady state flow period may be experienced after the pump is started (e.g., after the duration of time the 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 the first pump start and the first pump stop, as it may carry the lowest 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 instances, 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 β and permeability coefficient k, as at 406. For example, to calculate these coefficients, and thereby generate an accurate mud flow model, the

modules

204, 206 may begin by considering the mass conservation equations in the vibrator 306.

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

As a first approximation, the flowline effects can be ignored. For example, suppose the flowline is primarily a delay in the flow of mud due to the propagation of waves. Some damping effects may also occur, but their effect may be included in the porosity modeling of the shaker screen. The damping and porosity effects can be approximated with a first order system. Thus, the flow at the outlet of the flow line can be approximated. Another approximation is that the mud density has small variations in each section.

The conservation of mass equation 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 determination of mud density as a function of time. Furthermore, the cuttings may be considered to have a substantially constant density, so the remaining unknowns are the density of the cleaning mud, which may be correlated to the difference between the density of the mud and the density of the removed cuttings.

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

The vibrator volume at pump start-up is easier to resolve from a physical point of view than when the pump is stopped. If the time between the previous pump stop and pump start is long enough, the shaker screen volume may be considered near zero at the start of the pump start because the mud above the shaker screen may have been expelled through the shaker screen when the pump was shut off. Thus, in practice, the initial conditions of the vibrator may be known for the start-up of the pump, e.g. the vibrator volume is stationary.

The calculated vibrator volume at the end of the pump stop may not reach zero but tends towards zero. In particular, the calculated vibrator volume follows a first order response that asymptotically approaches a steady state value (zero when the pump is stopped). Thus, at the start of the next pump start, the initial calculated vibrator volume is not zero (but close to zero) even if the time between the last pump stop and the pump start is long enough (depending on the time constant τ).

In addition, the first order model also introduces error accumulation at other times. Accordingly, the 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-up, and if the pump shut-down is long enough, the shaker volume may be reset to zero at the beginning of each pump start-up. 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., mud volume in the working pit) and flow rate 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 startup. 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 is related 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

The ground loss coefficient beta may be constant under normal conditions, since it is dependent on the equipment configuration. However, when one or more screens of the shaker 306 become clogged, the surface loss coefficient β may change as this reduces cuttings filtration and increases mud buffering above the shaker 306. Furthermore, the surface loss coefficient β may change when the cuttings flow rate changes, as the cuttings flow rate changes may result in changes in the mud coating conditions. Cuttings flow changes may be automatically identified by the mud volume balance model, and mud loss coefficient recalibration may be automatically triggered without any operator input. However, shaker screen plugging may be difficult to predict. Thus, the operator may still need to take precautions to clean the filter or to 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, in addition to other components discussed above, for example, as shown and discussed above with reference to fig. 1. 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 working pit may be expressed at least in part as a function of the flow rate 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 may also include a diversion identification module 208 and a diversion compensation module 210. The measurements taken at 202 may be provided to a diversion compensation module 210 and a diversion identification module 208, and the diversion compensation module 210 and the diversion identification module 208 may modify the mud volume balance calculated in module 206.

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

Thus, the transfer identification module 208 may be generally passive, monitoring the mud level in the pit until a decline or rise or both is detected, e.g., by balancing the mud volume of the reference module 206. In this regard, it is determined whether the decrease or increase affects the liquid level in another pit, for example by examining the mud volume in the respective pits to determine a corresponding change in liquid level. For example, if the mud level in one non-working pit drops, a transfer to another may result in a corresponding increase in the level in the other, and such a transfer may not affect the work 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 the mud flow system.

Mud volume balance can be used to cross check the transfer. The mud volume balance is calculated as the difference between the measured working volume and the theoretical working volume. As described above, mud volume balance compensates for transient effects and has more stable characteristics than the originally measured working volume, for example in a working pit. Thus, when a transfer to the work pit occurs during a pump flow rate change, it may be difficult to identify from merely observing the mud volume in the work pit. However, it is much easier to observe the transfer in mud volume balance. Therefore, the mud balance is used as a reference for transfer cross-checking.

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

Further, as shown in the graph of mud flow 702, mud flow 702 may be unstable at the time of the transfer 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 the transfer event 705. Thus, it can be seen from graph 703 that 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 the liquid level in the working pit to increase. However, mud volume balance 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 the transfer event 705, it represents a mud-up event for the work pit. Coupled 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 increment event (kick) has occurred. Thus, there is a two-factor test to determine the presence of a diversion and to distinguish the diversion from a downhole add/loss event: the change in the volume of the slurry in the non-working pit, and the increase in the balance of the volume of the slurry in the working pit.

The mud volume in the work pit may then be adjusted to compensate for the transfer. This is illustrated by the summation between the transfer compensation module 210 and the mud volume balance module 206 in fig. 2. Compensation uses mud volume balance changes. In fact, the mud volume balance is not affected by transient effects, e.g., by pump flow changes, by cuttings recovery impacts at the shaker, and by surface losses. Thus, a change in mud balance during transfer may be representative of the amount of mud transferred from or to another pit.

Fig. 8 shows a flow diagram of a process 800 for detecting a diversion in a mud system (e.g., mud system 300) as part of the operation of a 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, change point analysis or other segmentation techniques 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-working pits (or non-working pits if the mud system 300 includes a single non-working 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 the non-working pit. Thus, the mud level in the non-working pit is the first trigger to determine whether a transfer is occurring. If this mud level change in the non-working pit does not precede the mud level change in the working pit, the mud level change in the working pit may be attributable to a downhole build-up/loss event or another event not caused by a diversion.

Once a change in mud level in the non-working pit is identified at 804, the process 800 may continue with determining whether 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 pit, and there may be reasons to transfer fluid between these non-working pits, 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 mud volume in the other non-working mud pit accounts for the change, which will keep the mud out of the working system so that the working mud volume is not affected. If the other non-work pit mud level changes to account for the change in the first pit, process 800 proceeds to 808, at 808, the boundary of the non-work pit (baseline level) changes, and process 800 returns to monitoring the mud level in the work pit at 802.

If there is no corresponding change in the other non-working pits (or if there are no other non-working pits, or if the change in the non-working pits does not fully account for the change in the non-working pits identified at 802), process 800 may continue with determining 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 the measured values, 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 working pit detected at 810, 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 a 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 kick-back alarm has been activated, it may be deactivated, as at 812, or not activated. The process 800 may then continue to change the boundary 814 of the working mud volume in order to bring the modeled mud volume back into agreement with the measured mud volume (e.g., recalibrate the model so that the mud balance is at or near zero).

FIG. 9 shows a flow diagram 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 the transient flow period, as at 904. Transient flow periods may occur immediately after the pump starts and stops. In an embodiment, the mud loss during transient flow periods may be primarily due to formation permeability, and this loss may be modeled as described above, e.g., using a 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 loss, such as mud loss from the shaker and drill cuttings. In some embodiments, the mud loss during the steady-state flow period may be modeled based on a surface loss coefficient β, as discussed above.

The 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 working pit is a pit through which mud is circulated during normal pumping operations. The non-working pit may store a mud reserve and mud may be transferred from the non-working pit to the working pit for use in the mud system. Thus, during normal pumping operations, fluid does not continuously circulate through the non-working pit and into/out of the well.

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

The method 900 may periodically (re) calibrate a model of one or both of the transient loss and/or the steady state loss, 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 a shaker screen becomes plugged. The transient losses may be recalibrated after the first pump start, or after each pump start, etc.

The 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 pit, as at 914. As discussed above, the detection of metastasis 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, and therefore any change in mud volume in the working pit may be due to other conditions, such as an increase or loss of mud downhole.

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 the non-working pit and the 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, for example, transient flow conditions may make it difficult to identify changes in the work pit volume.

Thus, the method 900 may base the detection of mud diversion on a deviation of the mud volume balance by a certain amount, e.g., a change in mud volume corresponding to (or substantially the same as) a non-working pit. If the deviation in mud volume balance corresponds to a change in mud volume in the non-working pit, the method 900 may determine that a diversion has occurred, rather than a downhole increase/loss event, and any kick alarm or the like may be deactivated (or not activated). Further, the 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, the mud volume balance may be prepared to form the basis for downhole increase/loss event detection, for example, by accurately modeling "normal" mud loss in the system (e.g., by vibrators or based on formation permeability) and accounting for the shift in mud volume balance, so as to allow downhole mud loss/increase to be distinguished from normal operation and shift.

Fig. 10 illustrates two

graphs

1000 and 1002 illustrating the operation of a 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, e.g., the volume measured at the working pit) is compared to a theoretical (model-based) working mud volume 1006. In a 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 transfer.

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

The second graph 1002 shows how the detection of the event 1020 affects mud volume balance. The mud volume balance peaks at the beginning of event 1020, as expected from the difference between

lines

1004, 1006.

In response to the event 1020, in at least some embodiments, an alarm can be activated and at least one task of the method 900 can determine whether the alarm 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-volume, 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. Transfer determination is discussed in detail above. If a diversion is determined, the alarm may be disarmed.

In another embodiment, an alarm is not immediately activated in response to detecting event 1020. Instead, a flag or warning may be set in response to the event 1020 and the 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. The method 900 suppresses the activation alarm if a transition occurs, otherwise activates the alarm.

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

From the user's perspective, the revision may be prospective. For example, there may be a delay or buffer in delivering mud measurements to the user so that the transfer can be detected and adapted in the model and the mud volume balance corrected before the user receives the measurements. Alternatively, the mud volume balance may be corrected in a retrospective manner when determining mud diversion. In either case, if a transfer is detected, the alarm may be initially activated and then deactivated, or a decision may be made whether to activate or deactivate such an alarm prior to activation based on whether a transfer 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 working pit volume may be quickly and operatively verified or identified as false and dismissed. In particular, embodiments of the present disclosure may make robust determinations that account for mud loss at the surface and in the well, as well as fluid transfer between non-working and working pits. This may facilitate control and operation of the mud system for circulating mud through the well, for example, by 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 standalone 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 conjunction with one or more processors 1104, the processor(s) 1104 being coupled to one or more storage media 1106. Processor(s) 1104 is also connected to network interface 1107 to allow computer system 1101A to communicate with one or more additional computer systems and/or computing systems, such as 1101B, 1101C and/or 1101D, over data network 1109 (note that

computer systems

1101B, 1101C and/or 1101D may or may not share the same architecture as computer system 1101A and may be located in different physical locations, e.g.,

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 located in different countries in 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.

Storage media 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, storage medium 1106 is depicted as being within computer system 1101A, in some embodiments storage medium 1106 may be distributed within multiple internal and/or external housings of computing system 1101A and/or across computing system 1101A and/or additional computing systems. Storage media 1106 may include one or more of various 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)EPROM (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 tape), optical media (e.g., Compact Disk (CD) or Digital Video Disk (DVD)),

A magnetic disk or other type of optical storage device, or other type of storage device. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system, possibly with multiple nodes. Such computer-readable or machine-readable storage media or media are considered to be part of an article (or article of manufacture). An article of manufacture or article of manufacture may refer to any manufactured single component or multiple components. The one or more storage media may be located in a machine that executes the machine-readable instructions, or at a remote site from which the machine-readable instructions are 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 understood 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 that 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.

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

Computational interpretation, models, and/or other interpretation aids may be refined in an iterative manner; this concept applies to the methods discussed herein. This may include using a feedback loop executed based on an algorithm, for example 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 being considered.

The foregoing description, for purpose 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. Additionally, the order in which the elements of the methods described herein are illustrated and described can be rearranged, and/or two or more elements can be presented simultaneously. 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

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

measuring the volume of working slurry in the working slurry pit and the volume of non-working slurry in the non-working slurry pit;

modeling a modeled working mud volume in a working mud pit;

determining a mud volume balance by calculating a difference between the measured value of 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 a measured value of a non-working mud volume and a change in a mud volume balance in the non-working mud pit; and

downhole buildup and loss are automatically detected based on mud volume balance.

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

3. The method of claim 1, wherein modeling the modeled working mud volume comprises:

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

determining a ground loss factor during the steady state period,

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

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

5. The method of claim 3, further comprising recalibrating the surface loss coefficients during a steady state flow period after the first pump start and before the first pump stop, wherein the surface loss coefficients are not recalibrated after the first pump stop and before the second pump stop.

6. The method of claim 3, further comprising recalibrating the permeability loss coefficient during pump startup before reaching a steady-state flow period after pump startup.

7. The method of claim 1, wherein detecting the diversion of mud comprises:

determining that a 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.

8. The method of claim 7, wherein detecting the diversion of mud further comprises determining that a mud volume in another non-working mud pit has not changed to compensate for the change in non-working mud volume, wherein determining that a mud volume balance has changed to compensate for the non-working mud volume change is also in response to determining that a mud volume in the other non-working mud pit has not changed to compensate.

9. The method of claim 1, further comprising deactivating or refraining from activating a kick alarm in response to detecting the diversion of mud.

10. 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.

11. 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:

modeling a modeled working mud volume in a working mud pit;

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

13. The computing system of claim 11, wherein modeling the modeled working mud volume comprises:

determining a ground loss factor during the steady state period,

14. The computing system of claim 13, wherein the permeability loss coefficient is related to mud flow into or out of a subterranean formation, and wherein the surface loss coefficient is related at least in part to mud flow out of a vibrator of a drilling system.

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

16. The computing system of claim 13, wherein the operations further comprise recalibrating the permeability loss factor during pump startup before reaching a steady-state flow period after pump startup.

17. The computing system of claim 11, wherein detecting the transfer of mud comprises:

18. The computing system of claim 17, wherein detecting the diversion of mud further comprises determining that a mud volume in another non-working mud pit has not changed to compensate for the change in non-working mud volume, wherein determining that a mud volume balance has changed to compensate for the change in non-working mud volume is also in response to determining that a mud volume in another non-working mud pit has not changed to compensate.

19. A computing system, comprising:

one or more processors; and

modeling a modeled working mud volume in a working mud pit;

20. The computing system of claim 19, wherein the operations further comprise, in response to detecting the transfer, correcting the mud volume balance to account for the transfer, and wherein modeling the modeled working mud volume comprises:

determining a ground loss factor during the steady state period,