MONITORING FLUID FLOW THROUGH A PIPELINE IN A CONTEXT OF MUD VOLUME BALANCE

Abstract

A method for monitoring fluid flow through a pipeline includes receiving wellbore input data related to a wellbore. The method also includes determining a reaming state of a drillstring in the wellbore based upon the wellbore input data. The method also includes receiving pipeline input data corresponding to the fluid flow through the pipeline. The fluid flows from the wellbore into the pipeline. The method also includes determining a reaming compensation based upon the reaming state and the pipeline input data. The reaming compensation ignores variations in the pipeline input data that are due to movement of the drillstring in the wellbore. The method also includes adjusting an alarm threshold in response to the reaming compensation.

Description

BACKGROUND

Mud flow-out paddle values are used to measure a fluid flow rate coming out of a wellbore annulus during drilling. More particularly, the values are used to monitor the fluid flow in a pipeline in a context of mud volume balance. Conventionally, a lookback median filter is applied on tumbling windows of the values. However, the size of the tumbling windows is constrained to a narrow window. In addition, the lookback median filter is sensitive to noise and may compromise the resolution of the data. Therefore, what is needed is an improved system and method for monitoring fluid flow through a pipeline in a context of mud volume balance.

SUMMARY

A method for monitoring fluid flow through a pipeline is disclosed. The method includes receiving wellbore input data related to a wellbore. The method also includes determining a reaming state of a drillstring in the wellbore based upon the wellbore input data. The method also includes receiving pipeline input data corresponding to the fluid flow through the pipeline. The fluid flows from the wellbore into the pipeline. The method also includes determining a reaming compensation based upon the reaming state and the pipeline input data. The reaming compensation ignores variations in the pipeline input data that are due to movement of the drillstring in the wellbore. The method also includes adjusting an alarm threshold in response to the reaming compensation.

A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes 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 receiving wellbore input data. The wellbore input data includes a block position (BPOS) of a rig over a wellbore. The operations also include determining a reaming state of a drillstring in the wellbore based upon the input data. The reaming state is determined based upon the BPOS. The reaming state includes not reaming indicating that the drillstring is stationary in the wellbore, reaming up indicating that the drillstring is moving up in the wellbore, or reaming down indicating that the drillstring is moving down in the wellbore. The operations also include receiving pipeline input data corresponding to fluid flowing in a pipeline. The fluid flows from the wellbore into the pipeline. The pipeline input data includes mud flow-out paddle values (MFOP) that are measured in the pipeline. The operations also include filtering the pipeline input data to produce filtered data. Filtering the pipeline input data includes applying a centered sliding median filter to the MFOP. The operations also include determining a reaming compensation based upon the reaming state and the filtered data. The reaming compensation ignores variations in the filtered data that are due to movement of the drillstring in the wellbore. The operations also include adjusting an alarm threshold in response to the reaming compensation. Adjusting the alarm threshold includes increasing the alarm threshold when the reaming state is reaming down. The alarm threshold is adjusted proportionally to a speed at which the drillstring moves in the wellbore.

A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving wellbore input data. The wellbore input data includes a block position (BPOS) of a rig over a wellbore. The operations also include determining (1) a minimum of the BPOS during a monitoring phase, (2) a block velocity (BVEL), and (3) a median of the BVEL over a predetermined time period. The minimum of the BPOS, the BVEL, and the median of the BVEL are determined based upon the BPOS. The BVEL includes a 10-second lookback two-point derivative of the BPOS. The operations also include determining a reaming state of a drillstring in the wellbore based upon the input data. The reaming state is determined based upon the BPOS. The reaming state is determined at a plurality of different time and/or depth intervals in the wellbore based upon the minimum of the BPOS, the BVEL, and the median of the BVEL. The reaming state includes not reaming indicating that the drillstring is stationary in the wellbore, reaming up indicating that the drillstring is moving up in the wellbore, or reaming down indicating that the drillstring is moving down in the wellbore. The operations also include receiving pipeline input data corresponding to fluid flowing in a pipeline. The fluid flows from the wellbore into the pipeline. The pipeline input data includes mud flow-out paddle values (MFOP) that are measured in the pipeline. The operations also include filtering the pipeline input data to produce filtered data. Filtering the pipeline input data includes applying a centered sliding median filter to the MFOP. The centered sliding median filter provides an increased filter window size in comparison to a lookback median filter that is applied on tumbling windows. The centered sliding median filter is less sensitive to noise without compromising resolution in comparison to the lookback median filter applied on tumbling windows. The operations also include determining a reaming compensation based upon the reaming state and the filtered data. The reaming compensation ignores variations in the filtered data that are due to movement of the drillstring in the wellbore. The operations also include adjusting an alarm threshold in response to the reaming compensation. Adjusting the alarm threshold comprises increasing the alarm threshold when the reaming state is reaming down. The alarm threshold is not adjusted when the reaming state is not reaming or reaming up. The alarm threshold is adjusted proportionally to a speed at which the drillstring moves in the wellbore. The alarm threshold is adjusted independently of a rate of rotation of the drillstring in the wellbore.

It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

FIG. 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

FIG. 2 illustrates a flowchart of a method for monitoring fluid flow through a pipeline, according to an embodiment.

FIG. 3 illustrates the raw unfiltered flow out paddle signal (top) and the resulting signal 320 after passing through the centered median filter (bottom), according to an embodiment.

FIG. 4 illustrates the input flow being pumped into the well (top) and the derived flow segmentation, computed upstream, which is used to define the monitoring domain of the algorithm (bottom), according to an embodiment.

FIG. 5 illustrates the movements of the block, the computed block velocity, and the final reaming regime segmentation results, according to an embodiment.

FIG. 6 illustrates the damping of local dips in flow out, according to an embodiment.

FIG. 7 illustrates the effects of applying a double threshold and renewing the reference point, according to an embodiment.

FIG. 8 illustrates the dynamic alarm threshold adjustments made in response to the detected reaming regime, according to an embodiment.

FIG. 9 illustrates the merging of alarms which are too close to each other in terms of time separation, according to an embodiment.

FIG. 10 illustrates management of flow-out changes showing issues related to the current definition of the monitoring domain, which can be too sensitive to local flow in instabilities.

FIGS. 11 and 12 illustrate schematic side views of fluid flowing from a wellbore and into a pipeline, according to an embodiment.

FIG. 13 illustrates a flowchart of a method for monitoring fluid flow through a pipeline in a context of mud volume balance (MVB), according to an embodiment.

FIG. 14 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed.

System Overview

FIG. 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

In the example of FIG. 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well/logging data), a processing component 116 (e.g., including calibration of the processing results with well data), a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120. The other information may be or include well data. The components, 112, 114 may be or include well data such as well logs, drilling logs, and/or cores, which may be used to calibrate the seismic data to rock and fluid properties.

In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, well data (e.g., used for calibration of rock and fluid properties), surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

In the example of FIG. 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of FIG. 1, the analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).

In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

FIG. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

In the example of FIG. 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

In the example of FIG. 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

In the example of FIG. 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a workstep may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).

Monitoring Fluid Flow Through a Pipeline in a Context of Mud Volume Balance

The present disclosure includes a mud volume balance (MVB) flow-out method that is configured to automatically detect abnormally high flow paddle values when the total input flow is stable (e.g., including booster flow if it is present). The actual monitoring phase may be delayed (e.g., by 5 minutes) after the total input flow becomes stable. This is to absorb the transient period during which the flow-out is not expected to be stable yet even though the input flow is stable already. During riser-less operations, the alarm may be disabled.

FIG. 2 illustrates a flowchart of a method for monitoring fluid flow through a pipeline, according to an embodiment.

Flow-Out Filtering

This operation applies a median filter on a centered sliding window. In an example, the window size is 60 seconds. Therefore, the output of this operation may be delayed by 30 seconds compared to the input. FIG. 3 illustrates the raw unfiltered flow out paddle signal 310 (top) and the resulting signal 320 after passing through the centered median filter (bottom), according to an embodiment.

Processing-related delays may not appear when looking at historical results, except at the very end of the signal. In this example, the last 30 seconds of filtered flow-out may not be visible at the end of the well. For such preprocessing steps, the output timestamps may be aligned with the original input timestamps, as operations may not be composable otherwise. In general, processing-related delay information may be delivered as an additional channel for analysis and troubleshooting purposes.

Monitoring and Reaming Detection

These operations may not introduce any delay. In other words, the output information may match with the timestamp and/or event time of the last added input point, and the resulting output point may be delivered right after the input point is added in the input stream.

Monitoring

FIG. 4 illustrates the input flow being pumped into the well (top) and the derived flow segmentation (TOTAL_FLOW_STATE), computed upstream, which is used to define the monitoring domain of the algorithm (bottom), according to an embodiment. More particularly, FIG. 4 shows the total input flow (e.g., injected into the well) 410, the filtered flow out paddle signal 420 (e.g., in the monitoring domain with active alarm detection). The monitoring becomes active when a predetermined amount of time (e.g., 5 minutes) has elapsed while the TOTAL_FLOW_STATE (e.g., output from operation states which consists of the sum of ‘flow in’ and ‘booster flow’ if it is present) is STABLE as per PIT_STATE algorithm criteria. The monitoring may be disabled when TOTAL_FLOW_STATE goes out of the STABLE state.

Reaming Detection

The flow-out signal may be impacted by the drill string movements while reaming. More particularly, a suction effect happens when the drill string is pulled upwards, causing the flow-out to decrease. In addition, a pressure wave travels down the annulus and displaces fluid upward when the drillstring is pushed downward, causing the flow out to increase. The greater the magnitude of the string speed, the greater the impact on flow-out. To compensate for these effects, a reaming detection algorithm may perform a segmentation of the block height signal.

Three distinct states/regimes may be identified:

• • 1. not reaming
  • 2. reaming up
  • 3. reaming down

To define the transitions from one state to the other, several temporary values may be determined: block velocity (BVEL), a median of the block velocity (BVEL_MED), and/or a minimum of a block position (MinBPOS). In an example, BVEL may be defined as the 10 seconds lookback two-points derivative of the BPOS signal. In an example, BVEL_MED may be defined as the lookback BVEL median over the last 30 secs. In an example, MinBPOS may be defined as the minimum BPOS value encountered during a monitoring phase.

The default state is “not reaming”. If the current BPOS value is within a predetermined distance (e.g., 10 cm) of MinBPOS, “not reaming” is enforced. When the current BVEL exceeds BVEL_MED*3+5 cm/s, the condition for entering the reaming up regime is met. When reaming up, a reaming down state or a pump stop may occur.

When a reaming down state is expected to occur, and when the current BVEL falls below −5 cm/s+BVEL_MED/3, the condition for entering the reaming down regime may be satisfied. When reaming down, to reach MinBPOS and/or max depth within the monitoring period (e.g., this may lead to a transition to the “not reaming” state), a change of direction, leading to the reaming up regime if the reaming up entering criteria is met, and not reaming otherwise. In another embodiment, when reaming down, the pumps may stop the monitoring and reset the reaming detection state machine.

In the reaming up regime, if BVEL becomes negative, but the reaming down entering condition is not met, the system may go in the “not reaming” regime, still expecting a reaming down to come. In the reaming down regime, if BVEL becomes positive, it may go in the “reaming up” regime, also expecting a reaming down to come.

Combining Operation

FIG. 5 illustrates the movements of the block 510, the computed block velocity 520, and the final reaming regime segmentation results, according to an embodiment. FIG. 5 also shows monitoring intervals when circulating and reaming. The result of this segmentation step may be used in the combining step. This operation combines the output of the flow-out filtering operation and the output of the monitoring and reaming detection to determine an alarm threshold above which the returned alarm should be on or off. It does not introduce any additional delay on top of upstream operations.

If the input monitoring state is “not monitoring”, then this step does not produce any additional information. The following assumes a “monitoring” state. The computation retains a rolling minimum of filtered flow-out points over each monitoring period, following an “ascending minima” algorithm. At any point in time, the “current minimum” (=the latest minimum point in the ascending minima buffer) may be the “reference point”. It contains the flow-out value from which the increase is tracked. If the current filtered flow-out value exceeds the reference value by more than an absolute delta of 0.05 (or 5%), an alarm may be triggered.

Damping of Local Decreases

FIG. 6 illustrates the damping of local dips in flow out, according to an embodiment. More particularly, FIG. 6 shows the total input flow (e.g., injected into the well) 610, the raw unfiltered flow out paddle signal 620 in the monitoring domain, the filtered flow-out 630, and the alarm threshold 640. The damping management may help to minimize false alarms. Sometimes, “local decreases” may be observed in the flow-out signal, causing the reference flow-out value to decrease. As long as they happen over a relatively short period in time, these dips in flow-out may be ignored and may have no impact on the resulting alarm threshold. To address this, a rolling maximum is used as well. In an example, it may be limited to a duration of 1 minute. Therefore, when the reference flow-out value decreases, the threshold may not be impacted before 1 minute has elapsed.

Double/Hysteresis Thresholding

FIG. 7 illustrates the effects of applying a double threshold and renewing the reference point, according to an embodiment. More particularly, FIG. 7 shows the total input flow (e.g., injected into the well) 710, the raw unfiltered flow out paddle signal 720 in the monitoring domain, the filtered flow-out 730, the alarm threshold 740, and the alarms 750.

To decrease the frequency of untimely alarm triggers that happen when the flow-out remains too close to the threshold value, a double threshold/hysteresis trigger may be implemented. In an example, a threshold of 0.05 (or 5%) may be used to trigger the alarm on, and a threshold of 0.01 (or 1%) may be used to trigger the alarm off. Therefore, when an alarm is switched off, this causes the flow-out signal to be at least 0.04 (or 4%) apart from the triggering threshold. Also, it has been observed that the flow-out sometimes stabilizes in two steps. Therefore, to avoid a continuous alarm on after the last step increase, the current reference point may be ignored if the alarm is in “on” state. The next reference point being used is its successor in the current rolling minimum sequence. Although alarms may appear to have a short duration when looking at historical results, they may not last less than 5 minutes in real-time, as explained below.

Special Processing when Reaming

If the reaming regime is different from “not reaming”, the combining operation may follow specific processing rules:

• • 1. If just entered a reaming up phase, a reaming down may be expected at some point and:
    • I. Some information is kept in memory until a reaming down phase is encountered:
      • a. The alarm threshold
      • b. The flow-out value just before entering this reaming up phase
    • II. New information is aggregated while streaming in reaming up regime:
      • a. The minimum flow encountered during this reaming up phase
      • b. The maximum reaming up speed/BVEL during this reaming UP phase
  • 2. While remaining in the reaming up phase, the minimum flow II.a and maximum speed II.b are updated while streaming.
  • 3. If just entered a reaming down phase, the items memorized and aggregated during the reaming up phase may be used to build a small model whose goal is to adjust the alarm threshold in order to compensate for the expected flow increase during the reaming down phase, in function of the current BVEL value.
  • 4.While remaining in the reaming down phase, the threshold may be adjusted according to the model built in step 3. The model gives an upper bound for the current maximum expected flow considering what was observed during the reaming up phase, and the alarm threshold is adjusted in consequence.

FIG. 8 illustrates the dynamic alarm threshold adjustments made in response to the detected reaming regime, according to an embodiment. More particularly, FIG. 8 shows the filtered flow-out 810, the alarm threshold 820, the block position (e.g., BPOS) 830, and the block velocity (BPOS derivative in a 10 second window) 840). Several estimates may be made (e.g., some dependent or independent from BVEL), and the most pessimistic/impacting estimate on the flow-out may be used for the adjusted threshold.

Combine/Join (Short-Circuit)

This operation gathers the outputs from the “monitoring and reaming” operation and the “combining operation”. When the “monitoring and reaming” operation returns a “not monitoring” state, an alarm “off” may be returned early (e.g., independently of the delay introduced by the flow-out filtering step). Otherwise, the alarm result from the “combining operation” may be returned, and it can be either off or on.

Alarm Merging

FIG. 9 illustrates the merging of alarms which are too close to each other in terms of time separation, according to an embodiment. More particularly, FIG. 9 shows the total input flow (e.g., injected into the well) 910, the raw unfiltered flow out paddle signal (e.g., in the monitoring domain) 920, the filtered flow-out signal 930, the alarm threshold 940, the alarms 950, the alarms (e.g., merged) 960, and the raw underlying alarms 970. The alarm merging operation may merge two alarms if they are less than a predetermined time (e.g., 5 minutes) apart. This works by delaying the alarm on to off by at most 5 minutes, and closing the alarm if there are no other triggers in this period. This may not introduce any delay when triggering an alarm on. This implies that, in real-time, while staying within the monitoring domain, an alarm may not be closed within 5 minutes of its opening time, independently of how short the alarm may look in the historical results.

Optional Changes

FIG. 10 illustrates management of flow-out changes showing issues related to the current definition of the monitoring domain, which can be too sensitive to local flow in instabilities. More particularly, FIG. 10 shows the total input flow (e.g., injected into the well) 1010, the raw unfiltered flow out paddle signal (e.g., in the monitoring domain) 1020, the alarms (e.g., merged) 1030, the raw underlying alarms 1040, the filtered flow-out signal 1050, and the alarm threshold 1060. The centered median filtering may be changed to a dynamic bilateral filter or equivalent to reduce the alarm delay in extreme cases where an alarm on can be reported early. The 5 minute delay before starting to monitor may be adjusted dynamically based on past observations. The limit on the monitoring domain may be removed, and the monitoring may instead be continuous. This implies building a model which dynamically predicts what the flow-out should be (e.g., with a confidence score) depending on the raw unsegmented flow in. Thus, the computation may be independent from the TOTAL_FLOW_STATE output from operation states. This may provide better results on the rare occasions where the input flow changes at a slow rate or when it is noisy.

The kind of state segmentation used to qualify the reaming regime may be avoided as well, as it may be fragile and ill-adapted to some shapes of BPOS variations. There may be a different way to build an approximation of a linear or quasi-linear filter modelling the impact of the drill string movements on the flow-out in a more continuous fashion (e.g., the same way the monitoring domain may be extended not to rely on TOTAL_FLOW_STATE segmentation). Also, it may be beneficial to take advantage of the flow-out filtering delay to take more time to produce a more accurate result.

In addition, “controls engineering” and “data science” and/or “machine learning” methodologies used to solve “grey box system identification” problems may be beneficial for this kind of computation. This may be used to continuously approximate the flow-out “expected response” depending on the inputs having an impact on it, and help to build more simple, general, predictable, and robust models.

FIGS. 11 and 12 illustrate schematic side views of fluid flowing from a wellbore 1100 and into a pipeline 1130, according to an embodiment. More particularly, the fluid may flow down a drill string 1110 in the wellbore 1100 and then up an annulus 1120. The fluid may then flow from the annulus 1120 into the pipeline (also referred to as flowline) 1130. A sensor (e.g., flow paddle) 1140 may measure the flow rate of the fluid in the pipeline 1130. As the drill string 1110 moves up (FIG. 11), a negative volume is displaced. Thus, for a constant flow rate into the drill string 1110, the flow out (e.g., and into the pipeline 1130) decreases. Conversely, as the drill string 1110 moves down (FIG. 12), a positive volume is displaced. Thus, for a constant flow rate into the drill string 1110, the flow out (e.g., and into the pipeline 1130) increases.

The return flow sensor may provide a first warning of a kick, particularly in gas prone areas. The volume of a gas kick at bottom of the wellbore may be less than the limit of resolution for the pit level sensors. The return flow from the well may increase with no change in pump rate when the pumping rate (i.e., flow rate in) is constant. The flow rate out from the wellbore 1110 may be equal to the flow rate into the wellbore 1110 using quantitative flow rate measuring equipment (e.g., FLAG) and may show a steady trend with no abnormal deviations on the qualitative equipment (e.g., flow paddle, flow show indicator). If there is an increase in the flow rate out, it may be indicative of formation fluid flowing into the wellbore 1110. Invading fluids are generally lighter than mud. This further reduces the bottom hole pressure (BHP) and may cause more formation fluid to flow into the wellbore 1110.

In a gas influx, the size of the gas bubbles increases as they move up the well, which may increase flow rate out (and pit gain). This may result in a further reduction of the bottom hole pressure, providing more favorable conditions for the formation fluid to flow into the wellbore 1110. This may cause the drilling fluid to flow from the wellbore 1110 into the low pressured zone (e.g., depleted zone), resulting in mud losses or lost circulation/lost returns. Mud losses or lost returns may also occur when drilling into a fractured zone. Mud losses or lost returns may cause the reduction of hydrostatic column length in the annulus, which in turn reduces the BHP. Formation pressures of the zones drilled earlier, which initially was lower than BHP, may now become higher than BHP and may start flowing into wellbore 1110. This may result in an increase in the flow rate out. The flow rate out (i.e., return flow from the wellbore 1110) when the pumps are off (e.g., during a pipe connection or a flow check) should result in no return flow from the wellbore 1110, as nothing is pumped into it. If the wellbore 1110 continues to flow when the pumps are off, it may mean that the formation fluid is flowing into the wellbore 1110. The BHP pressures may be higher during circulating (e.g., BHP=ECD) than when the pumps are off (e.g., BHP=ESD)

Exemplary Method

FIG. 13 illustrates a flowchart of a method 1300 for monitoring fluid flow through a pipeline 1130 in a context of mud volume balance (MVB), according to an embodiment. An illustrative order of the method 1300 is provided below; however, one or more portions of the method 1300 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 1300 may be performed with a computing system (described below).

The method 1300 may include receiving wellbore input data, as at 1305. The wellbore input data may include a block position (BPOS) of a rig over a wellbore 1110. An example of the wellbore input data may be seen in FIG. 5 (e.g., block position 510).

The method 1300 may also include determining a minimum of the BPOS during a monitoring phase, a block velocity (BVEL), and/or a median of the BVEL over a predetermined time period (e.g., 30 seconds), as at 1310. Examples of these parameters may be seen in FIG. 5 (e.g., block velocity 520). The minimum of the BPOS, the BVEL, and the median of the BVEL may be determined based upon the BPOS. The BVEL may include a 10-second lookback two-point derivative of the BPOS.

The method 1300 may also include determining a reaming state of a drillstring 1120 in the wellbore 1110 based upon the input data, as at 1315. An example of the reaming state(s) may be seen in FIG. 5 (e.g., not reaming, reaming up, reaming down). The reaming state may be determined based upon the BPOS. The reaming state may be determined at a plurality of different time and/or depth intervals in the wellbore 1110 based upon the minimum of the BPOS, the BVEL, and/or the median of the BVEL. The reaming state may include “not reaming” indicating that the drillstring 1120 is stationary in the wellbore 1110, “reaming up” indicating that the drillstring 1120 is moving up in the wellbore 1110, or “reaming down” indicating that the drillstring 1120 is moving down in the wellbore 1110.

The method 1300 may also include receiving pipeline input data corresponding to fluid flowing in a pipeline 1130, as at 1320. An example of the pipeline input data may be seen in FIG. 3 (e.g., flow out paddle signal 310). The fluid flows from the wellbore 1110 into the pipeline 1130. The pipeline input data may include mud flow-out paddle values (MFOP) that are measured by the sensor 1140 in the pipeline 1130.

The method 1300 may also include filtering the pipeline input data to produce filtered data, as at 1325. An example of the pipeline input data may also be seen in FIG. 3 (e.g., flow out paddle signal 320 after centered median filtering). Filtering the pipeline input data may include applying a centered sliding median filter to the MFOP. The centered sliding median filter provides an increased filter window size in comparison to a lookback median filter that is applied on tumbling windows. The centered sliding median filter is less sensitive to noise without compromising resolution in comparison to the lookback median filter applied on tumbling windows.

The method 1300 may also include determining a reaming compensation based upon the reaming state and the pipeline input data (e.g., the filtered data), as at 1330. The reaming compensation ignores variations in the filtered data (MFOP) that are due to movement of the drillstring 1120 in the wellbore 1110.

The method 1300 may also include adjusting an alarm threshold in response to the reaming compensation, as at 1335. An example of the reaming compensation and/or the adjusted alarm threshold may be seen in FIG. 8 (e.g., alarm threshold 820). Adjusting the alarm threshold may include increasing the alarm threshold when the reaming state is reaming down. The alarm threshold may not be adjusted when the reaming state is not reaming and/or reaming up. The alarm threshold may be adjusted proportionally to a speed at which the drillstring 1120 moves in the wellbore 1110. The alarm threshold may be adjusted independently of a rate of rotation of the drillstring 1120 in the wellbore 1110.

In an embodiment, the compensated alarm threshold may be built empirically from the uncompensated one and is subject to change because it may not be backed by any strong theoretical model. When reaming up, the compensated alarm threshold may be frozen (i.e., remain constant). More particularly, it remains equal to the uncompensated alarm threshold right before entering the reaming up regime. When reaming down, an offset may first be determined. The offset may be proportional to the current BVEL, and may be added to a computed base alarm threshold. The current base alarm threshold may be the maximum of: (1) the last known uncompensated alarm threshold right before going into the preceding reaming up regime and/or (2) the last known uncompensated alarm threshold right before going into the preceding reaming up regime, plus the maximum loss in flow observed during the preceding reaming up regime. In the end, the final compensated alarm threshold may be the maximum of both uncompensated and compensated alarm thresholds.

The method 1300 may also include displaying the wellbore input data, the pipeline input data, the filtered data, the reaming compensation, and/or the adjusted alarm threshold, as at 1340.

The method 1300 may also include triggering the alarm in response to the filtered data exceeding the adjusted alarm threshold, as at 1345.

The method 1300 may also include performing an action in response to the alarm being triggered, as at 1350. The action may be or include generating and/or transmitting a signal (e.g., using a computing system) that recommends, instructs, or causes a physical action to occur in the wellbore and/or pipeline. The action may also or instead include performing the physical action. The physical action may include selecting where to drill the wellbore (or a new wellbore) 1110, drilling the wellbore, varying a weight and/or torque on a drill bit that is drilling the wellbore, varying a drilling trajectory of the wellbore, varying a concentration and/or flow rate of a fluid pumped into the wellbore, or the like. The action may also or instead include actuating a pump or a valve to change the flow rate or pressure of the fluid flowing through the pipeline 1130.

Exemplary Computing System

In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 14 illustrates an example of such a computing system 1400, in accordance with some embodiments. The computing system 1400 may include a computer or computer system 1401A, which may be an individual computer system 1401A or an arrangement of distributed computer systems. The computer system 1401A includes one or more analysis modules 1402 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 1402 executes independently, or in coordination with, one or more processors 1404, which is (or are) connected to one or more storage media 1406. The processor(s) 1404 is (or are) also connected to a network interface 1407 to allow the computer system 1401A to communicate over a data network 1409 with one or more additional computer systems and/or computing systems, such as 1401B, 1401C, and/or 1401D (note that computer systems 1401B, 1401C and/or 1401D may or may not share the same architecture as computer system 1401A, and may be located in different physical locations, e.g., computer systems 1401A and 1401B may be located in a processing facility, while in communication with one or more computer systems such as 1401C and/or 1401D that are located in one or more data centers, and/or located in varying countries on different continents).

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

The storage media 1406 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of FIG. 14 storage media 1406 is depicted as within computer system 1401A, in some embodiments, storage media 1406 may be distributed within and/or across multiple internal and/or external enclosures of computing system 1401A and/or additional computing systems. Storage media 1406 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. 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 having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In some embodiments, computing system 1400 contains one or more de-risking module(s) 1408. In the example of computing system 1400, computer system 1401A includes the de-risking module 1408. In some embodiments, a single de-risking module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of de-risking modules may be used to perform some aspects of methods herein.

It should be appreciated that computing system 1400 is merely one example of a computing system, and that computing system 1400 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of FIG. 14, and/or computing system 1400 may have a different configuration or arrangement of the components depicted in FIG. 14. The various components shown in FIG. 14 may be implemented in hardware, software, or a combination of both 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 modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.

Computational interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 1400, FIG. 14), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic 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 limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, 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 fluid flow through a pipeline, the method comprising:

receiving wellbore input data related to a wellbore;

determining a reaming state of a drillstring in the wellbore based upon the wellbore input data;

receiving pipeline input data corresponding to the fluid flow through the pipeline, wherein the fluid flows from the wellbore into the pipeline;

determining a reaming compensation based upon the reaming state and the pipeline input data, wherein the reaming compensation ignores variations in the pipeline input data that are due to movement of the drillstring in the wellbore; and

adjusting an alarm threshold in response to the reaming compensation.

2. The method of claim 1, wherein the wellbore input data comprises a block position (BPOS) of a rig over the wellbore.

3. The method of claim 2, wherein the reaming state is determined based upon the BPOS.

4. The method of claim 2, further comprising determining a minimum of the BPOS, a block velocity (BVEL), a median of the BVEL, or a combination thereof, wherein the reaming state is determined based upon the minimum of the BPOS, the BVEL, the median of the BVEL, or the combination thereof.

5. The method of claim 1, wherein the reaming state comprises not reaming indicating that the drillstring is stationary in the wellbore, reaming up indicating that the drillstring is moving up in the wellbore, or reaming down indicating that the drillstring is moving down in the wellbore.

6. The method of claim 1, wherein the pipeline input data comprises mud flow-out paddle values (MFOP) that are measured in the pipeline.

7. The method of claim 6, further comprising filtering the pipeline input data to produce filtered data, wherein filtering the pipeline input data comprises applying a centered sliding median filter to the MFOP, and wherein the reaming compensation is also based upon the filtered data.

8. The method of claim 1, wherein adjusting the alarm threshold comprises increasing the alarm threshold when the reaming state is reaming down.

9. The method of claim 1, wherein the alarm threshold is adjusted proportionally to a speed at which the drillstring moves in the wellbore.

10. The method of claim 1, wherein the alarm threshold is adjusted independently of a rate of rotation of the drillstring in the wellbore.

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, the operations comprising: receiving wellbore input data, wherein the wellbore input data comprises a block position (BPOS) of a rig over a wellbore; determining a reaming state of a drillstring in the wellbore based upon the input data, wherein the reaming state is determined based upon the BPOS, and wherein the reaming state comprises not reaming indicating that the drillstring is stationary in the wellbore, reaming up indicating that the drillstring is moving up in the wellbore, or reaming down indicating that the drillstring is moving down in the wellbore; receiving pipeline input data corresponding to fluid flowing in a pipeline, wherein the fluid flows from the wellbore into the pipeline, wherein the pipeline input data comprises mud flow-out paddle values (MFOP) that are measured in the pipeline; filtering the pipeline input data to produce filtered data, wherein filtering the pipeline input data comprises applying a centered sliding median filter to the MFOP; determining a reaming compensation based upon the reaming state and the filtered data, and wherein the reaming compensation ignores variations in the filtered data that are due to movement of the drillstring in the wellbore; and adjusting an alarm threshold in response to the reaming compensation, wherein adjusting the alarm threshold comprises increasing the alarm threshold when the reaming state is reaming down, and wherein the alarm threshold is adjusted proportionally to a speed at which the drillstring moves in the wellbore.

12. The computing system of claim 11, further comprising determining a minimum of the BPOS, a block velocity (BVEL), a median of the BVEL, or a combination thereof based upon the BPOS.

13. The computing system of claim 12, wherein the reaming state is determined at a plurality of different time and/or depth intervals in the wellbore based upon the minimum of the BPOS, the BVEL, and/or the median of the BVEL.

14. The computing system of claim 11, wherein the centered sliding median filter provides an increased filter window size in comparison to a lookback median filter that is applied on tumbling windows, and wherein the centered sliding median filter is less sensitive to noise without compromising resolution in comparison to the lookback median filter applied on tumbling windows.

15. The computing system of claim 11, wherein the alarm threshold is not adjusted when the reaming state is not reaming or reaming up.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:

receiving wellbore input data, wherein the wellbore input data comprises a block position (BPOS) of a rig over a wellbore;

determining (1) a minimum of the BPOS during a monitoring phase, (2) a block velocity (BVEL), and (3) a median of the BVEL over a predetermined time period, wherein the minimum of the BPOS, the BVEL, and the median of the BVEL are determined based upon the BPOS, and wherein the BVEL comprises a 10-second lookback two-point derivative of the BPOS;

determining a reaming state of a drillstring in the wellbore based upon the wellbore input data, wherein the reaming state is determined based upon the BPOS, wherein the reaming state is determined at a plurality of different time and/or depth intervals in the wellbore based upon the minimum of the BPOS, the BVEL, and the median of the BVEL, and wherein the reaming state comprises not reaming indicating that the drillstring is stationary in the wellbore, reaming up indicating that the drillstring is moving up in the wellbore, or reaming down indicating that the drillstring is moving down in the wellbore;

receiving pipeline input data corresponding to fluid flowing in a pipeline, wherein the fluid flows from the wellbore into the pipeline, wherein the pipeline input data comprises mud flow-out paddle values (MFOP) that are measured in the pipeline;

filtering the pipeline input data to produce filtered data, wherein filtering the pipeline input data comprises applying a centered sliding median filter to the MFOP, wherein the centered sliding median filter provides an increased filter window size in comparison to a lookback median filter that is applied on tumbling windows, and wherein the centered sliding median filter is less sensitive to noise without compromising resolution in comparison to the lookback median filter applied on tumbling windows;

determining a reaming compensation based upon the reaming state and the filtered data, and wherein the reaming compensation ignores variations in the filtered data that are due to movement of the drillstring in the wellbore; and

adjusting an alarm threshold in response to the reaming compensation, wherein adjusting the alarm threshold comprises increasing the alarm threshold when the reaming state is reaming down, wherein the alarm threshold is not adjusted when the reaming state is not reaming or reaming up, wherein the alarm threshold is adjusted proportionally to a speed at which the drillstring moves in the wellbore, and wherein the alarm threshold is adjusted independently of a rate of rotation of the drillstring in the wellbore.

17. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise displaying the filtered data and the adjusted alarm threshold.

18. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise triggering the alarm in response to the filtered data exceeding the adjusted alarm threshold.

19. The non-transitory computer-readable medium of claim 18, wherein the operations further comprise performing an action in response to the alarm being triggered.

20. The non-transitory computer-readable medium of claim 19, wherein the action comprises generating or transmitting a signal that recommends, instructs, or causes a physical action to occur in the wellbore and/or the pipeline.