ASSESSING CURABILITY IN LOST CIRCULATION EVENTS

Abstract

A method for characterizing a subsurface interval where drilling fluid is being lost includes receiving surface drilling values captured while drilling a wellbore through the subsurface interval. The method also includes analyzing the surface drilling values and identifying on-bottom surface drilling values and on-bottom mechanical specific energy (MSE) values. The method also includes identifying a first changepoint in the on-bottom surface drilling values. The method also includes identifying a second changepoint in the on-bottom MSE values. The method also includes determining a cause of a lost circulation event in the subsurface interval based upon the first and second changepoints.

Description

BACKGROUND

Loss circulation events generally occur when drilling fluid circulated into a well is lost into the formation, rather that maintaining pressure, facilitating drilling activities, etc. There are different mechanisms that drive lost circulation events. Some of those mechanisms may be cured with mitigation techniques, while other mechanisms are uncurable. Conventional workflows generally focus on proposing an appropriate lost circulation curation method. For example, conventional methods have been proposed to control the loss of drilling fluid and/or estimating modeling fracture properties in the wellbore, but not whether a lost circulation event is curable or worth attempted curation.

Lost circulation occurs approximately in over 26% of the wells worldwide and costs a few billion dollars a year. The losses may be seepage, partial, severe, or total, and may be classified based on the nature of losses and type of lost circulation formation. By nature, the losses may be subdivided into induced and natural. In the induced losses, the hydrostatic or hydrodynamic pressure of the drilling/completion fluid exceeds the formation strength and causes the lost circulation. In the natural losses, the existing fracture(s) (e.g., karst, void, or cavern) crossed while drilling. By formation types, the following categories may be classified: highly permeable formations, cavernous formations, natural fractures, and induced fractures.

SUMMARY

A method for characterizing a subsurface interval where drilling fluid is being lost is disclosed. The method includes receiving surface drilling values captured while drilling a wellbore through the subsurface interval. The method also includes analyzing the surface drilling values and identifying on-bottom surface drilling values and on-bottom mechanical specific energy (MSE) values. The method also includes identifying a first changepoint in the on-bottom surface drilling values. The method also includes identifying a second changepoint in the on-bottom MSE values. The method also includes determining a cause of a lost circulation event in the subsurface interval based upon the first and second changepoints.

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 surface drilling values captured while drilling a wellbore through a subsurface interval. The operations also include analyzing the surface drilling values and identifying on-bottom surface drilling values and on-bottom mechanical specific energy (MSE) values. The operations also include identifying a first changepoint in the on-bottom surface drilling values. The operations also include identifying a second changepoint in the on-bottom MSE values. The operations also include identifying a first portion of a subsurface interval based upon the first changepoint and the second changepoint. The operations also include determining a cause of a lost circulation event in the wellbore based upon the first portion of the subsurface interval.

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 surface drilling values captured while drilling a wellbore through a subsurface interval. The surface drilling values include standpipe pressure (SPPA) values. The operations also include identifying on-bottom surface drilling values based upon the surface drilling values. The on-bottom surface drilling values include on-bottom SPPA values based upon the SPPA values, which represent a subset of the SPPA values that occurs while a drill bit is on-bottom in the wellbore. The operations also include identifying on-bottom mechanical specific energy (MSE) values based upon the surface drilling values. The operations also include identifying first changepoints in the on-bottom surface drilling values that are greater than a first threshold. The first changepoints are identified in the on-bottom SPPA values. The operations also include identifying second changepoints in the on-bottom MSE values that are greater than a second threshold. The operations also include determining a cause of a lost circulation event based upon the first and second changepoints.

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 schematic view of a wellbore extending through several different types of formations.

FIG. 3 illustrates a table of failure mechanisms as they relate to observed MSE behavior in a well, according to an embodiment.

FIG. 4 illustrates plots of time series (STOR, ROP, sWOB, MSE, RIG_STATE, BPOS) in a well, prior to total losses event, and in the stand drilled to expose the formation when encountering total losses, according to an embodiment.

FIG. 5 illustrates plots of time series (STOR, ROP, SWOB, MSE, TD) in a well, in the stand drilled to expose the formation when encountering total losses.

FIG. 6 illustrates a flowchart of a method, according to an embodiment.

FIG. 7A illustrates a plot in which two Karsts are visible, according to an embodiment.

FIG. 7B illustrates calipers in total losses zones in a well are plotted, showing large cavities, according to an embodiment.

FIG. 8 illustrates an MSE distribution that was collected drilling through a formation, according to an embodiment.

FIG. 9 shows plots of a well log and an identification of lost circulation events at depth intervals, according to an embodiment.

FIG. 10 illustrates a flowchart of a conductive drilling break algorithm diagram, according to an embodiment.

FIG. 11 illustrates drilling parameters for a well referred to as Well A, according to an embodiment.

FIG. 12 illustrates additional drilling parameters for Well A, according to an embodiment.

FIG. 13 illustrates additional drilling parameters for Well A, according to an embodiment.

FIG. 14 illustrates additional drilling parameters for Well A, according to an embodiment.

FIG. 15 illustrates drilling parameters and estimated downhole weight on bit and downhole torque for Well A, according to an embodiment.

FIG. 16 illustrates drilling parameters and time segments where the MSE is homogeneous on Well A, according to an embodiment.

FIG. 17 illustrates drilling parameters and segments characteristics for Well A, according to an embodiment.

FIG. 18 illustrates drilling parameters and segments characteristics for Well A during a lost circulation event, according to an embodiment.

FIG. 19 illustrates drilling parameters, drilling breaks, and borehole shapes from wireline calipers for Well A, according to an embodiment.

FIG. 20 illustrates drilling parameters and drilling breaks, which provides an overview of Well A, according to an embodiment.

FIG. 21 illustrates a flowchart of a method for characterizing a subsurface interval where drilling fluid is being lost, according to an embodiment.

FIG. 22 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.

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, 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.

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, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 may 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 and other information). 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 may 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 (Schlumberger Limited, Houston Texas), the INTERSECT™ reservoir simulator (Schlumberger Limited, 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 (Schlumberger Limited, 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 may 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) may 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 (Schlumberger Limited, 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 may 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 may 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 may 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 may be accessed and restored using the model simulation layer 180, which may 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 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.).

Lost Circulation Event—Curability Assessment

Embodiments of the present disclosure include systems and methods for assisting a well engineer to decide whether a lost circulation event occurring while drilling is curable, or whether it is not curable, and the borehole should be plugged and a side-track planned. This may reduce cost and carbon footprint of drilling operations by avoiding the utilization of costly lost circulation control materials.

There are different mechanisms that drive lost circulation events. Some of those mechanisms may be cured with lost circulation materials (LCMs), while some other mechanisms are uncurable. Embodiments of the present disclosure may provide a workflow to detect from dynamic drilling parameters what is the likely mechanism at play, and whether it is uncurable.

Sharp mechanical specific energy (MSE) drops in vertical wellbore sections may be associated with weak or cavernous formations that likely would exhibit total mud losses. Many wells that suffered total losses that cannot be cured with LCMs had a corresponding-in-time sharp MSE drop. These total losses were cured by other techniques, including cement plugs and side-tracks. Thus, when encountering sharp MSE drops concurrently with total losses, cement plugs are generally used, rather than pumping LCM to reduce lost time and resources.

Conversely, wells that suffered total or partial losses with corresponding stable MSE in the stand drilled after the total losses began proved to be curable with LCM. Stable MSE indicates that the formation is homogenous, and the corresponding mud loss mechanism is likely matrix or induced fracture losses. As matrix and induced fractures are known to be curable with LCM, LCM treatments may be used when encountering total mud losses with corresponding stable MSE. Those results confirmed that analyzing the MSE may be employed to characterize what is the likely failure mechanism, and to determine one or more appropriate corrective actions.

Such an MSE interpretation method may save time and money spent in attempting to cure the uncurable. This value increases when the amount of chemicals and LCM pumped before deciding to pump cement is included. This recommended practice thus reduces wasted time and resources.

FIG. 2 illustrates a schematic view of a wellbore 200 extending through several different types of formations (also referred to as subsurface intervals) 210A-210E. FIG. 2 also indicates potential failure mechanisms. FIG. 3 illustrates a table of failure mechanisms as they relate to observed MSE behavior in a well, according to an embodiment. Non-limiting, corrective actions are also shown in the column on the right. Large fracture and dissolution cavities may have different mechanical properties, and drilling through those features may use less energy than drilling through a competent rock formation. They are also discrete events within a geological formation, and therefore may have outlier mechanical properties within a formation.

FIG. 4 illustrates plots of time series surface drilling values 400 (e.g., STOR, ROP, sWOB, MSE, RIG_STATE, BPOS) in a well, prior to total losses event, and in the stand drilled to expose the formation when encountering total losses, according to an embodiment. The MSE method is an evaluation of the MSE time-series trend just before and after a total lost circulation (LC) event is observed. In its current form, it applies to vertical well sections, as a method has not yet been developed to isolate formation-driven MSE changes for horizontal wellbore sections. The MSE calculation occurs in applications that may be used to monitor the MSE time series.

An MSE method for characterizing total LC events may include displaying the MSE time series. In an example, no less than 60 minutes of MSE may be displayed to visualize the recent trends. The method then monitors mud returns. If the mud returns drop or stop (e.g., total mud losses), the MSE time series may be monitored while drilling an additional stand of pipe to expose the LC zone. Stable MSE during the drilling of the stand indicates that the formation is homogeneous and that the losses mechanism is likely matrix or induced fractures. The lost circulation may thus be treated with LCM. Continuation of MSE sharp drops indicates that the cavernous formation is expansive and may involve the use of a cement plug or side track to restore circulation.

FIG. 4 is an example of sudden sharp MSE drops, according to an embodiment. Sudden sharp MSE drops may be seen in the time period between 1:17 am and 1:24 am. If mud returns continue normally, a sudden sharp MSE drop that did not correspond to a total LC event may be reported. The flowchart of FIG. 6, discussed below, may be employed to determine LC treatment course of action. Further, FIG. 4 shows that prior to encountering total losses, the formation is competent, and there are no visible precursors highlighting that the event is about to occur.

FIG. 5 illustrates plots of time series (STOR, ROP, SWOB, MSE, TD) in a well, in the stand drilled to expose the formation when encountering total losses. The large cavities are detectable from the MSE proxy that is computed. Between 1:17 AM and 1:24 AM, the hole depth has increased by two meters, with low to no energy needed to drill it. This MSE behavior is consistent with drilling through a cave. Mud losses into a cave cannot be cured using LCM. Rather, cement or side track may be employed. The following 3 meters were drilled with a stable MSE, consistent with drilling a competent formation.

FIG. 6 illustrates a flowchart of a method 600, according to an embodiment. The block “Observe Drilling breaks (MSE)” represents an automated aspect, using the following approach, by looking at dynamic drilling parameters measured at the surface (e.g., the drilling rig): rate of penetration, weight on bit, hook load, block position, torque, rotations per minute, stand pipe pressure, hole depth, and bit depth.

Detecting drilling breaks may be executed according to the following workflow, or others. It is noted that the specific values are examples only and wide variation may be permitted. The method may begin by determining an MSE proxy while on bottom drilling. The method may also include filtering out a predetermined time (e.g., 2 minutes) prior to going off bottom and a predetermined amount of time (e.g., 2 minutes) after going on bottom. The method may also include filtering out high frequency MSE variations. This may include a rolling quantile 25 over a 60 second window. The method may also include determining MSE statistics per formation. The method may also include identifying where the MSE drops by more than a predetermined amount (e.g., 50%) of a quantile (e.g., 05) of the MSE distribution for the geological formation. The method may also include depth gating the events from time domain to depth domain. The method may also include applying a low-pass filter in depth domain to preserve particular low MSE events. The method may also include displaying the low MSE events. The method may also include performing a wellsite action in response to the MSE events.

Embodiments of the present disclosure may accurately detect large cavities and fractures, which may lead to lost circulation events being uncurable. In FIG. 7A, two Karsts are visible on FMI16 and HWYH_1170_0. The drop in WOB and MSE involved to drill through the karsts may be seen. In FIG. 7B, calipers in total losses zones in a well are plotted, showing large cavities.

There are other factors that influence changes in MSE. Individuals applying the MSE method for LC characterization may question and assess the extent to which additional factors, such as the following, influence MSE time series trends in the reservoirs they are drilling. One factor may be or include bit wear. As the drilling system is less efficient in converting energy intro drilled volume, the MSE may increase as the bit is wearing. Another factor may be or include motor wear or motor stalls. Similarly, the mud flow in may be converted less efficiently into downhole RPM. Therefore, the (e.g., surface) MSE may be higher or exhibit jumps. Another factor may be or include the formation. For example, going from a stiff formation, to a soft(er) formation, the MSE may decrease. Over time, the MSE time series trend characteristics may be more thoroughly characterized and additional interpretations may be added to what is included in this best practice document.

FIG. 8 illustrates an MSE distribution that was collected drilling through a formation in a well, according to an embodiment. It may be seen that formation 8 has a substantially lower MSE median value than the formations 1, 4, 7, and 9. Thus, drilling across the transition between formations 1, 4, 7, and 9 and formation 8 may thus result in a MSE drop, but it may be a result of the formation transition and not a cavern. Time and experience studying a case such as this will allow MSE method users to distinguish a cavern related MSE drop from a formation transition related MSE drop. Using the absolute values for MSE to compare multiple sections together may not be useful, as the MSE values may be dependent upon the BHA configuration.

FIG. 9 shows plots of a well log and an identification of lost circulation events at depth intervals, according to an embodiment.

Real-Time Evaluation of the Lost Circulation Zone Based on a Changepoint Analysis of the Mechanical Specific Energy and Standpipe Pressure

There are multiple mechanisms that may drive a lost circulation event while drilling a well. Efficiently dealing with those events involves the operations team quickly getting an understanding of the subsurface conditions that caused the event. Globally, some lost circulation events are cured by basic lost circulation materials (LCM)/bridging, while others fail to be cured even after days of attempts with lost circulation cement plugs (LCP) and novel materials.

The aperture of the lost circulation zone (LCZ) remains unknown in most cases unless open-hole logs are run to identify it. The conventional approach to cure the losses is to start with less aggressive materials followed by more aggressive ones, and the success ratio to cure is based on the field practices, rather than being linked to the potential opening of LCZs.

The present disclosure determines that/whether lost circulation events related to the penetration of large, connected, open cavities (e.g., karsts) may be characterized, and those large open cavities may be identified in near-real time by analyzing the dynamic drilling parameters with the help of machine learning.

There are different mechanisms that are driving the lost circulation events. Within the same formation, some lost circulation zones may be easily cured with LCM/LCP, while others involve more effort and time and may be uncurable. The present disclosure proposes a workflow and an algorithm to detect from dynamic drilling parameters what is the likely mechanism at play, and whether the lost zone is curable. Large fracture and dissolution cavities may have different mechanical properties, and drilling through those features may involve less energy than through a competent rock formation. They are also discrete events within a geological formation and therefore may have outlier mechanical properties within a formation.

The test was performed in over 300 wells, across different lost circulation zones (some wells had several zones). The developed algorithm was incorporated into the software in a real-time monitoring center, allowing near real-time estimation of the aperture to decide regarding the LCM or LCP to be used. The results showed that, in upper sections, the multiple lost circulations zones presented with different thicknesses. The majority of the identified karsts are within a range of 2 to 8 ft, with some going over 10 ft. In the deeper formations, LCZ with an aperture of 4 to 9 ft were identified. An interesting part was related to the difference between mechanical specific energy (MSE) while drilling competent formation and the lost circulation zone when MSE values were dropping almost to zero. The present disclosure provides an approach for using machine learning to identify the aperture of the lost circulation based on the real-time parameters.

Lost Circulation Zones

There are multiple formations prone to lost circulation. The conventional approach for the LCZs is to use the decision tree, when less aggressive materials are tried first, and then followed by more aggressive ones if no success with curing lost circulation is achieved. Still, the quantification of the loss zone in every well shall be the first step done prior to any curing attempt, merged with the selection of LCM/cement/other materials based on the identified aperture.

The fracture size of some formations is within 2 ft, with a small number between 2 to 4 ft. The opening of the loss circulation zone across some formations may be or include a series of holes with a diameter of 5 to 7 cm. Due to the dissolution of pre-existing fractures, the karst aperture in the vertical wells may vary from 2 to 30 mm, and in the horizontal wells (drilling is done along the karst) from 200 to 5000 mm. The cavity high in the carbonate formation may be in the range of 0.10 m to 0.20 m.

It may be concluded that the opening of the lost circulation zone varies from one formation to another, and within the same formation, the aperture may be different. The majority of that information may be obtained based on the open hole logs. If the decision on how to cure the losses and even to cure them or not to cure them is taken on a well-by-well basis, then running an open hole log in every well is time-consuming and has a financial impact. Therefore, there is a need to develop a way to evaluate the aperture of the lost circulation zone as soon as it has been penetrated. Further decisions to cure LCZ shall be based on it.

Method

In order to characterize the subsurface interval where the drilling fluid is being lost, it is proposed to automate the human interpretation that is today made by a drilling engineer. Today, a drilling engineer characterizes the subsurface where the fluid is being lost by analyzing the weight on bit (WOB)/hook load (HKLD), surface torque (STOR) and standpipe pressure (SPPA) measurements that are being made on surface. The human interpretation assumes that when the HKLD increases/WOB decreases while the STOR is dropping and the SPPA is decreasing, the drilling bit is within an open cavity (e.g., a karst, or large open fracture) that is conductive to fluids. While those criteria are enough for a human analysis, replicating that analysis through a purely computing-driven approach may use a more advanced method.

• • 1. HKLD may increase if, while drilling, the bit is picked up or drilling continues with less aggressive parameters.
  • 2. Frictions on the drilling tool string in the case of deviated boreholes may decrease the signal-to-noise ratio when it comes to detecting large underground cavities.

The method may include:

• • 1. Computing the estimated downhole weight on bit and downhole Torque to remove the friction effects on the WOB and STOR measurements.
  • 2. Computing the mechanical specific energy (MSE) to have an estimate of the mechanical energy that will be used to drill through a unit of rock. This may remove the effect of the operational drilling engineer deciding to drill aggressively.
  • 3. Utilizing a changepoint detection approach to identify when the MSE changes by more than a predetermined amount in time.
  • 4. Forecasting the expected MSE from the prior recorded MSE values, and detecting negative anomalies, in order to identify the drilling breaks.
  • 5. Forecasting the expected SPPA from the prior recorded SPPA values, and detecting negative anomalies, in order to identify the drilling breaks that are conductive to drilling fluids.
  • 6. Filtering-out drilling breaks detected during downlinks and just before/after connection, as those drilling practices will generate false positives.

FIG. 10 illustrates a flowchart of a conductive drilling break algorithm diagram, according to an embodiment. The estimation of downhole torque, and downhole weight on bit allows removing the friction between the drilling tool string and the borehole. Those downhole estimations may be utilized to compute the mechanical specific energy, using the below equation:

$\begin{array}{cc}MSE=0.35\left(\frac{WOB}{{\pi \left(\frac{BS}{2}\right)}^2}+\frac{120\pi RPM\times TOR}{ROP\times {\pi \left(\frac{BS}{2}\right)}^2}\right)& \left(1\right)\end{array}$

• • With:
  • TOR: Downhole Torque, ft-lbs
  • RPM: Rotation per minute
  • ROP: Rate of Penetration, ft/hr.
  • WOB: Downhole Weight on Bit, lbs.
  • MSE: Mechanic Specific Energy, psi.
  • BS: Bit Size, in.

The MSE time series may then be analyzed using the PELT (Pruned Exact Linear Time) algorithm to identify changepoints. One goal of PELT is to identify points in a sequence where the statistical properties of the data change, known as change points. Identifying these change points helps in understanding the underlying dynamics of the data and in modelling different segments between the change points.

The algorithm initializes the segment, and then computes a cost function for each data point of the time series:

$\begin{array}{cc}C\left(\tau \right)=\sum _{i=1}^{m+1}c\left({\tau }_{i-1}+1,{\tau }_i\right)+\beta m& \left(2\right)\end{array}$

• • With:
  • C(τ) is the total cost for a particular segmentation τ.
  • τ={τ₀, τ₁, . . . , τ_m} represents the set of change points in the sequence, with τ₀=0 and τ_m=n (the length of the sequence).
  • c (τ_i-1+1, τ_i) represents the cost of the segment between changepoints i and i−1.
  • m is the number of detected changepoints.
  • β is a penalty term for each changepoint.
  • A pruning step prunes the list of candidates' change points by removing those that will not minimize the cost function in the future.

The algorithm seeks to find the segmentation τ that minimizes the total cost C(τ), balancing the fit to the data and the number of detected change points through the penalty term B. The pruning step in PELT efficiently eliminates non-minimizing segmentations, ensuring that the algorithm operates in linear time. Ensuring the algorithm operates in linear time is critical as the algorithm needs to be used in real time.

Finally, the characteristics of each segment (e.g., time interval between two consecutive changepoints) may be computed-thickness, defined as start measured depth of the well (DMEA) minus end DMEA, and median MSE, WOB, FLWI, SPPA—and classified as:

• • 1. Competent Formation (where forecasted MSE and SPPA values match the actual MSE and SPPA values).
  • 2. Drilling Break-Non-Conductive to Fluid (where forecasted MSE is significantly higher than the actual MSE values).
  • 3. Drilling Break-Conductive to Fluid (where forecasted MSE is significantly higher than the actual MSE values, and forecasted SPPA values are significantly higher than the actual SPPA values).

In order to validate whether the approach is correct, the algorithm is validated against the image logs and daily drilling reports of wells where lost circulation events occurred.

Testing

The method has been tested on wells where:

• • 1. High Frequency Drilling Parameters are available.
  • 2. Validation data is available:
    • a. Daily Drilling Reports.
    • b. And/Or Borehole Image logs.

FIG. 11 illustrates drilling parameters for Well A, according to an embodiment. The tracks are now described from top to bottom.

• • Track 1 (Top): Daily Drilling Report Comments.
  • Track 2: Trip Tank Volume (TTV1), Flow Rate In (FLWI), Standpipe Pressure (SPPA), Total Active Volume (TVA).
  • Track 3: Rotations Per Minute (RPM), Rate of Penetration (ROP), Surface Torque (STOR), Block Position (BPS).
  • Track 4: Hook Load (HKLD), Weight On Bit (WOB).
  • Track 5: Rig State.
  • Track 6: Drilling State.
  • Track 7: On Bottom Mechanical Specific Energy (MSE_OB), Hole Depth (DMEA), Bit Depth (DBTM).

No lost circulation events are reported on the time interval presented in FIG. 11. The drilling parameters on Well A illustrate the variety of nominal drilling parameter signatures that are expected, and that should not be flagged by the algorithm as drilling breaks, or conductive drilling breaks.

FIG. 12 illustrates additional drilling parameters for Well A, according to an embodiment. More particularly, FIG. 12 shows a signature of a nominal drilling stand. Between 7:45 AM and 8:45 AM, the drilling engineer used constant WOB, RPM and got a resulting constant STOR and ROP, yielding a constant MSE, indicative of homogeneous subsurface properties. The Tracks definitions are the same as in FIG. 11.

FIG. 13 illustrates additional drilling parameters for Well A, according to an embodiment. More particularly, FIG. 13 shows a signature of a downlink/change in drilling parameters. Between 8:55 AM and 9:00 AM, the drilling team reduced the WOB, leading to a decrease in STOR. Simultaneously, a downlink has been performed, causing a temporary drop in SPPA. Tracks definitions are the same as in FIG. 11.

Well A, as represented in FIG. 13 highlights the importance of removing downlinks from the analysis in order to avoid the algorithm falsely flagging that time interval as being a formation zone where fluid is being lost. It also highlights the importance of using the mechanical specific energy.

FIG. 14 illustrates additional drilling parameters for Well A, according to an embodiment. More particularly, FIG. 14 shows a signature of a lost circulation event. At 06:23 AM, SPPA is dropping by 600 psi. Once the drilling operations resume at 07:14 AM, the drilling bit may move down with no WOB, and no increase in STOR beyond its value when the drilling bit is not on bottom. This is indicative that the formation is open. The WOB and STOR increase at 07:21 AM, drop at 07:27 AM to recover at 07:30 AM, indicative of a second open interval.

Well A, as represented in FIG. 14 highlights the importance of looking at estimated downhole weight on bit and downhole torque, as the borehole frictions on the drilling string are causing the surface torque to be high, even when the bit is not on bottom.

FIG. 15 illustrates drilling parameters and estimated downhole weight on bit and downhole torque for Well A, according to an embodiment. The tracks are described from top to bottom.

• • Track 1 (Top): Daily Drilling Report Comments.
  • Track 2: Trip Tank Volume (TTV1), Flow Rate In (FLWI), Standpipe Pressure (SPPA), Total Active Volume (TVA).
  • Track 3: Rotations Per Minute (RPM), Rate of Penetration (ROP), Surface Torque (STOR), Block Position (BPS).
  • Track 4: Downhole Weight on Bit (DWOB_RC) and Downhole Torque (DTOR_RC).
  • Track 5: Hook Load (HKLD), Weight On Bit (WOB).
  • Track 6: Rig State.
  • Track 7: Drilling State.
  • Track 8: On Bottom Mechanical Specific Energy (MSE_OB), Hole Depth (DMEA), Bit Depth (DBTM).

The PELT algorithm may then be applied in order to identify the time segments where the energy to drill through a unit of rock (MSE) is homogeneous.

FIG. 16 illustrates drilling parameters and time segments where the MSE is homogeneous on Well A, according to an embodiment. The tracks are described from top to bottom.

• • Track 1 (Top): Daily Drilling Report Comments.
  • Track 2: Trip Tank Volume (TTV1), Flow Rate In (FLWI), Standpipe Pressure (SPPA), Total Active Volume (TVA).
  • Track 3: Rotations Per Minute (RPM), Rate of Penetration (ROP), Surface Torque (STOR), Block Position (BPS).
  • Track 4: Segment Sequential Number.
  • Track 5: Hook Load (HKLD), Weight On Bit (WOB).
  • Track 6: Rig State.
  • Track 7: Drilling State.
  • Track 8: On Bottom Mechanical Specific Energy (MSE_OB), Hole Depth (DMEA), Bit Depth (DBTM).

As pictured in FIG. 16, the algorithm delineates time intervals where the energy to drill through the formation on Well A is near 0 (Segment 5555), a pickup in energy (Segment 5556), followed by a drop (Segment 5557) and partial recovery (Segment 5558). From segment 5559 onwards, the MSE has recovered and is constant again. It is notable that the algorithm correctly does not create a segment within Segment 5558 when the drilling engineer is picking up the bit (07:38 AM).

The last part of the algorithm includes analyzing each segment, and identifying which segments correspond to consistent formation, drilling break (e.g., non-conductive to fluid), and drilling breaks (e.g., conductive to fluids). The latter is the root cause of the lost circulation events.

FIG. 17 illustrates drilling parameters and segments characteristics for Well A, according to an embodiment. The tracks are described from top to bottom.

• • Track 1: Trip Tank Volume (TTV1), Flow Rate In (FLWI), Standpipe Pressure (SPPA), Total Active Volume (TVA).
  • Track 2: Rotations Per Minute (RPM), Rate of Penetration (ROP), Surface Torque (STOR), Block Position (BPS).
  • Track 4: Segment thickness (thickness).
  • Track 5: Rig State.
  • Track 6: Drilling State.
  • Track 8: On Bottom Mechanical Specific Energy (MSE_OB), Hole Depth (DMEA), Bit Depth (DBTM), Non Conductive Drilling Break Flag (DrillingBreakFlag), Conductive Drilling Break Flag (KarstFlag).
  • Track 9: On Bottom Mechanical Specific Energy (MSE_OB), Percentile 25 of MSE_OB on the segment (MSE_q25), Minimum MSE_OB value on the segment (MSE_min) and the expected MSE_OB value on the segment (MSEBaseline).
  • Track 10: Standpipe Pressure (SPPA), Flow Rate In (FLWI), Percentile 25 of SPPA on the segment (SPP_q25), Minimum value of SPPA on the segment (SPP_min) and expected SPPA value on the segment (SPPBaseline).

On the drilling stand of Well A presented in FIG. 17, minor fluctuations of MSE and SPPA are shown, however, no drastic changes are visible, and the algorithm has no segments (false positives).

FIG. 18 illustrates drilling parameters and segments characteristics for well A during a lost circulation event, according to an embodiment. The tracks definitions are the same as in FIG. 17. Here, the algorithm has flagged the 3 segments (5555, 5556, and 5557) as being conductive drilling breaks, with a cumulative thickness of 12 ft.

FIG. 19 illustrates drilling parameters, drilling breaks, and borehole shapes from wireline calipers for Well A, according to an embodiment. The tracks definitions are the same as FIG. 17. When analyzed together with wireline calipers, the two drilling breaks (07:00 AM and 9:45 AM) are also visible at corresponding depths. The caliper thickness of the first drilling break is 7.8 ft, while the thickness from the drilling data is 12 ft. The second drilling break is not flagged as being conductive to fluid, as it is not associated with a further pressure drop. The algorithm may be validated so that it does not generate too many false positives (e.g., flagging intervals as drilling breaks that should not be flagged).

FIG. 20 illustrates drilling parameters and drilling breaks, which provides an overview of Well A, according to an embodiment. The tracks definitions are the same as FIG. 17. The overview of Well A confirms that the number of false positives is very low. The intervals marked as “Drilling Break” correspond to drilling out the cement of the previous section (e.g., unconsolidated), and the lost circulation interval.

A qualitative assessment of the results on 10 test wells resulted in the following:

• • True Positive: The likely lost circulation localization was correctly identified by the algorithm.
  • False Negative: The likely lost circulation localization was incorrectly identified as a competent drilling segment.
  • True Negative: The likely competent drilling segments was correctly identified.
  • False Positive: At least one segment was erroneously identified as conductive drilling break.

TABLE 1
Summary of True and False
Well	True Positive	False Negative	True Negative	False Positive
WellA	Y	N	Y	N
WellB	Y	N	Y	Y
WellC	Y	N	Y	N
WellD	Y	N	Y	N
WellE	N	Y	Y	Y
WellF	Y	N	Y	Y
WellG	Y	N	Y	N
WellH	Y	N	Y	N
WellI	Y	N	Y	N
WellJ	N	Y	Y	Y

The case study and the validation on 10 wells confirm that the algorithm may be utilized to identify where the lost circulation events are occurring. However, having observed at least one false positive (e.g., segments erroneously identified as a drilling break conductive) on 4 of the 10 test wells indicates that the algorithm is best utilized at the time of a confirmed lost circulation event, in order to identify precisely where the drilling fluid is being lost in the formation. That information may be utilized by the well construction company to identify the thickness of the LCZ and select the materials to cure losses.

FIG. 21 illustrates a flowchart of a method 2100 for a flowchart of a method for characterizing a subsurface interval 210A-210E where drilling fluid is being lost, according to an embodiment. An illustrative order of the method 2100 is provided below; however, one or more portions of the method 2100 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 2100 may be performed by the computing system 2200 (described below).

The method 2100 may include receiving surface drilling values 400 (FIG. 4), as at 2105. The surface drilling values 400 may be captured while drilling a wellbore 200 through the subsurface interval 210A-210E. The surface drilling values 400 may include a plurality of surface weight on bit (SWOB) values 410, a plurality of surface torque (STOR) values 420, a plurality of standpipe pressure (SPPA) values 430, or a combination thereof.

The method 2100 may also include determining a plurality of downhole drilling values, as at 2110. The downhole drilling values may be based upon the surface drilling values 400. The downhole drilling values may include a plurality of downhole WOB values and/or a plurality of downhole torque values. The downhole WOB values and/or the downhole torque values may be based upon the SWOB values 410 and/or the STOR values 420.

The method 2100 may also include determining a plurality of mechanical specific energy (MSE) values 500 (FIG. 5), as at 2115. The MSE values 500 may be over time. The MSE values 500 may be based upon the surface drilling values and/or the downhole drilling values. More particularly, the MSE values 500 may be based upon the downhole WOB values and/or the downhole torque values. The MSE values 500 may provide an estimate of an amount of energy to drill through a unit of rock in the subsurface interval 210A-210E.

The method 2100 may also include identifying a plurality of on-bottom surface drilling values, as at 2120. The on-bottom surface drilling values may be based upon the surface drilling values, the downhole drilling values, and/or the MSE values. The on-bottom surface drilling values may include a subset of the surface drilling values 400 (e.g., a subset of the SPPA values 430) that occurs while a drill bit 220 (FIG. 2) is on-bottom in the wellbore 200.

The method 2100 may also include identifying a plurality of on-bottom MSE values 1500, 1600, 1700 (FIGS. 15-17), as at 2125. The on-bottom MSE values 1500, 1600, 1700 may be based upon the surface drilling values, the downhole drilling values, and/or the MSE values 500. The on-bottom MSE values 1500, 1600, 1700 may include a subset of the MSE values 500 that occur while the drill bit 220 is on-bottom in the wellbore 200.

The method 2100 may also include identifying one or more first changepoints in the on-bottom surface drilling values that are greater than a predetermined drilling data threshold, as at 2130. The first changepoints may be identified in on-bottom SPPA measurements that include a subset of the SPPA values 430 that occurs while the drill bit 220 is on-bottom in the wellbore 200. In another embodiment, the threshold may be determined based on particular factors (e.g., type of drill bit). In yet another embodiment, the threshold may be dynamic in nature based upon real-time measurements.

The method 2100 may also include identifying one or more second changepoints in the on-bottom MSE values 1500, 1600, 1700 that are greater than a predetermined MSE threshold, as at 2135. In another embodiment, the threshold may be determined based on particular factors (e.g., type of drill bit). In yet another embodiment, the threshold may be dynamic in nature based upon real-time measurements.

The method 2100 may also include forecasting a plurality of forecasted standpipe pressure (SPPA) values, as at 2140. The forecasted SPPA values may be based upon the SPPA values. Each of the forecasted SPPA values occurs at a same time as one of the on-bottom SPPA values.

The method 2100 may also include identifying the on-bottom SPPA values that are lower than their corresponding forecasted SPPA values, as at 2145. This may represent first negative anomalies that indicate first drilling breaks that are conductive to the drilling fluid.

The method 2100 may also include forecasting (e.g., predicting) a plurality of forecasted MSE values, as at 2150. The forecasted MSE values may be forecasted based upon the on-bottom MSE values 1500, 1600, 1700. Each of the forecasted MSE values occurs at a same time as one of the on-bottom MSE values 1500, 1600, 1700.

The method 2100 may also include identifying the on-bottom MSE values 1500, 1600, 1700 that are lower than their corresponding forecasted MSE values, as at 2155. This may represent second negative anomalies that indicate second drilling breaks.

The method 2100 may also include identifying a first portion of the subsurface interval 210A-210E, as at 2160. The first portion of the subsurface interval 210A-210E may be based upon the one or more first changepoints, the one or more second changepoints, the on-bottom MSE values 1500, 1600, 1700 that are lower than their corresponding forecasted MSE values, the on-bottom SPPA values that are lower than their corresponding forecasted SPPA values, or a combination thereof. The first portion of the subsurface interval 210A-210E may correspond to a first portion of rock with mechanical properties below a predetermined mechanical property threshold. The first portion of the subsurface interval 210A-210E may also or instead correspond to the rock that is non-conductive to the drilling fluid.

The method 2100 may also include identifying a second portion of the subsurface interval 210A-210E, as at 2165. The second portion of the subsurface interval 210A-210E may be based upon the one or more first changepoints, the one or more second changepoints, the on-bottom MSE values 1500, 1600, 1700 that are lower than their corresponding forecasted MSE values, the on-bottom SPPA values that are lower than their corresponding forecasted SPPA values, or a combination thereof. The second portion of the subsurface interval 210A-210E may correspond to a second portion of the rock with mechanical properties above the predetermined mechanical property threshold. The second portion of the subsurface interval 210A-210E may also or instead correspond to the rock that is conductive to the drilling fluid.

The method 2100 may also include determining a cause of a lost circulation event in the subsurface interval 210A-210E, as at 2170. This may also or instead include identifying a location of the lost circulation event in the wellbore 200 and/or the subsurface interval 210A-210E. The cause and/or location may be determined based upon the identified first portion of the subsurface interval 210A-210E and/or the identified second portion of the subsurface interval 210A-210E.

The method 2100 may also include determining whether the cause is curable, as at 2175.

The method 2100 may also include displaying one or more outputs, as at 2180. The outputs may include the wellbore 200, the subsurface interval 210A-210E, the first portion of the subsurface interval 210A-210E, the second portion of the subsurface interval 210A-210E, the (e.g., location of the) lost circulation event, the cause of the lost circulation event, the curability of the lost circulation event, or a combination thereof.

The method 2100 may also include performing a wellsite action, as at 2185. The wellsite action may be based upon or in response to the first portion of the subsurface interval 210A-210E, the second portion of the subsurface interval 210A-210E, the lost circulation event, the cause of the lost circulation event, the curability of the lost circulation event, or a combination thereof. The wellsite action may be or include generating and/or transmitting a signal (e.g., using a computing system) that instructs or causes a physical action to occur at a wellsite. The wellsite action may also or instead include curing the cause. For example, the wellsite action may include performing a lost circulation mitigation action in the wellbore 200 in response to determining that the cause is curable.

In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 22 illustrates an example of such a computing system 2200, in accordance with some embodiments. The computing system 2200 may include a computer or computer system 2201A, which may be an individual computer system 2201A or an arrangement of distributed computer systems. The computer system 2201A includes one or more analysis modules 2202 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 2202 executes independently, or in coordination with, one or more processors 2204, which is (or are) connected to one or more storage media 2206. The processor(s) 2204 is (or are) also connected to a network interface 2207 to allow the computer system 2201A to communicate over a data network 2209 with one or more additional computer systems and/or computing systems, such as 2201B, 2201C, and/or 2201D (note that computer systems 2201B, 2201C and/or 2201D may or may not share the same architecture as computer system 2201A, and may be located in different physical locations, e.g., computer systems 2201A and 2201B may be located in a processing facility, while in communication with one or more computer systems such as 2201C and/or 2201D 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 2206 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of FIG. 22 storage media 2206 is depicted as within computer system 2201A, in some embodiments, storage media 2206 may be distributed within and/or across multiple internal and/or external enclosures of computing system 2201A and/or additional computing systems. Storage media 2206 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 2200 contains one or more lost circulation event module(s) 2208. In the example of computing system 2200, computer system 2201A includes the lost circulation event module 2208. In some embodiments, a single lost circulation event module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of lost circulation event modules may be used to perform some aspects of methods herein.

It should be appreciated that computing system 2200 is merely one example of a computing system, and that computing system 2200 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of FIG. 22, and/or computing system 2200 may have a different configuration or arrangement of the components depicted in FIG. 22. The various components shown in FIG. 22 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 2200, FIG. 22), 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.

TABLE 2
Definitions
Term              Definition
MSE               Mechanical Specific Energy; It's a measure of drilling efficiency.
                  MSE is the energy required to remove a unit volume of rock.
Lost Circulation  It is the loss of drilling or completion fluids resulting in the inability
                  to maintain the fluid level at surface level through normal procedures.
LCM               Lost Circulation Materials
DDI               Daily Driller Instruction
WSL               Well Site Leader
DS                Drilling Superintendent
Hi-Po event       High Potential event
LL                Lesson Learned
NPT               Non-productive time
OI                Operations Integrity
OIC               Operations Integrity Center
WE                Well Engineer
SWE               Senior Well Engineer
WEM               Well Engineering Manager
Failure mode      The way in which a failure mechanism manifests itself in a failing
                  activity.
                  Example: Lost circulation event
Failure mechanism The physical, chemical, subsurface, or other process defect that has
                  led to a non-conformance.
                  Example: Drilling induced fractures
Corrective action Actions taken to eliminate the root cause(s) of an existing non-
                  conformity

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 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 characterizing a subsurface interval where drilling fluid is being lost, the method comprising: receiving surface drilling values captured while drilling a wellbore through the subsurface interval;analyzing the surface drilling values and identifying on-bottom surface drilling values and on-bottom mechanical specific energy (MSE) values;identifying a first changepoint in the on-bottom surface drilling values;identifying a second changepoint in the on-bottom MSE values; anddetermining a cause of a lost circulation event in the subsurface interval based upon the first and second changepoints.

2. The method of claim 1, wherein the surface drilling values comprise a plurality of surface weight on bit (SWOB) values, a plurality of surface torque (STOR) values, and a plurality of standpipe pressure (SPPA) values, wherein downhole drilling values are determined based upon the surface drilling values, and wherein the downhole drilling values comprise a plurality of downhole WOB values and a plurality of downhole torque values that are based upon the SWOB values and the STOR values.

3. The method of claim 2, wherein MSE values are determined based upon the downhole drilling values, and wherein the MSE values provide an estimate of an amount of energy to drill through a unit of rock in the subsurface interval.

4. The method of claim 1, wherein the on-bottom surface drilling values comprise a subset of the surface drilling values that occurs while a drill bit is on-bottom in the wellbore.

5. The method of claim 4, wherein the on-bottom MSE values comprise a subset of the MSE values that occur while the drill bit is on-bottom in the wellbore.

6. The method of claim 1, wherein the first changepoint is greater than a first threshold, and wherein the second changepoint is greater than a second threshold.

7. The method of claim 1, wherein the first changepoint is identified in on-bottom standpipe pressure (SPPA) measurements that comprise a subset of the surface drilling values that occur while a drill bit is on-bottom in the wellbore.

8. The method of claim 1, further comprising: identifying a portion of the subsurface interval based upon the first changepoint and the second changepoint; anddisplaying the portion of the subsurface interval, the cause of the lost circulation event, or both.

9. The method of claim 8, further comprising determining whether the cause is curable.

10. The method of claim 9, further comprising performing a lost circulation mitigation action in the wellbore adjacent to the portion of the subsurface interval in response to determining that the cause is curable.

11. A computing system, comprising: one or more processors; anda 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 surface drilling values captured while drilling a wellbore through a subsurface interval;analyzing the surface drilling values and identifying on-bottom surface drilling values and on-bottom mechanical specific energy (MSE) values;identifying a first changepoint in the on-bottom surface drilling values;identifying a second changepoint in the on-bottom MSE values;identifying a first portion of a subsurface interval based upon the first changepoint and the second changepoint; anddetermining a cause of a lost circulation event in the wellbore based upon the first portion of the subsurface interval.

12. The computing system of claim 11, wherein the surface drilling values comprise a plurality of surface weight on bit (SWOB) values, a plurality of surface torque (STOR) values, and a plurality of standpipe pressure (SPPA) values, wherein downhole drilling values are determined based upon the surface drilling values, wherein the downhole drilling values comprise a plurality of downhole WOB values and a plurality of downhole torque values that are based upon the SWOB values and the STOR values, wherein MSE values are determined based upon the downhole drilling values, and wherein the MSE values provide an estimate of an amount of energy to drill through a unit of rock in the subsurface interval.

13. The computing system of claim 11, wherein the operations further comprise: forecasting a plurality of forecasted surface drilling values based upon the surface drilling values, wherein each of the forecasted surface drilling values occurs at a same time as one of the on-bottom surface drilling values;identifying the on-bottom surface drilling values that are lower than their corresponding forecasted surface drilling values, which represent second negative anomalies that indicate second drilling breaks that are conductive to a drilling fluid;forecasting a plurality of forecasted MSE values based upon the on-bottom MSE values, wherein each of the forecasted MSE values occurs at a same time as one of the on-bottom MSE values; andidentifying the on-bottom MSE values that are lower than their corresponding forecasted MSE values, which represent first negative anomalies that indicate first drilling breaks.

14. The computing system of claim 13, wherein the first portion of the subsurface interval is also based upon the on-bottom surface drilling values that are lower than their corresponding forecasted surface drilling values, and the on-bottom MSE values that are lower than their corresponding forecasted MSE values, and wherein the first portion of the subsurface interval corresponds to a first portion of a rock with mechanical properties below a predetermined mechanical property threshold.

15. The computing system of claim 14, wherein the operations further comprise identifying a second portion of the subsurface interval based upon the first changepoint, the second changepoint, the on-bottom surface drilling values that are lower than their corresponding forecasted surface drilling values, and the on-bottom MSE values that are lower than their corresponding forecasted MSE values, wherein the second portion of the subsurface interval corresponds to a second portion of the rock that is conductive to the drilling fluid, and wherein the cause of the lost circulation event is also determined based upon the second portion of the subsurface interval.

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 surface drilling values captured while drilling a wellbore through a subsurface interval, wherein the surface drilling values comprise standpipe pressure (SPPA) values;identifying on-bottom surface drilling values based upon the surface drilling values, wherein the on-bottom surface drilling values comprise on-bottom SPPA values based upon the SPPA values, which represent a subset of the SPPA values that occurs while a drill bit is on-bottom in the wellbore;identifying on-bottom mechanical specific energy (MSE) values based upon the surface drilling values;identifying first changepoints in the on-bottom surface drilling values that are greater than a first threshold, wherein the first changepoints are identified in the on-bottom SPPA values;identifying second changepoints in the on-bottom MSE values that are greater than a second threshold; anddetermining a cause of a lost circulation event based upon the first and second changepoints.

17. The non-transitory computer-readable medium of claim 16, wherein the surface drilling values also comprise surface weight on bit (SWOB) values and surface torque (STOR) values, wherein downhole drilling values are determined based upon the surface drilling values, wherein the downhole drilling values comprise downhole WOB values and downhole torque values, wherein the downhole WOB values and the downhole torque values are based upon the SWOB values and the STOR values, wherein MSE values are determined based upon the downhole drilling values, wherein the MSE values are based upon the downhole WOB values and the downhole torque values, and wherein the MSE values provide an estimate of an amount of energy to drill through a unit of rock in the subsurface interval.

18. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise: forecasting forecasted SPPA values based upon the on-bottom SPPA values, wherein each of the forecasted SPPA values occurs at a same time as one of the on-bottom SPPA values;identifying the on-bottom SPPA values that are lower than their corresponding forecasted SPPA values, which represent first negative anomalies that indicate first drilling breaks that are conductive to drilling fluid, wherein a first portion of the subsurface interval and a second portion of the subsurface interval are also identified based upon the on-bottom SPPA values that are lower than their corresponding forecasted SPPA values;forecasting forecasted MSE values based upon the on-bottom MSE values, wherein each of the forecasted MSE values occurs at a same time as one of the on-bottom MSE values; andidentifying the on-bottom MSE values that are lower than their corresponding forecasted MSE values, which represent second negative anomalies that indicate second drilling breaks, wherein the first portion of the subsurface interval and the second portion of the subsurface interval are also identified based upon the on-bottom MSE values that are lower than their corresponding forecasted MSE values.

19. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise: identifying a first portion of the subsurface interval based upon the first changepoints and the second changepoints, wherein the first portion of the subsurface interval corresponds to a first portion of rock with mechanical properties below a predetermined mechanical property threshold;identifying a second portion of the subsurface interval based upon the first changepoints and the second changepoints, wherein the second portion of the subsurface interval corresponds to a second portion of rock that is conductive to a drilling fluid;determining the cause of a lost circulation event in the subsurface interval based upon the first and second portions of the subsurface interval; anddetermining whether the lost circulation event is curable.

20. The non-transitory computer-readable medium of claim 19, wherein the operations further comprise performing a wellsite action in response to determining that the cause is curable, wherein the wellsite action comprises generating and transmitting a signal that instructs or causes a physical action to occur, and wherein the physical action comprises a lost circulation mitigation action in the wellbore.