Systems and methods to perform a downhole inspection in real-time

Systems and methods to perform an automated downhole inspection in real-time are disclosed. A method to perform the downhole inspection includes deploying a camera and a logging tool downhole. The method also includes obtaining real-time transmissions of images from the camera. The method further includes obtaining real-time transmissions of data from the logging tool. The method further includes determining a presence of a downhole anomaly based on the real-time transmissions of images and the real-time transmissions of data.

The present disclosure relates to systems and methods to perform a downhole inspection in real-time.

Tubulars and casings have multiple oil and gas applications, such as, but not limited to, to transport fluids, to prevent cave-ins, and/or to prevent contamination of subterranean formation, convey downhole tools, as well as other applications. A tubular or casing failure can be dangerous, so tubulars and casings are periodically inspected to reduce the likelihood of pipeline or casing failure. Inspections of pipeline casings focus on the structural integrity, filler quantity, quality, and electrical isolation between pipeline and casing.

DETAILED DESCRIPTION

The present disclosure relates to systems and methods to perform an automated downhole inspection in real-time. Downhole inspections include inspections of a hydrocarbon well or a hydrocarbon water well, a wellbore of the hydrocarbon well, tubulars deployed in the hydrocarbon well, as well as casings installed in the hydrocarbon well. As referred to herein, a tubular may be coiled tubing, drill pipe, liner, production tubing, or another type of conveyance that has an inner diameter that provides a passageway for fluids and/or downhole tools to pass through. A camera and a logging tool (e.g., a wireline logging tool) are deployed in a wellbore of a hydrocarbon well. In some embodiments, computer vision with machine learning is utilized for automated pattern recognition of one or more anomalies from digital images and videos. Real-time transmissions of images from the camera and transmissions of data from the logging tool are obtained and are analyzed to determine the presence of a downhole anomaly in real-time and based on the real-time data. As referred to herein, an anomaly includes damages to and/or corrosions of a tubular or a casing that is deployed in a wellbore. In one or more of such embodiments, an anomaly along a tubular or a casing is a leak or hole in the tubular or the casing. In one or more of such embodiments, an anomaly along a tubular or a casing includes an area of the tubular or casing that has a thickness that is less than a threshold thickness or is less than the thickness of other areas of the tubular or casing by a threshold amount. In one or more of such embodiments, an anomaly along a tubular or a casing is corrosion along the tubular or casing. In some embodiments, determining the presence of the downhole anomaly is performed while the camera and the logging tool are deployed downhole. In some embodiments, an automated real-time determination of the presence of the downhole anomalies is performed through at least one of computer vision and artificial intelligence techniques based on the real-time transmissions of the images and the data to determine the presence of the downhole anomalies.

In some embodiments, computer vision with machine learning is utilized to automatically determine the presence of the downhole anomaly. In one or more of such embodiments, the downhole anomaly is compared with one or more downhole anomalies present in a similar downhole environment. In one or more of such embodiments, an improvement or an optimization to a well intervention operation is determined based on the presence of the downhole anomaly. More particularly, computer vison and deep learning are utilized to classify images of anomalies. Further, the classified images are analyzed in combination with machine learning models that are based on cased hole log data to determine precisely the anomaly type and depth. In one or more of such embodiments, an improvement or an optimization to a recompletion operation is determined based on the presence of the downhole anomaly. More particularly, the presence of the downhole anomaly and similar downhole anomalies are analyzed and compared to each other to determine an optimal recompletion operation or a recompletion operation that exceeds a set of criteria associate with the recompletion operation. In one or more of such embodiments, improved anomaly detection and interpretation for multiple wells reduce time spent to compare anomalies versus different wells completions, and reduce time spent to determine which design is vulnerable to the anomaly relative to other designs. In one or more of such embodiments, an improvement to a plug and abandon operation is determined based on the presence of the downhole anomaly. More particularly, the presence of the downhole anomaly and similar downhole anomalies are analyzed and compared to each other to determine an optimal location to set a plug or a location that satisfies a set of criteria for setting a plug, and the amount of casing or tubing that should be retrieved or reused during a plug and abandon operation. In one or more of such embodiments, an improvement of the time spent identifying the downhole anomaly results in a faster determination of where to set a permanent plug and how much the amount of casing or tubing that should be retrieved or reused during a plug and abandon operation. In some embodiments, an analysis of the downhole anomaly is performed and a determination of how to improve performance of a yet-to-be deployed tubular or casing is made based on an analysis of the downhole anomaly. Additional descriptions of the foregoing operations are provided in the paragraphs below and are illustrated in at leastFIGS.1A-4.

Now turning to the figures,FIG.1Aillustrates a schematic view of a wireline logging environment100in which a real-time downhole inspection tool124is deployed on a wireline119in a wellbore106. Similarly,FIG.1Bis a schematic view of a wireline logging environment150in which downhole inspection tool124ofFIG.1Ais deployed on wireline119in a tubular114. In the embodiments ofFIGS.1A and1B, data is transmitted via a cable of wireline119ofFIG.1A. Additional discussions of various components of real-time downhole inspection tool124are provided in the paragraphs below and are illustrated in at leastFIG.2.

In the embodiments ofFIGS.1A and1B, a well102having wellbore106extends from a surface108of the well102to or through a subterranean formation112. A casing116is deployed along wellbore106to insulate downhole tools and strings deployed in casing116, to provide a path for hydrocarbon resources flowing from subterranean formation112, to prevent cave-ins, and/or to prevent contamination of subterranean formation112. Casing116is normally surrounded by a cement sheath128, which is deposited in an annulus between the casing116and wellbore106to fixedly secure casing116to the wellbore106and to form a barrier that isolates casing116. Although not depicted, there may be layers of casing concentrically placed in wellbore106, each having a layer of cement or the like deposited thereabout.

A vehicle180carrying real-time downhole inspection system184and wireline119is positioned proximate to the well102. Wireline119, along with real-time downhole inspection tool124having a logging tool125and a camera127are lowered through the blowout preventer103and wellhead136into the well102. Data indicative of measurements obtained by logging tool125may be transmitted via wireline119or via another telemetry system to surface108for processing by real-time downhole inspection system184or by another electronic device operable to process data obtained by logging tool125. In the embodiment ofFIG.1A, data obtained by logging tool125and images obtained by camera127of real-time downhole inspection tool124are transmitted to downhole inspection system184while real-time downhole inspection tool124is traversing the interior of casing116.

Real-time real-time downhole inspection system184may include any electronic and/or optoelectronic device operable to receive data and/or process data indicative of one or more formation properties to determine the formation properties. In the embodiment ofFIG.1A, real-time downhole inspection system184is stored on vehicle180. In some embodiments, real-time downhole inspection system184may also be housed in a temporary and/or permanent facility (not shown) proximate to the well102. In other embodiments, the real-time downhole inspection system184may also be deployed at a remote location relative to the well102. Additional operations of real-time downhole inspection system184are provided in the paragraphs below.

real-timeIn the embodiments ofFIGS.1A and1B, data is transmitted via a cable of wireline119to real-time downhole inspection system184. Real-time downhole inspection system184performs operations described herein to determine a presence of a downhole anomaly based on the real-time transmissions of the images and the data. In some embodiments, real-time downhole inspection system184utilizes one or more computer vision algorithms with machine learning to analyze the received data indicative of images and logging data obtained by camera127and logging tool125.

In some embodiments, where real-time downhole inspection system184is deployed in a casing such as casing116ofFIG.1A, real-time downhole inspection system184compares the received data with historical data obtained from previous runs in casing116ofFIG.1A, or other tubulars (not shown). In some embodiments, where real-time downhole inspection system184is deployed in a tubular such as tubular114ofFIG.1B, real-time downhole inspection system184compares the received data with historical data obtained from previous runs in tubular114ofFIG.1B, or other tubulars (not shown). In one or more of such embodiments, real-time downhole inspection system184utilizes machine learning algorithms to dynamically compare data obtained from camera127and logging tool125with previous casing or tubing inspection operations. For example, real-time downhole inspection system184compares data from previous measurements of casing116or tubular114(e.g., made one month ago, one year ago, or another time) with current measurements to determine the presence of corrosions, leaks, or other types of anomalies, and whether the detected anomalies have increased or worsened over time. In one or more of such embodiments, real-time downhole inspection system184also predicts when the detected anomalies (e.g., corrosion) would cause casing116or tubular114to fail, measurements to be taken (e.g., applying a sealing material to the corroded section, replacing the corroded section, as well as other potential operations), cost of each alternative measurement, and the likelihood of success of each alternative measurement. In some embodiments, real-time downhole inspection system184analyzes data indicative of images and logging data obtained by camera127and logging tool125to determine the presence of corrosion, leaks, and/or other types of anomalies while real-time downhole inspection tool124is deployed downhole. In some embodiments, real-time downhole inspection system184compares data indicative of images from camera127with the logging data obtained from logging tool125to assess the presence of corrosion, leaks, and/or other types of anomalies and to confirm the presence of corrosion, leaks, and/or other types of anomalies).

In some embodiments, real-time downhole inspection system184includes a storage medium containing instructions to obtain real-time transmissions of data from logging tool125and images from camera127, and to determine a presence of a downhole anomaly based on the real-time transmissions of the data and images. Additional descriptions of the operations of real-time downhole inspection system184and operations performed to conduct a downhole inspection are provided in the paragraphs below and are illustrated in at leastFIGS.3and4. AlthoughFIGS.1A and1Beach illustrates a single real-time downhole inspection tool124deployed downhole, in some embodiments, multiple downhole inspection tools (not shown) are simultaneously deployed in a well to monitor different sections of casing and tubulars that are disposed in the well. In one or more of such embodiments, images and data obtained by the real-time downhole inspection tools are transmitted via one or more wirelines, such as wireline119ofFIGS.1A and1Bto real-time downhole inspection system184. Further, although the foregoing paragraphs describe transmitting images and data via wireline119, in some embodiments, images and data are transmitted acoustically, optically, wirelessly, or by other types of telemetric systems, from real-time downhole inspection tool124to real-time downhole inspection system184. In some embodiments, real-time downhole inspection system184is a downhole system. In one or more of such embodiments, real-time downhole inspection system184is an onboard component of real-time downhole inspection tool124. In other embodiments, real-time downhole inspection tool124is a component of real-time downhole inspection system184.

FIG.2illustrates a real-time downhole inspection tool124that is deployable in a wellbore of a hydrocarbon well. Real-time downhole inspection tool124has a logging tool125and a camera127. In the embodiment ofFIG.2, real-time downhole inspection tool124is suspended by wireline119having a cable121inside wireline119. In some embodiments, cable121is a hybrid cable that provides both power and data transmission to real-time downhole inspection tool124. In one or more of such embodiments, cable121includes a fiber optical cable that provides data transmission (such as images obtained by camera127and data obtained by logging tool125) to and from real-time downhole inspection tool124and an electrical cable that provides power to components of real-time downhole inspection tool124. In some embodiments, cable121and real-time downhole inspection tool124are lowered through a blowout preventer or a wellhead of a well into a wellbore of the well. In one or more of such embodiments, where existing tubulars (“pipes”) are installed in the wellbore, real-time downhole inspection tool124is lowered into the pipes to provide real-time analysis of anomalies (e.g., corrosion) inside the pipes. In one or more of such embodiments, while real-time downhole inspection tool124is lowered into the pipes, camera127continuously scans areas of the pipes near real-time downhole inspection tool124for leaks, corrosions, as well as other types of anomalies. Similarly, logging tool125continuously determines and logs data indicative of leaks, corrosions, as well as other types of anomalies near real-time downhole inspection tool124. Images and data obtained by camera127and logging tool125are continuously or periodically transmitted via cable121. In some embodiments, the images and data are transmitted to a surface-based electronic device (e.g., desktop computer, lap top computer, server system, or other types of electronic device operable to perform processing operations described herein) to perform operations described herein and illustrated in at leastFIGS.3and4. In some embodiments, the images and data are transmitted to an electronic device that is located in a downhole location. In some embodiments, real-time downhole inspection tool124includes an onboard processor that is operable to receive the images and data obtained by camera127and logging tool125and perform the operations described herein and illustrated inFIGS.3and4.

Data indicative of images and logging data obtained by camera127and logging tool125are analyzed and assessments of the presence of corrosion, leaks, and/or other types of anomalies are dynamically determined while real-time downhole inspection tool124is deployed downhole. In some embodiments, data indicative of images from camera127are compared with the logging data obtained from logging tool125to assess the presence of corrosion, leaks, and/or other types of anomalies (e.g., data indicative of the images obtained from camera127and logging data obtained from logging tool125are compared with each other to confirm the presence of corrosion, leaks, and/or other types of anomalies). In one or more of such embodiments, data indicative of images from camera127and from the logging tool125used to complement each other to improve the accuracy of real-time downhole inspection tool124(e.g., using data indicative of the images obtained from camera127to determine location and area of a leak in the pipe, and using logging data obtained from logging tool125to perform a volumetric analysis of the leak).

In some embodiments, real-time downhole inspection tool124also includes additional components (not shown) that obtain downhole measurements. In one or more of such embodiments, real-time downhole inspection tool124includes calipers, electromagnetic tools, acoustic tools, and/or other types of tools that measure the thickness of tubulars and casings that are installed in the wellbore.

FIG.3is a system diagram300of real-time downhole inspection system184ofFIGS.1A and1B. Real-time downhole inspection system184includes a storage medium306and processors310. In the embodiments ofFIG.3, processors310are onboard processors of real-time downhole inspection system184. In some embodiments, processors310are remote processors. Storage medium306may be formed from data storage components such as, but not limited to, read-only memory (ROM), random access memory (RAM), flash memory, magnetic hard drives, solid-state hard drives, CD-ROM drives, DVD drives, floppy disk drives, as well as other types of data storage components and devices. In some embodiments, storage medium306includes multiple data storage devices. In further embodiments, the multiple data storage devices may be physically stored at different locations. Data indicative of parameters and measurements of downhole anomalies are stored at a first location320of storage medium306. In some embodiments, historical data of previous runs are also stored at first location320.

As shown inFIG.3, instructions to obtain real-time transmissions of images from the camera are stored at a second location322of storage medium306. Further, instructions to obtain real-time transmissions of data from the logging tool are stored at a third location324of the storage medium306. Further, instructions to determine a presence of a downhole anomaly based on the real-time transmissions of the images and the data are stored at a fourth location326of storage medium306. Instructions to perform operations described herein are stored at other locations of storage medium306. In some embodiments, processors310and storage medium310are components of real-time downhole inspection tool124ofFIGS.1A,1B, and2.

FIG.4illustrates a process400to perform automated real-time downhole inspection. Although the operations in process400are shown in a particular sequence, certain operations may be performed in different sequences or at the same time where feasible. Further, although some of the operations are described to be performed by processors310of real-time downhole inspection system184ofFIGS.1A and1B, the operations may be performed by other processors of other electronic devices.

At block S402, a camera and a logging tool are deployed downhole. In that regard,FIG.1Aillustrates real-time downhole inspection tool124deployed in casing119of wellbore106. Similarly,FIG.1Billustrates real-time downhole inspection tool124deployed in tubular114that runs through wellbore106. At block S404, processors310obtain real-time transmissions of images from the camera. At block S406, processors310obtain real-time transmissions of data from the logging tool. In the embodiments ofFIGS.1A,1B, and2, for example, data indicative of images obtained by camera127of real-time downhole inspection tool124and logging data obtained by logging tool125of real-time downhole inspection tool124are transmitted by cable121of wireline119. In some embodiments, data is transmitted uphole via cable121and is subsequently wirelessly transmitted to real-time downhole inspection system184. In some embodiments, data is transmitted from real-time downhole inspection tool124to real-time downhole inspection system184acoustically, optically, wirelessly, or through another telemetry system.

At block S408, processors310automatically determine a presence of a downhole anomaly based on the real-time transmissions of the images and the data. In some embodiments, processors310utilize computer vision with machine learning to determine the presence of the downhole anomaly. In one or more of such embodiments, processors310compare a downhole anomaly with one or more downhole anomaly present in a similar downhole environment. For example, after processors310determine the existence of a downhole anomaly in casing119ofFIG.1A, processors310determine the presence of another downhole anomaly that is present or was present within a threshold period of time (such as within one day, one week, one month, or another period of time), and within a threshold distance (such as within one meter, 10 meters, 100 meters, or another distance) of the downhole anomaly. Processors310then compare the downhole anomaly with the pre-existing anomaly. In one or more of such embodiments, processors310determine the severity of the downhole anomaly based on the severity of the pre-existing anomaly. In one or more of such embodiments, processors310determine how to address the anomaly (such as whether to repair the anomaly, seal the tubular or casing, or another course of action to address the anomaly) based on how the pre-existing anomaly was or is being addressed.

In some embodiments, processors310determine an improvement or an optimization to a well intervention operation based on the presence of the downhole anomaly. In one or more of such embodiments, processors310determine an improvement to a well intervention operation based on data obtained from the real-time transmissions of the images and the data. In one or more of such embodiments, processors310determine an improvement or an optimization based on the presence of the downhole anomaly. In one or more of such embodiments, processors310determine an improvement to a plug an abandon operation based on the presence of the downhole anomaly. In some embodiments, processors310perform an analysis of the downhole anomaly and determine how to improve performance of a yet-to-be deployed tubular or casing is made based on an analysis of the downhole anomaly. In some embodiments, processors310analyze operations performed to repair or improve the anomaly (such as operations performed to seal a leak), and the cost of such operations (such as the cost associated with sealing a nearby valve during the process to seal the leak). In one or more of such embodiments, processors310determine one or more operations that would reduce the material cost of future operations to repair or improve similar anomalies. In some embodiments, processor310analyzes the performance of a current or previous downhole inspection operation, and determines one or more improvements to the performance of a subsequent downhole inspection operation based on the analysis of the performance of the current or previous downhole inspection operation.

The above-disclosed embodiments have been presented for purposes of illustration and to enable one of ordinary skill in the art to practice the disclosure, but the disclosure is not intended to be exhaustive or limited to the forms disclosed. Many insubstantial modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. For instance, although the flowcharts depict a serial process, some of the steps/processes may be performed in parallel or out of sequence, or combined into a single step/process. The scope of the claims is intended to broadly cover the disclosed embodiments and any such modification. Further, the following clauses represent additional embodiments of the disclosure and should be considered within the scope of the disclosure:

Clause 1, a method to perform downhole inspection in real-time, the method comprising: deploying a camera and a logging tool downhole; obtaining real-time transmissions of images from the camera; obtaining real-time transmissions of data from the logging tool; and determining a presence of a downhole anomaly based on the real-time transmissions of images and the real-time transmissions of data.

Clause 2, the method of clause 1, wherein determining the presence of the downhole anomaly comprises performing an automated real-time determination of a presence of the downhole anomaly through computer vision and artificial intelligence techniques based on the real-time transmissions of images and the real-time transmissions of data.

Clause 3, the method of clauses 1 or 2, wherein determining the presence of the downhole anomaly is performed while the camera and the logging tool are deployed downhole.

Clause 4, the method of any of clauses 1-3, further comprising utilizing artificial intelligence techniques to determine the presence of the downhole anomaly.

Clause 5, the method of clause 4, further comprising utilizing computer vision with machine learning to determine the presence of the downhole anomaly.

Clause 6, the method of clause 5, wherein utilizing computer vision with machine learning comprises comparing the downhole anomaly with another downhole anomaly present in a similar downhole environment.

Clause 7, the method of clauses 5 or 6, further comprising determining, based on the presence of the downhole anomaly, an improvement to a well intervention operation.

Clause 8, the method of clauses 5 or 6, further comprising determining, based on the presence of the downhole anomaly, an improvement to a recompletion operation.

Clause 9, the method of clauses 5 or 6, further comprising determining, based on the presence of the downhole anomaly, an improvement to a plug and abandon operation.

Clause 10, the method of any of clauses 1-9, further comprising: analyzing the downhole anomaly; and improving performance of a subsequent downhole inspection operation based on an analysis of the downhole anomaly.

Clause 11, a downhole inspection system, comprising a storage medium; and one or more processors configured to: obtain real-time transmissions of images from a camera of a logging tool; obtain real-time transmissions of data from the logging tool; and determine a presence of a downhole anomaly based on the real-time transmissions of images and the real-time transmissions of data.

Clause 12, the downhole inspection system of clause 11, wherein the one or more processors are further configured to analyze the downhole anomaly; and improve performance of a subsequent downhole inspection operation based on an analysis of the downhole anomaly.

Clause 13, the downhole inspection system of clauses 11 or 12, wherein the presence of the downhole anomaly is determined while the camera and the logging tool are deployed downhole.

Clause 14, the downhole inspection system of any of clauses 11-13, wherein the one or more processors are further configured to utilize artificial intelligence techniques to determine the presence of the downhole anomaly.

Clause 15, the downhole inspection system of any of clauses 11-14, wherein the one or more processors are further configured to utilize computer vision with machine learning to determine the presence of the downhole anomaly.

Clause 16, the downhole inspection system of clause 15, wherein the one or more processors are further configured to: utilize computer vision with machine learning to compare the downhole anomaly with another downhole anomaly present in a similar downhole environment; and determine the presence of the downhole anomaly based on a comparison of the downhole anomaly with another downhole anomaly present in a similar downhole environment.

Clause 17, a machine-readable medium comprising instructions stored therein, which when executed by one or more processors, causes the one or more processors to perform operations comprising: obtaining real-time transmissions of images from a camera of a logging tool; obtaining real-time transmissions of data from the logging tool; determining a presence of a downhole anomaly based on the real-time transmissions of images and the real-time transmissions of data; analyzing the downhole anomaly; and improving performance of a subsequent downhole inspection operation based on an analysis of the downhole anomaly.

Clause 18, the machine-readable medium of clause 17, further comprising instructions stored therein, which when executed by one or more processors, causes the one or more processors to perform operations comprising utilizing artificial intelligence techniques to determine the presence of the downhole anomaly.

Clause 19, the machine-readable medium of clauses 17 or 18, further comprising instructions stored therein, which when executed by one or more processors, causes the one or more processors to perform operations comprising utilizing computer vision with machine learning to determine the presence of the downhole anomaly.

Clause 20, the machine-readable medium of any of clauses 17-19, further comprising instructions stored therein, which when executed by one or more processors, causes the one or more processors to perform operations comprising: utilizing computer vision with machine learning to compare the downhole anomaly with another downhole anomaly present in a similar downhole environment; and determining the presence of the downhole anomaly based on a comparison of the downhole anomaly with another downhole anomaly present in a similar downhole environment.

As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise” and/or “comprising,” when used in this specification and/or the claims, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. In addition, the steps and components described in the above embodiments and figures are merely illustrative and do not imply that any particular step or component is a requirement of a claimed embodiment.