Patent Grant
11939827
The present disclosure applies to measuring and estimating conditions while drilling wells, e.g., oil wells.
From its name, bottom-hole pressure is the pressure at the bottom of the well, which consists of static and dynamic pressures. During mud circulation, BHP is equal to a sum of hydrostatic pressure and frictional pressure generated in the annulus. Monitoring BHP is essential during drilling operations to ensure that the applied static/dynamic fluid pressure is within the drilling margin. For example, this can ensure that a drilling mud has a sufficient mud weight to avoid wellbore instability but not greater than a fracture pressure that would induce formation fractures. Induced formation fractures (and therefore mud circulation losses) can occur when the BHP rises above the fracture pressure. Although in some cases the mud losses due to hydraulic fracturing can be minor and/or only occur while circulating, the incidents can still incur a significant cost due to the lost drilling mud. However, in cases where the fracture propagation pressure may be significantly less than the fracture initiation pressure (e.g., highly depleted sands), a total loss of circulation can result in an uncontrolled flow of the drilling mud/gas/hydrocarbons to the surface (e.g., a kick/blowout) from a shallower, less depleted zones, or potentially borehole collapse.
One way to measure BHP is to run Pressure While Drilling (PWD) tools. PWD tools can be installed in the Bottom Hole Assembly (BHA) and can be capable of measuring pressure and ECD in real-time. A significant downside of PWD tools is that they are extremely expensive. As a result of such costs, not all wells are drilled with PWD tools. This means that some wells are being drilled blindly without having a tool that could inform the driller of the actual pressure in the well.
Insufficient hole cleaning procedure during drilling operations can lead to a build-up of cuttings either at total depth (TD) in vertical wells or on the low side in horizontal wells. The cuttings build-up can increase the cuttings concentration percentage and, as a result, the density and viscosity of the mud system can increase. Poor hole cleaning can be a major contributor to other non-productive-time, such as stuck pipe incidents, loss of circulation, and formation fracturing, which can be induced due to the high equivalent circulating density (ECD) caused by the presence of excess cuttings. Frequent and global causes of stuck pipe incidents include the inefficient removal of formation cuttings from the wellbore while drilling.
The present disclosure describes techniques that can be used to estimate bottom hole pressure (BHP) at a drill bit during a drilling operation through the use of smart polymers introduced into drilling fluid and photographed in returning drilling mud after exposure to downhole conditions. The techniques can include the use of pressure-responsive polymers to measure the BHP in the oil and gas industry. In some implementations, a computer-implemented method includes the following. Units of smart polymers that are pressure-responsive are inserted by a monitoring system into drilling fluid pumped into a well during a drilling operation. An insertion timestamp associated with each unit is stored by the monitoring system. Each insertion timestamp indicates a time that each unit was inserted into the drilling fluid. Continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined by the monitoring system using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to be made to drilling parameters for the drilling operation are suggested by the monitoring system based at least in part on the estimate of the BHP.
The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method, the instructions stored on the non-transitory, computer-readable medium.
The subject matter described in this specification can be implemented in particular implementations, so as to realize one or more of the following advantages. The techniques of the present disclosure can provide a robust, efficient method to measure BHP. This can replace procedures that measure BHP by running theoretical Equivalent Circulating Density (ECD) simulation models to predict BHP based on several inputs. Although such models are cost efficient, the models are limited to being a mere prediction tool and do not provide actual measurements of pressure near the bit while drilling. The techniques described in the present disclosure can take advantage of emerging technologies aligned with the fourth industrial revolution (4IR), such as automation, Internet of Things (IoT), artificial intelligence (AI) machine learning, and data analytics. Techniques can include the use of various applications to ensure safe drilling operations and to optimize drilling performance. For example, optimizing operations and drilling performance can refer to achieving oil production values that indicate or result in a performance greater than a predefined threshold (e.g., a threshold percentage improvement of oil production versus past production values). Using smart polymers, e.g., as pills injected into drilling mud, can be an early indicator of poor hole cleaning. For example, a sudden increase in the measured pressure can alert the driller of potential poor hole cleaning that may result in a stuck pipe due to pack-off. Safe operational margins can be predicted, and alerts can be generated if operations fall outside of the safe operational margins. A hole cleaning efficiency index can be determined based on pressure.
The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the accompanying drawings, and the claims. Other features, aspects, and advantages of the subject matter will become apparent from the Detailed Description, the claims, and the accompanying drawings.
Like reference numbers and designations in the various drawings indicate like elements.
The following detailed description describes techniques for using smart polymers and a camera positioned in a well (e.g., at the shale shaker) to evaluate bottom hole pressure (BHP) at different timestamps, e.g., after the smart polymers are injected into drilling fluid, exposed to downhole conditions, and returned in drilling mud. The techniques can be used, for example, to construct a pressure profile for the well. For example, the pressure profile can be based on an analysis of images captured through the use of smart polymers that are returned from the bottom hole, such as through the drilling mud. Various modifications, alterations, and permutations of the disclosed implementations can be made and will be readily apparent to those of ordinary skill in the art, and the general principles defined may be applied to other implementations and applications, without departing from the scope of the disclosure. In some instances, details unnecessary to obtain an understanding of the described subject matter may be omitted so as to not obscure one or more described implementations with unnecessary detail and inasmuch as such details are within the skill of one of ordinary skill in the art. The present disclosure is not intended to be limited to the described or illustrated implementations, but to be accorded the widest scope consistent with the described principles and features.
The present disclosure describes techniques that use smart fluids/polymers and a camera recording drilling mud returns at the shale shaker to predict BHP at the bit. For example, the techniques of the present disclosure can make use of pressure as a trigger to change the color/intensity of smart polymers/fluids. Then, the change in color/intensity of smart polymers/fluids can be automatically detected by computational models that analyze frames captured by the camera. Smart polymers refer to and include stimuli-responsive polymers that change properties according to environments in which they are placed. Different stimuli can include, for example, pressure, temperature, potential of hydrogen (pH), and ionic strength. Changes in properties can include, for example, shape, chemical properties, and color. The present disclosure focuses on pressure as a trigger to change the color of smart polymers/fluids. The change of color can be detected by one or more cameras at one or more locations (e.g., including a location at the shale shaker).
Image data from the camera/vision sensors are expected to be primarily processed in continuous recording to capture the trends of the flow over time. The frames from the camera are processed by the image processing algorithms and Machine Learning/Deep Learning models deployed in the edge/fog server. The methods described in the present disclosure use a set of image processing techniques to detect the polymer intensity/color/light as well as to enhance the contrast and brightness of the frames as triggered by pressure values. These image processing techniques may include pixilation, image segmentation, intensity quantification, and supervised learning regression models (including ML and DL). The algorithms convert the images to arrays that can be later translated to numerical values referring to the pressure measurements.
To develop and train the ML model, the data generated from a PWD tool in one or multiple wellbores are assigned to the respective continuous recorded images of the polymer pills returning to the shale shaker. The recorded images along with the pressure data from PWD are then used to train a model that can later predict the BHP in psi. That is, a frame x containing known pixel intensities is assigned a BHP value as observed by the PWD. Alternatively, another method to train the model is to use the polymer pill in a laboratory environment where pressures on the fluid and responses are well known and measured. One way to achieve this goal is to use any instrument that has the capability of applying and measuring high pressures, e.g., a cement consistometer. Certain pressures can be applied, and images of the resulting polymers can be captured. The images, along with the actual applied pressures, are then used to train the model to predict the BP. Notably, the PWD data or laboratory data are required for the learning phase of the model, as these data represent the targets/labels. After the model is derived, the objective of the model is to predict the returning polymer's pressure value using the images captured and processed during real-time drilling operations. The term real-time can correspond, for example, to events that occur within a specified period of time, such as determining BHP within one second of receiving smart polymers exposed to down-hole conditions.
The present disclosure describes the use of smart polymers that react to pressure stimuli. The use of smart polymers can be combined with the use of an Internet of things (IoT) platform used at, or in communication with, a drilling rig. The IoT platform can include (or be in communication with): 1) smart, waterproof, high-resolution, wireless cameras, or any other image or vision sensor, including infra-red sensors, gamma ray sensors, computed tomography (CT) scans, x-rays, or image/video capture techniques; 2) edge/fog computing hardware; and 3) application software for image/video processing to transform discrete images to digital pressure readings. In some implementations, red-green-blue (RGB) mappings to specific pressures can be developed once the smart polymers are formulated.
Drilling operations typically consist of breaking rock to deepen a wellbore. An essential component for drilling operations is the drilling mud that circulates from the surface through the drilling pipe, into the hole, and back again from the annulus to the surface. Although drilling muds have multiple functions (e.g., lubrication and cooling, cuttings transport, among others), the present disclosure focuses on the drilling mud effects to maintain pressure in the wellbore to balance formation pressure, e.g., to maintain safety. Consideration of only the hydrostatic pressure exerted by the drilling mud may not be accurate enough to ensure safe drilling operations, as the pressure differs along the wellbore depth. Dynamic BHP, which can be more accurate, refers to the dynamic density exerted by the circulation of drilling mud in the annulus plus the hydrostatic pressure. Monitoring BHP is essential to ensure that the applied dynamic fluid pressure is within the drilling margin, e.g., a difference between the fracture pressure and either the pore pressure or the minimum mud pressure (e.g., whichever of the two is higher). Monitoring BHP can prevent wellbore instability, for example.
Unlike hydrostatic pressure, dynamic BHP is the sum of the static mud weight plus the annular frictional pressure loss in dynamic conditions. As drilling fluid exits the drill bit through the nozzles and flows to the surface, fluid-contact with the wellbore creates friction, which exerts additional pressure on the rock formation. BHP, e.g., measured in pounds per square inch (psi), can be calculated using the following equation:
BHP=Phydrostatic+ΔPfrictional (1)
where Phydrostatic and ΔPfrictional refer to the hydrostatic pressure and annular frictional pressure loss, respectively. Hydrostatic pressure is the force per unit area that the drilling mud column is exerting on the formation at any given depth. Hydrostatic pressure in psi can be calculated using the following formula:
Phydrostatic=MW×Depth×0.052 (2)
where MW is the mud weight in pounds per gallon (ppg).
If the mud pumps are shut off and the drilling fluid is not circulating through the annulus (static), then the annular frictional pressure is zero, and BHP can be reduced to:
BHP=Phydrostatic (3)
In cases where surface backpressure is applied using a surface choke (e.g., in managed pressure drilling), then this pressure can also be added to the aforementioned equations to compute the total dynamic/static BHP.
If the BHP rises above the fracture pressure, then the rock formation can break, causing mud losses to occur. If mud losses due to hydraulic fracturing are minor and/or only occur while circulating, one of the greatest concerns, in this case, can include the often-significant cost of the lost drilling mud. However, in cases where the fracture propagation pressure may be significantly less than the fracture initiation pressure (e.g., in highly-depleted sands), a resulting total loss of circulation can result in an uncontrolled flow to the surface (kick/blowout) from shallower, less-depleted zones, or potentially borehole collapse.
The present disclosure addresses the current limitations with BHP measurements. The least expensive procedure to measure BHP is to run theoretical Equivalent Circulating Density (ECD) simulations that can estimate ECD based on several inputs. Estimates of ECD can then be converted to estimates of BHP. Although ECD models are cost efficient, ECD simulations are limited to being only a prediction tool and not actual measurements of pressure at the bit, as the ECD simulations fail to integrate all the physical phenomena involved during drilling. An alternative way to measure BHP is to use Pressure While Drilling (PWD) tools. PWD tools can be installed in the Bottom Hole Assembly (BHA) and are capable of measuring pressure and ECD in real-time. The term real-time can correspond, for example, to events that occur within a specified period of time, such as determining BHP within one second of receiving smart polymers exposed to down-hole conditions. However, the use of PWD tools can considerably increase the costs of operations as well as introducing points of failure due to the use of additional sensors. As a result, not all wells are drilled with PWD tools. This means that some wells are being drilled blindly without having a tool that can inform the driller of the actual pressures that exist downhole.
The calculation of the BHP is a challenging task due to different downhole factors and conditions, such as the amount of cuttings in the wellbore at different depths, and pressure exerted by the mud friction, among other factors/conditions. To overcome these challenges, the present disclosure describes a method that utilizes smart fluids/polymers and a camera at the shale shaker to predict/estimate the BHP at the drilling bit.
The present disclosure describes the use of pressure as a trigger to change the color/intensity of smart polymers/fluids that can be automatically detected by computational models to analyze frames obtained by one or more cameras that are recording images at the shale shakers. There are two types of stimuli-responsive polymers: reversible and irreversible. Reversible polymers return to their natural state once the trigger has been eliminated from the environment. The present disclosure focuses on irreversible polymers, where properties do not return back to their initial state. Irreversible polymers can enable the detection of maximum BHP measured at any given wellbore depth, which is often at the bit due to an increasing hydrostatic column of the fluid.
In the techniques described in the present disclosure, the smart polymers (e.g., in the form of pills of various sizes and shapes) are designed to be pumped with the drilling fluid used during drilling operations. As an example, the pills can be pumped with the drilling mud at different intervals (e.g., every one, three or five minutes). In another example, the pills can be pumped every one stand (e.g., every 90 feet). In this way, the smart polymers delivered as pills can serve as “pressure polymers” that are designed to be triggered by mechanical stress. For example, the pressure-responsive polymers can change properties as a function of the mechanical stress applied. As smart polymer pills exit the well through the annulus, for example, the camera at the shale shaker can capture continuous images of the returning mud. Image processing algorithms as well as machine-learning (ML) and deep-learning (DL) models can be used to predict/estimate the BHP. One or more models can then correlate the timestamps of the smart polymer pills' arrival at the shale shaker with the respective hole depth by utilizing the rig sensor for mud flow rate and knowing the annular area of the well.
Pressure-sensitive polymers can be used in operations that require underbalanced drilling. In underbalance drilling, the operations are designed and executed such that the BHP is less than the formation pressure. In such cases, a camera at the shale shaker for capturing images of pressure sensitive polymers can offer a new method to measure the BP. Techniques of the present disclosure can include the use of ML/DL models capable of determining digital numerical pressure values using image data. For example, predicting the BHP can be based on observed characteristics of the smart polymer at the shale shaker.
The input data 204 can include drilling parameters and sensor data 208 and image data (e.g., shale shaker images 210). The drilling parameters and sensor data 208 can include depth data, mud flow rates, mud rheology, stand pipe pressures, and pressures (PWD).
The edge/fog server 202 can use various models, including physics-based models 212 and supervised learning models 214, which can serve as inputs to data-driven ML/DL models 216. The physics-based models 212 can use as input ECD data. The supervised learning models 214 can use as input outputs of image processing algorithms 220 that can perform functions 222 including image segmentations, intensity quantification, and brightness enhancement.
BHP Prediction
Image data from the camera/vision sensors are expected to be primarily processed in continuous recording to capture the trends of the flow over time. The frames from the camera can be processed by the image processing algorithms 220 and the ML/DL models 216 deployed in the edge/fog server 202. The methods described in the present disclosure use a set of image processing techniques to detect polymer features (e.g., the polymer intensity, color, and light) in the frame as well as to enhance the contrast and brightness of the frames. These image processing techniques can include pixilation, image segmentation, intensity quantification, or supervised learning models (e.g., including ML and DL). The algorithms can convert the images to arrays (e.g., multi-dimensional arrays) that can later be translated to digitize numerical values of pressure. The numerical representation of the intensities of the polymers mixed with the fluid observed at the shale shakers can be directly used to estimate the BHP. For instance, a simple logistic regression model may be used as follows:
BHP=β×max(pixel intensity) (4)
Where β refers to the coefficient learned by a regression model and max (pixel intensity) to the pixel with the highest intensity values in a frame, respectively. BHP refers to the pressure values as measured by the PWD tool. The linear regression, as a supervised learning model, can learn this relationship by observing multiple samples S with their respective target labels (BHP).
A linear regression model may also include other terms to include drilling mud flow rates, mud weight, and rheology, among others, as follows:
BHP=β×max(pixel intensity)+α×flowrate+γ×mudweight+δ×viscosity (5)
These models, however, can observe one or multiple pixels in the frame and can have limited capabilities to find non-linear relationships between the inputs and targets. Consequently, supervised learning DL models, such as convolutional neural networks (CNN), auto encoder neural networks (AE-NN), among others, can be derived from the frames to classify the observed (intensity/color/light) images. These DL models can automatically extract abstract features from the frames that can be linked to the BHP as a target. In supervised learning, each frame containing the set of intensities observed from the smart polymer can be assigned a label (BHP in psi) to train the regression DL model, as shown in
To label the data (frames with their respective label), the data generated from a PWD tool in one or multiple wellbores can be assigned to the respective continuous recorded images of the polymer pills returning to the shale shaker. The recorded images along with the pressure data from the PWD tool can then be used to train a model that can later predict the BHP in psi. That is, a frame x containing known pixel intensities can be assigned a BHP value as observed by the PWD. Alternatively, another method to train the model is to use the polymer pill in a laboratory environment where pressures on the fluid and response are well known and measured. One way to achieve this goal is to use an instrument that has the capability of applying and measuring high pressures, e.g., a cement consistometer. Certain pressures can be applied, and images of the resulting polymers can be captured. These images, along with the actual applied pressures, can then be used to train the model to predict the BHP. The PWD data or laboratory data can be used for the learning phase of the model, as these represent the targets/labels. After the model is derived, the objective of the model is to predict these values.
Safe Operational Margins Prediction
Occasionally, the margins between pore and fracture pressures are very narrow, depending on the formation tops, e.g., during underbalanced drilling. During these operations, it is critical for the crew to accurately identify safe operational parameters to avoid possible influxes or drilling mud circulation losses (e.g., due to induced fractures). A similar model as the one described with reference to
Hole Cleaning Performance
Each image frame can be transformed into an abstract feature 406 representation using a CNN 404 (or using other DL model or image processing techniques). As an alternative to CNN, other frameworks can use image processing techniques or DL models (e.g., AE-NN) for generating the abstract features 406. These features can then be concatenated with additional features 408 that can include, for example, mud flow rate, stand pipe pressure, and hole diameter, among others. Finally, the set of features can be fed as input into a sequence model 410, e.g., recurrent neural network/long short-term memory (RNN/LSTM) cells. The sequence model 410 can produce an output layer that makes a single value prediction, e.g., an estimated hole cleaning performance 412. Data labels can be allocated in several ways, e.g., returning cutting volume and shape quantification using the camera mounted on the shale shakers 108 processed using computer vision techniques.
Chemical Concept
Smart polymers can include the use of small plastic architected beads inclusive of green fluorescent protein chromophore (GFPC) analogue, ethyl[(4Z)-2-methyl-5-oxo-4-(2,3,4-trimethoxy benzylidene)-4,5-dihydro-1H-imidazol-1-yl] acetate (hereafter, BDI) for use in irreversible pressure sensing. Plastics containing a GFPC analogue that then undergo plastic deformation can produce a corresponding shift in fluorescence energy. The reason for this is that the BDI molecule, with several hydrophobic functional groups such as —OCH3, —COOEt, and —CH3, is ideal for plastic bending, as these groups often lead to formation of slip or weak interaction planes in crystals.
There are many potential structures which can be architected to achieve these ends. For example, a hollow sphere composed of plastic with embedded BDI can be designed such that the hollow sphere is rated for a certain (e.g., pre-determined) crush strength. For example, a hollow sphere (e.g., plastic with BDI) rated to 4000 psi would crush above 4000 psi. The fluorescence signal change can be seen in the visible light region of the spectrum, providing an indicator that the material was exposed to pressures exceeding 4000 psi. Similarly, a hollow plastic sphere rated to 10,000 psi may be a sensor for measuring exposures to pressures in excess of 10,000 psi.
At 502, units of smart polymers that are pressure-responsive are inserted by a monitoring system into drilling fluid pumped into a well during a drilling operation. The units of smart polymers can have a pill shape, for example. The units of smart polymers are configured to change properties as a function of mechanical stress and increasing pressures applied to the units of smart polymers by downhole conditions. In some implementations, pumping the units of smart polymers into the drilling fluid can occur at different intervals (e.g., every one, three, or five minutes) or can be pumped every one stand (e.g., every 90 feet). From 502, method 500 proceeds to 504.
At 504, an insertion timestamp associated with each unit is stored by the monitoring system. Each insertion timestamp indicates a time that each unit was inserted into the drilling fluid. From 504, method 500 proceeds to 506.
At 506, continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera positioned at a sensing location and linked to the monitoring system. For example, capturing the continuous images can include capturing, in the units of smart polymers, evidence of mechanical stress caused by pressure changes experienced by the units of smart polymers. The sensing location can be, for example, a shale shaker, a centrifuge, a de-sander, and a de-silter. From 506, method 500 proceeds to 508.
At 508, an estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined by the monitoring system using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Estimating the BHP can include correlating an arrival timestamp identifying a time of arrival of each unit of smart polymer at the sensing location with a respective hole depth by utilizing a rig sensor for mud flow rate and based on an annular area of the well. From 508, method 500 proceeds to 510.
At 510, changes to be made to drilling parameters for the drilling operation are suggested by the monitoring system based at least in part on the estimate of the BP. For example, changes can be made in drilling parameters that are associated with changes in mud rheology, mud weight, and mud flow rate. After 510, method 500 can stop.
In some implementations, in addition to (or in combination with) any previously-described features, techniques of the present disclosure can include the following. Outputs of the techniques of the present disclosure can be performed before, during, or in combination with wellbore operations, such as to provide inputs to change the settings or parameters of equipment used for drilling. Examples of wellbore operations include forming/drilling a wellbore, hydraulic fracturing, and producing through the wellbore, to name a few. The wellbore operations can be triggered or controlled, for example, by outputs of the methods of the present disclosure. In some implementations, customized user interfaces can present intermediate or final results of the above described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or “app”), or at a central processing facility. The presented information can include suggestions, such as suggested changes in parameters or processing inputs, that the user can select to implement improvements in a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the suggestions can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The suggestions, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction. In some implementations, the suggestions can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time can correspond, for example, to events that occur within a specified period of time, such as within one minute or within one second. Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.
The computer 602 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 602 is communicably coupled with a network 630. In some implementations, one or more components of the computer 602 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.
At a top level, the computer 602 is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer 602 can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.
The computer 602 can receive requests over network 630 from a client application (for example, executing on another computer 602). The computer 602 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 602 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.
Each of the components of the computer 602 can communicate using a system bus 603. In some implementations, any or all of the components of the computer 602, including hardware or software components, can interface with each other or the interface 604 (or a combination of both) over the system bus 603. Interfaces can use an application programming interface (API) 612, a service layer 613, or a combination of the API 612 and service layer 613. The API 612 can include specifications for routines, data structures, and object classes. The API 612 can be either computer-language independent or dependent. The API 612 can refer to a complete interface, a single function, or a set of APIs.
The service layer 613 can provide software services to the computer 602 and other components (whether illustrated or not) that are communicably coupled to the computer 602. The functionality of the computer 602 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 613, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer 602, in alternative implementations, the API 612 or the service layer 613 can be stand-alone components in relation to other components of the computer 602 and other components communicably coupled to the computer 602. Moreover, any or all parts of the API 612 or the service layer 613 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.
The computer 602 includes an interface 604. Although illustrated as a single interface 604 in
The computer 602 includes a processor 605. Although illustrated as a single processor 605 in
The computer 602 also includes a database 606 that can hold data for the computer 602 and other components connected to the network 630 (whether illustrated or not). For example, database 606 can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database 606 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer 602 and the described functionality. Although illustrated as a single database 606 in
The computer 602 also includes a memory 607 that can hold data for the computer 602 or a combination of components connected to the network 630 (whether illustrated or not). Memory 607 can store any data consistent with the present disclosure. In some implementations, memory 607 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 602 and the described functionality. Although illustrated as a single memory 607 in
The application 608 can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 602 and the described functionality. For example, application 608 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 608, the application 608 can be implemented as multiple applications 608 on the computer 602. In addition, although illustrated as internal to the computer 602, in alternative implementations, the application 608 can be external to the computer 602.
The computer 602 can also include a power supply 614. The power supply 614 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 614 can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power supply 614 can include a power plug to allow the computer 602 to be plugged into a wall socket or a power source to, for example, power the computer 602 or recharge a rechargeable battery.
There can be any number of computers 602 associated with, or external to, a computer system containing computer 602, with each computer 602 communicating over network 630. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 602 and one user can use multiple computers 602.
Described implementations of the subject matter can include one or more features, alone or in combination.
For example, in a first implementation, a computer-implemented method includes the following. Units of smart polymers that are pressure-responsive are inserted by a monitoring system into drilling fluid pumped into a well during a drilling operation. An insertion timestamp associated with each unit is stored by the monitoring system. Each insertion timestamp indicates a time that each unit was inserted into the drilling fluid. Continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined by the monitoring system using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to be made to drilling parameters for the drilling operation are suggested by the monitoring system based at least in part on the estimate of the BP.
The foregoing and other described implementations can each, optionally, include one or more of the following features:
A first feature, combinable with any of the following features, where the units of smart polymers have a pill shape.
A second feature, combinable with any of the previous or following features, the method further including pumping the units of smart polymers into the drilling fluid at different intervals or every one stand.
A third feature, combinable with any of the previous or following features, where the units of smart polymers are configured to change properties as a function of mechanical stress and increasing pressures applied to the units of smart polymers by downhole conditions.
A fourth feature, combinable with any of the previous or following features, where capturing the continuous images includes capturing, in the units of smart polymers, evidence of mechanical stress caused by pressure changes experienced by the units of smart polymers.
A fifth feature, combinable with any of the previous or following features, where estimating the BHP includes correlating an arrival timestamp identifying a time of arrival of each unit of smart polymer at the sensing location with a respective hole depth by utilizing a rig sensor for mud flow rate and based on an annular area of the well.
A sixth feature, combinable with any of the previous or following features, where the sensing location is selected from the group consisting of a shale shaker, a centrifuge, a de-sander, and a de-silter.
In a second implementation, a non-transitory, computer-readable medium stores one or more instructions executable by a computer system to perform operations including the following. Units of smart polymers that are pressure-responsive are inserted by a monitoring system into drilling fluid pumped into a well during a drilling operation. An insertion timestamp associated with each unit is stored by the monitoring system. Each insertion timestamp indicates a time that each unit was inserted into the drilling fluid. Continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined by the monitoring system using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to be made to drilling parameters for the drilling operation are suggested by the monitoring system based at least in part on the estimate of the BP.
The foregoing and other described implementations can each, optionally, include one or more of the following features:
A first feature, combinable with any of the following features, where the units of smart polymers have a pill shape.
A second feature, combinable with any of the previous or following features, the operations further including pumping the units of smart polymers into the drilling fluid at different intervals or every one stand.
A third feature, combinable with any of the previous or following features, where the units of smart polymers are configured to change properties as a function of mechanical stress and increasing pressures applied to the units of smart polymers by downhole conditions.
A fourth feature, combinable with any of the previous or following features, where capturing the continuous images includes capturing, in the units of smart polymers, evidence of mechanical stress caused by pressure changes experienced by the units of smart polymers.
A fifth feature, combinable with any of the previous or following features, where estimating the BHP includes correlating an arrival timestamp identifying a time of arrival of each unit of smart polymer at the sensing location with a respective hole depth by utilizing a rig sensor for mud flow rate and based on an annular area of the well.
A sixth feature, combinable with any of the previous or following features, where the sensing location is selected from the group consisting of a shale shaker, a centrifuge, a de-sander, and a de-silter.
In a third implementation, a computer-implemented system includes one or more processors and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors. The programming instructions instruct the one or more processors to perform operations including the following. Units of smart polymers that are pressure-responsive are inserted by a monitoring system into drilling fluid pumped into a well during a drilling operation. An insertion timestamp associated with each unit is stored by the monitoring system. Each insertion timestamp indicates a time that each unit was inserted into the drilling fluid. Continuous images and observed characteristics of drilling mud exiting through an annulus of the well and containing the units of smart polymers are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of a bottom hole pressure (BHP) at a drill bit of the drilling operation is determined by the monitoring system using the continuous images, the observed characteristics, and the insertion timestamps associated with each unit of smart polymer. Determining the estimate is based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Changes to be made to drilling parameters for the drilling operation are suggested by the monitoring system based at least in part on the estimate of the BIP.
The foregoing and other described implementations can each, optionally, include one or more of the following features:
A first feature, combinable with any of the following features, where the units of smart polymers have a pill shape.
A second feature, combinable with any of the previous or following features, the operations further including pumping the units of smart polymers into the drilling fluid at different intervals or every one stand.
A third feature, combinable with any of the previous or following features, where the units of smart polymers are configured to change properties as a function of mechanical stress and increasing pressures applied to the units of smart polymers by downhole conditions.
A fourth feature, combinable with any of the previous or following features, where capturing the continuous images includes capturing, in the units of smart polymers, evidence of mechanical stress caused by pressure changes experienced by the units of smart polymers.
A fifth feature, combinable with any of the previous or following features, where estimating the BHP includes correlating an arrival timestamp identifying a time of arrival of each unit of smart polymer at the sensing location with a respective hole depth by utilizing a rig sensor for mud flow rate and based on an annular area of the well.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.
The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, such as LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.
A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub-programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.
The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.
Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory.
Graphics processing units (GPUs) can also be used in combination with CPUs. The GPUs can provide specialized processing that occurs in parallel to processing performed by CPUs. The specialized processing can include artificial intelligence (AI) applications and processing, for example. GPUs can be used in GPU clusters or in multi-GPU computing.
A computer can include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto-optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.
Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD-ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLU-RAY.
The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated into, special purpose logic circuitry.
Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that the user uses. For example, the computer can send web pages to a web browser on a user's client device in response to requests received from the web browser.
The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch-screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.
The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship.
Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at the application layer. Furthermore, Unicode data files can be different from non-Unicode data files.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.
Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.
Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.
| Number | Name | Date | Kind |
|---|---|---|---|
| 10905636 | Musa et al. | Feb 2021 | B2 |
| 20190375978 | Shojaei et al. | Dec 2019 | A1 |
| 20190382519 | Musa et al. | Dec 2019 | A1 |
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| Bhattacharya et al., “Mechanical-Bending-Induced Fluorescence Enhancement in Plastically Flexible Crystals of a GFP Chromophore Analogue,” Angewandte Chemie, Jul. 2020, 132(45):20050-20055, 6 pages. |
| Guo et al., “Fluorescence Chemosensors for Hydrogen Sulfide Detection in Biological Systems,” Analyst, Mar. 2015, 140(6):1772-1786, 18 pages. |
| Gustafson et al., “Design of Irreversible Optical Nanothermometers for Thermal Ablations,” Chemical Communications, Jan. 2013, 49(7):680-682, 3 pages. |
| Han et al., “Fluorescent Indicators for Intracellular pH,” Chem. Rev., May 2010, 110(5):2709-2728, 20 pages. |
| Herrmann, “Dynamic Combinatorial/Covalent Chemistry: A Tool to Read, Generate and Modulate the Bioactivity of Compounds and Compound Mixtures,” Chemical Society Reviews, Mar. 2014, 43(6):1899-1933, 36 pages. |
| Li et al., “Dually emitting carbon docts as fluorescent probes for ratiometric fluorescent sensing of pH values, mercury(II), chloride and Cr(VI) via different mechanisms,” Microchimica Acta, May 2019, 186(6):341, 10 pages. |
| Qu et al., “Polyethyleneimine-templated Ag nanoclusters: A new fluorescent and colorimetric platform for sensitive and selective sensing halide ions and high disturbance-tolerant recognitions of iodide and bromide in coexistence with chloride under condition of high ionic strength,” Analytical Chemistry, Nov. 2012, 84(23):10373-10379, 7 pages. |
| Stefani et al., “Thermochromic Fluorophores and Their NIR Laser Induced Transformation,” Chemistry of Materials, Dec. 2006, 18(26):6115-6120, 6 pages. |
| Tollan et al., “Irreversible Thermochromic Behavior in Gold and Silver Nanorod/Polymeric Ionic Liquid Nanocomposite Films,” ACS Applied Materials & Interfaces, Feb. 2009, 1(2):348-352, 5 pages. |
| Yu et al., “Carbon-Dot-Based Ratiometric Fluorescent Sensor for Detecting Hydrogen Sulfide in Aqueous Media and inside Live Cells,” Chemical Communications, Jan. 2013, 49(4):403-405, 3 pages. |
