Patent Grant
10900343
The application generally relates to control systems and methods for drilling. The application relates more specifically to autonomous methods for controlling drilling parameters based on drilling medium characteristics.
Historically the process of drilling, e.g. for oil and gas exploration, geothermal wells, and the like, has been a process requiring users to apply intuition and experience to continuously adjust drilling system parameters to achieve acceptable drilling. Parameters must change as the drilling system dynamics, the drilling medium, e.g. rock types, and other process elements vary. Automation and autonomous control of drilling equipment may significantly improve performance by allowing more rapid adjustment to varying conditions based on measurement of drilling parameters and on models of drilling, wherein the models are based on scientific principles. The use of such technology may increase drilling speed, reduce equipment failure, and provide greater energy efficiency in the drilling process. Given the large scale and enormous costs associated with drilling, changes of a few percent in such metrics may reap enormous economic benefits.
Rotary drilling is a complex process that is largely controlled by highly trained and experienced human operators. Drilling conditions may change constantly during the drilling operation in response to heterogeneous rock formations, bit wear, and interactions between a drill string and the wellbore. Furthermore, observed conditions at the surface may differ dramatically from conditions downhole. Improving drilling performance can have an enormous economic impact by reducing the time spent drilling, on a per-unit basis, and by reducing costly equipment failures.
Drilling operations are repetitive and inherently dangerous. Automation of drilling operations and autonomous control of operations may improve safety, enhance drilling operations in harsh environments, and increase drilling efficiency. Field data discloses that automated drilling systems may achieve improvements in penetration rate of 10% or greater. Despite the potential benefits from automation, field drilling is largely a manual process, currently, in which operators continuously adjust to conditions to achieve basic regulation of routine control setpoints.
One approach to autonomous drilling has been to optimize high level drilling performance metrics such as the rate of penetration (ROP) or the mechanical specific energy (MSE). MSE is the amount of energy expended in removing a unit volume of rock, with units typically in pounds per square inch (psi). For example, the Fastdrill technology by Exxon-Mobil estimates MSE online and provides prompts to the driller with suggested setting changes. Recently, several research groups have developed and tested optimizing automation tools that attempt to maximize ROP based on measured signals in the rock. They exploit a model to predict drilling performance. Some may employ the Bourgoyne and Young model as described in A. T. Bourgoyne, F. S. Young, “A Multiple Regression Approaches to Optimal Drilling and Abnormal Pressure Detection,” Journal Of The Society Of Petroleum Engineers, Vol. 14(4), 1974, Pp. 371-384, and others employ the Jorden and Shirley model as described in R. Jorden, O. Shirley, “Application of Drilling Performance Data to Overpressure Detection,” Paper SPE 1407 presented at the SPE Symposium on Offsore Technology and Operations, New Orleans, La., May 1966, pp. 1387-1394. Still others employ a phenomenological rock-bit interaction model developed by Detournay. The use of model fitting approaches may be complicated by the unknown properties of the rock formation and its inhomogeneity.
Different rock types have very different characteristics defined by unique model parameters, and indiscriminate modeling across rock types will result in inaccurate predictions. Furthermore, key parameters in the most effective rock-bit interaction models also depend on bit characteristics, including wear over time. Therefore, the ability to determine the rock type and detect changes in real time is essential to successful automation.
A Bayesian change point detector may be used to determine a variation in rock formation. The Detournay parameters for the data segment are then determined and used to determine optimal drilling settings which are then presented to the driller or used in a feedback control method to maximize ROP.
What is needed is a system and/or method that satisfies one or more of these needs or provides other advantageous features. Other features and advantages will be made apparent from the present specification. The teachings disclosed extend to those embodiments that fall within the scope of the claims, regardless of whether they accomplish one or more of the aforementioned needs.
One embodiment relates to a method for autonomously controlling a rotary drilling system includes applying a predetermined force (sometimes called “weight-on-bit”) setpoint to a first controller; applying a predetermined rotary speed to a second controller; applying a first controller output and a second controller output to a drilling process module; measuring a plurality of outcome parameters of the drilling process module; receiving drilling process inputs and process outcome parameters; estimating a plurality of rock parameters associated with a rock type based on drilling process inputs and process outcome parameters; comparing the estimated drilling medium (e.g. rock) parameters with a database of drilling medium profiles; determining whether a change in the outcome parameters have occurred which indicate that a change in the drilled material has occurred; searching the database rock profiles for optimal operating conditions in response to determining that a change in the material being drilled is indicated; generating an updated set of drilling parameters corresponding to the optimal operating conditions rock parameters in response to the comparing of database rock profiles; transmitting the updated set of drilling parameters comprising the force setpoint and rotary speed adjusting the drilling parameters by subtracting measured drilling parameters from the updated set of drilling parameters; generating desired control actuator setpoints for predetermined force and predetermined rotary speed; systematically varying one or more control setpoints in the vicinity of the drilling parameters indicated by the database and simultaneously evaluating process outcome parameters to identify and ultimately converge to locally optimal drilling conditions in accordance with an optimal search algorithm; and adding new relationships between drilling process inputs and process outcome parameters, obtained from measurements of the drilling process, to the database of drilling medium profiles via a machine learning process.
Another embodiment relates to a method for controlling an autonomous percussive drilling system includes applying a force applied to the rock by the weight-on-bit setting, a hammer pressure, and a rotary speed; transmitting the force, hammer pressure and rotary speed to a drilling process for a drilling rig; transmitting parameter outputs as the drilling rig penetrates into rock layers in response to the input parameter setpoints; determining a plurality of outcomes of the drilling process; and classifying the drilling medium in response to measured drilling data by applying physics-based drilling models or by comparing to an existing database of drilling medium (e.g. rock) profiles; executing an algorithm in response to determining the drilling medium to computer predetermined operating conditions associated with the drilling medium; adjusting at least one of the force, pressure, or rotary speed of the drilling system to achieve the predetermined operating conditions; systematically varying one or more control setpoints in the vicinity of the predetermined operating parameters and simultaneously evaluating process outcome parameters to identify and ultimately converge to locally optimal drilling conditions in accordance with an optimal search algorithm; and updating the physical models of drilling and/or the database of drilling medium profiles based on the input parameters and measured drilling process outcome parameters, via a machine learning process.
An advantage of the disclosure is applications for both rotary and percussive drilling. The method includes online classification of drilling medium, e.g. rock type. For rotary drilling, the classification method includes a drilling model based on the most widely accepted theoretical model of rotary drag bit drilling, and identifies material type and drilling region (e.g. I, II, or III). Drilling region (sometimes called drilling phase) refers to the range of drilling conditions in which there is a prescribed relationship, often approximated as linear, between rate of penetration (ROP) and weight-on-bit (WOB) in regions I and II; region III may exhibit a similar relationship, but more generally incorporates complex effects of system dysfunction and is not usually characterized relationally. The model includes at least three drilling regions based on the alignment of measured parameters with the model. Parameters may be compared to test parameters determined from prior drilling data. In rotary and percussive drilling, a machine learning approach may be used, and measured drilling data may be compared in real time to data from historical drilling data, and classification determinations made based on said data. Measured data is also used to augment and improve the historical drilling database via machine learning.
Another advantage is intelligent control of autonomous penetration including novel control methods and algorithms to enable autonomous drilling through multi-layered structures. Related techniques are disclosed for both rotary and percussive techniques. The disclosed methods apply knowledge of the fundamental characteristics of the drilling processes based on prior published theory and experimental data. The methods apply multilayered control systems design to achieve improved drilling performance.
Alternative exemplary embodiments relate to other features and combinations of features as may be generally recited in the claims.
The application will become more fully understood from the following detailed description, taken in conjunction with the accompanying figures, wherein like reference numerals refer to like elements, in which:
Before turning to the figures which illustrate the exemplary embodiments in detail, it should be understood that the application is not limited to the details or methodology set forth in the following description or illustrated in the figures. It should also be understood that the phraseology and terminology employed herein is for the purpose of description only and should not be regarded as limiting.
A method for fully autonomous drilling is disclosed. The method is directed to autonomous management of transitions between multiple layers of different material, e.g., rock layers, using experimental data. In an embodiment the method utilizes a Detournay model as described below for rotary drag bit drilling. Further, the method uses a classifier algorithm and database from previous drilling data to correlate measured rock properties taken during a drill operation, with rock types and desired drilling control parameters. Data from one operating point is sufficient to estimate the rock type, the drilling region, and the optimal drilling settings for that rock type. The rock type is estimated continuously. Drilling parameters are updated in response to detected variations in the rock type. Local searches are performed around the prescribed optimal settings to determine the true optimal parameter, in response to minor deviations from the database data. Low level PI controllers may be used to regulate drilling parameters to desired settings. The Detournay model describes a phenomenological model of the drilling process for drag bits with polycrystalline diamond compacts (PDC) as the cutting surface. The Detournay model describes drilling as a three dimensional relationship between scaled weight (w), scaled torque (τ), and depth of cut (d), referred to hereinafter as Detournay parameters. Detournay parameters are employed to provide physical meaning that is not dominated by the impact of bit size and rotational speed.
The Detournay model for rotary drag bit drilling describes three drilling regimes referred to as phases I, II, and III. Phase I is characterized by frictional contact between formation and the bit, whereby w is insufficient for the cutters to penetrate the rock and the bit simply grinds at the rock. This phenomenon is a result of the cutting edge of the cutter having a finite sharpness characterized by the size of a flattened portion of the cutting edge known as a “wear flat.” It is also known as plowing in metal-cutting parlance. An ideally sharp bit would have no Phase I. Phase II begins once a critical weight on bit has been reached such that the rock cannot support additional bearing stress generated on the fully engaged wear flat. Any further increase in w drives the cutter into the rock and directly translates into an increase in cutting force, causing the bit to increasingly act as if perfectly sharp. Phase II is associated with productive and efficient drilling, and thus represents the target operating region. Phase II begins after a point commonly referred to as the founder point. Drilling efficiency decreases as w increases in phase III because of system dysfunction, e.g. inability to clear cuttings or drill string resonance. Drilling performance at higher weight may be degraded through any number of mechanisms including, e.g., stick-slip and bit balling.
Percussive drilling also occurs in three drilling phases for different weight-on-bit levels. Phase 1 represents a regime where WOB is insufficient to maintain good contact between the hammer and rock. ROP increases linearly until WOB reaches a critical value, Fmin, where good contact is achieved. At WOB values higher than Fmin, the ROP is relatively insensitive to changes in WOB. This is region 2. Finally, region 3 can exist when WOB is so high that the motor rotation is degraded. In this case, the ROP begins to decrease with increasing WOB. At some point, excessive WOB will stall the motor and ROP will go to zero. The ROP is relatively invariant with increasing WOB in region 2. Since torque increases with WOB, more energy is consumed with greater WOB. Therefore, in general, percussive drilling may be viewed as optimal very near the region 1-region 2 transition, i.e. where WOB=Fmin. In this area, ROP is approximately maximized while energy (MSE) is lower than for higher values of WOB.
The drilling response in Detournay's model for rotary drag bit drilling describes Phases I and II as having linear relationships between w, τ and d in three-dimensional space. Furthermore, Phase I is constrained to intersect the origin. For simplicity, in one embodiment of the invention, Phase I is characterized by a linear relationship but need not be. Thus, the disclosed model for rate independent rock-bit interaction is a piecewise continuous function in three-dimensional space with three linear segments as shown in
Because weight-on-bit is a controlled parameter, w may be defined as the independent variable. The drilling model requires two critical values to separate the three regions. w12 and w23 may be defined to denote the scaled weight at the phase III transition and phase II-III transition respectively. Equation 1 below may be used to compute the scaled torque t from the weight-on-bit w, to ensure continuity and intersection of the origin:
Depth of cut, d, is defined similarly but with different scalar parameters a.
In an embodiment a primary metric for drilling optimization is mechanical specific energy (MSE). According to the Detournay model for rotary drag bit drilling a minimum MSE occurs at the transition from phase II to III (the founder point). This transition begins when further increases in w no longer translate into pure cutting of virgin rock, and drilling proceeds in a less efficient manner (due, for example, to regrinding of cuttings, poor energy transfer, etc.). Equation 2 below determines MSE utilizing the Detournay parameters:
Here, R is the drill bit radius. For a non-coring bit having a full cross-section, Eq. 2 computes the sum of linear and rotational energy per volume of rock removed.
Minimization of MSE is a reliable parameter for achieving high rates of penetration and avoiding potentially deleterious effects introduced during inefficient drilling. This allows the system to enter Phase III while still increasing ROP.
MSE is also a useful parameter to minimize for high-performance percussive drilling. In percussive drilling, unlike in rotary drilling, the maximum ROP does not necessarily coincide with minimum MSE. It may be desirable to maximize ROP.
Referring next to
From step 16, control system 10 proceeds to step 18, to an autonomous operating point control, or AOPC, process, to generate the preferred setpoints 21. AOPC 18 includes an estimator block 20. Block 20 received measured drilling process inputs 12, 14 and process outcome parameters from step 16, and estimates the Detournay parameters associated with the current rock type, as discussed in further detail below. These parameters are then compared with a database, or setpoint lookup 22. Based on setpoint lookup 22, predetermined appropriate setpoints are generated and transmitted to a supervisory controller 24 (labeled “change detection/local optimal search”). The Detournay parameters are also transmitted from step 20, to supervisory controller 24. Supervisory controller 24 performs two functions. First, supervisory controller 24 determines whether a change in outcome parameters 16 have occurred to indicate that a new material has been encountered. If there is no significant change, then the setpoint values from the database are passed through to the low-level control system. If at step 24 a change in the material being drilled is indicated, then the supervisory controller 24 triggers and executes a local search for optimal operating conditions by accessing database 22. Generally, control system 10 searches for settings that minimize MSE, but it can also maximize ROP by co-optimizing the two, or optimize other metrics. One object may be to maximize over WOB a cost function f(WOB) defined as:
f(WOB)=A*ROP(WOB)+B*1/MSE(WOB EQ. 4
where A and B are selectable weights and ROP and MSE are both functions of WOB. Maximizing this expression would allow us to “co-optimize” the two metrics. Alternatively, a second function fl(WOB) could be constructed from the inverses of the terms in f(WOB); this function would be minimized as an alternate means of co-optimizing the ROP and MSE. In one embodiment an optimization algorithm such as a Golden Section Search may be employed about a fixed interval around the AOPC setpoint. In another embodiment, control method 10 may adaptively determine an initial search interval instead of a fixed interval.
Once parameter setpoints 21 have been generated at step 24, an antistall controller 26 receives setpoints 21 to determine whether stall conditions may exist at the adjusted setpoints 21. Stall conditions can occur when transitioning from a hard material—that requires a very high WOB—to a much softer material—that cannot tolerate high WOB. Softer rock layers generate significantly higher ratios of torque to WOB than harder rock layers. In response to the changing rock layers, torque τ may exceed system operational limits under high WOB and case the drill bit to stall. To avoid stall, antistall controller 26 monitors torque τ. If τ exceeds a configurable threshold value, e.g. 80% of the drill rig limits, anti-stall controller 26 reduces the target WOB at step 14, e.g., by an amount determined by a barrier function. The barrier function is configured to respond more rapidly to material changes than AOPC system 18. Barrier function may include a time constant of several seconds to filter noise encountered in the drilling process.
Referring next to
where
The barrier function describe in Eq. 3 has an advantage by introducing no penalties until reaching the initial torque for a barrier penalty, τ0, and having a continuous first derivative below the critical torque, τc. The parameter k can be used to adjust the rate at which the barrier function increases. In
Material estimation in control system 10 may be determined by generating Detournay model parameters for each general type of rock layer that may be anticipated in the geological characteristics. When drilling rock layers, the rock type and drilling phases I, II, or III may then be classified as the Detournay model which is closest to measured data of Detournay parameters. For example, three types of rock layer material may be sandstone, concrete, and granite. Detournay parameters are specific to the drilling layer or medium, and to the configuration of the drill bit. Therefore this approach requires either experimental profiles for a specific bit configuration, online machine learning to enable the automatic development of a database from real drilling data, or extensive modeling to capture the relevant bit characteristics.
Detournay models for each of the three exemplary rock types may be fit to test data using a least squares approach. An optimization fit seven parameters: a1, a2, and a3 in the equations for both t and d, as well as w12. The parameter w23 may be selected, e.g., through a separate process as the w which provided the minimum MSE. Before computing the residuals, the data may be normalized based on a filtered maximum values for t and d over all tests.
Calculating the mathematical “distance” from the current operating point to the models may be implemented in two steps. Step one is to use the two bisecting planes of the three phases to determine which line segment is closest to the current set of Detournay parameters (estimated from measured data). Step one may be performed for each of model being tested. Once the closest segments are identified, standard computation of the distance from a point to a line is used to determine the distance to the model. Data may be normalized before distance is computed. These distances are compared and the closest model is selected as the estimated rock for the current data point. An added benefit of this approach is that phase is also predicted by the model from the first step.
Running this classifier on the training data results in about an 84% success rate in identifying sandstone, 86% success in identifying concrete, and 99% success in identifying granite. Any confusion may result from the fact that the models for sandstone and concrete are fairly close to each other in some portions of the torque, frequency and distance range. When integrated with the autonomous controller, a mode filter may be implemented on the classifier output to prevent control behavior transitions from occurring in response to noise in the classifier output. A mode filter may take the mode of the rock estimate over a predetermined interval, e.g, between 1 second to 10 seconds, and more preferably from 3 seconds to 5 seconds, although other time intervals may be applied depending on rock layer characteristics.
In one embodiment a controller for system 10 may be a PC-based supervisory control and data acquisition (SCADA) system integrated with data acquisition hardware. Process data may include WOB, torque, rotary speed, and drill head position. WOB may be calculated, e.g., from measured differential pressure across the hydraulic cylinders. Torque may be determined by measuring the input pressure to a hydraulic drive motor (not shown). Rotary speed may be determined using a rotary pulse generator on the hydraulic motor. A linear potentiometer may be used to determine a drill-string position.
In one exemplary embodiment a controller of control system 10 comprises a LabView virtual instrument (VI) integrated with MATLAB for data processing. Real-time estimation and control calculations are performed in the Labview VI, in some cases using embedded MATLAB scripts. The VI interfaces with the data acquisition hardware and displays the process variables to the operator via the display. Data may be acquired at a sampling rate of 2048 samples per second and collected in 256 sample increments. The collected data is then processed in MATLAB for analysis. Rotational speed of the drill head is controlled using voltage-controlled proportional valves which modulate the hydraulic fluid flow to the rotation motor. A pressure relief valve may be used to limit output torque. WOB may be controlled using voltage-controlled proportional valves which modulate the hydraulic cylinder pressures.
Proportional-integral (PI) or PID feedback controllers may be used to achieve low-level control to regulate rotary speed and applied WOB. Control signals transmitted from the controller direct the behavior of the hydraulic valves.
Referring next to
In the percussive autonomous drilling control embodiment of
In one exemplary embodiment of percussive autonomous drilling control system 50, only WOB is varied in real-time. When using separate power sources for hammer pressure and rotation, performance is effectively maximized when both of these parameters are maximized. WOB therefore provides the variable parameter that determines drilling success, failure, and performance.
In another exemplary embodiment, the hammer and rotary motor share a single power supply. Therefore to obtain optimal performance, the hammer pressure setpoint 54 and rotary speed are autonomously traded against each other in real-time to maximize performance. In this case, control system 50 varies all three control parameters (WOB, hammer pressure, and rotary speed) autonomously in real-time.
In one embodiment the optimization algorithm implemented in optimization block 62 may be referred to as a golden section search (GSS) algorithm. The GSS algorithm assumes that the global extrema lies within a search interval (a,b), and that the objective function is unimodal between (a,b) [28]. The search space is sequentially searched with decreasing intervals based on the golden ratio. This approach is well suited for ROP optimization because the limits (a,b) may be determined analytically using Hustulid's model of the physics of percussive drilling, which defines the bounds of the drilling Phases based on parameters of the drilling medium and the drilling process. The GSS algorithm may then be performed within this smaller interval.
The GSS may implemented in the Labview control software by National Instruments Corp. of Austin, Tex. Sampling intervals in the range of 10 to 20 seconds may be used to ensure that parameters stabilize and provide a large signal-to-noise ratio for an average ROP estimate. The average ROP may be calculated by dividing the change in depth from the beginning to the end of the calculation interval by the time interval. This substantially smooths the rate calculation. In addition, a shorter interval, or measurement interval, is used after a fixed delay. The fixed delay may be introduced to enable the WOB to converge to the setpoint, and eliminate effects of elasticity in the drill rig or test fixture components. Elasticity in the drill rig or test fixture can show up as drill depth changes when WOB is modulated. Once the search is completed, the best setting is chosen from all the settings that were sampled.
Referring next to
In addition to control schemes described above, alternate control methods may be applied with the scope of the invention and the appended claims. E.g., single parameter regulation may be used, wherein a single parameter, e.g. the drilling torque, is regulated to a predetermined fixed value regardless of the drilling medium, and the remaining control parameters, e.g. WOB modulated either to preserve the torque value, or according to a rules based model. Another optional control method may be a material estimator in which parameters characterizing the drilling medium are estimated from real-time measurements, and optimal settings for that material may be selected from a lookup table or via algorithms. Another alternate control method that may be used in the control system 10, 50, is a nonlinear adaptive control wherein a model of rock-bit interaction can be parameterized, e.g. using the Detournay construct. This mode may be applied, for example, in a plant model and adaptive control methods used to adapt to drilling parameters as they change. Another optional method for the control system 10, 50 may be reinforcement learning. Adaptive, learning or optimal controllers may either be applied to subsystems or to the entire system. In the approaches described above, low-level control may be handled separately in order to partially isolate the dynamics of the drilling rig from the drilling process. Thus adaptive or learning methods may be applied solely to the higher-level control. Alternately, the control system may apply any of the aforementioned control methods directly to the low-level control inputs, enabling direct adaptation to the full system dynamics.
Referring next to
In addition to providing a means of drilling process control,
While the exemplary embodiments illustrated in the figures and described herein are presently preferred, it should be understood that these embodiments are offered by way of example only. Accordingly, the present application is not limited to a particular embodiment, but extends to various modifications that nevertheless fall within the scope of the appended claims. The order or sequence of any processes or method steps may be varied or re-sequenced according to alternative embodiments.
The present application contemplates methods, systems and program products on any machine-readable media for accomplishing its operations. The embodiments of the present application may be implemented using an existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose or by a hardwired system.
It is important to note that the construction and arrangement of the autonomous drilling systems as shown in the various exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in the claims. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present application. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means plus function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present application.
As noted above, embodiments within the scope of the present application include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media which can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CDROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.
It should be noted that although the figures herein may show a specific order of method steps, it is understood that the order of these steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. It is understood that all such variations are within the scope of the application. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.
This invention was developed under Contract No. DENA0003525 awarded by the United States Department of Energy/National Nuclear Security Administration. The Government has certain rights in this invention.
| Number | Name | Date | Kind |
|---|---|---|---|
| 10221671 | Zhang | Mar 2019 | B1 |
| 20120085584 | Jiao | Apr 2012 | A1 |
| 20150233229 | Benson | Aug 2015 | A1 |
| 20150252664 | Astrid | Sep 2015 | A1 |
| 20160110481 | Jain | Apr 2016 | A1 |
| 20170260822 | Edbury | Sep 2017 | A1 |
| 20170335670 | Dykstra | Nov 2017 | A1 |
| 20180038215 | Badkoubeh | Feb 2018 | A1 |
| Entry |
|---|
| D.A. Glowka; Development of a Method for Predicting the Performance and Wear of PDC Drill Bits; Jun. 1987; SAND86-1745, 205 pages. |
| E. Detournay, P. Defourny; A Phenomenological Model of the Drilling Action of Drag Bits; International Journal of Rock Mechanics and Mining Sciences 29 (1); 1992; 13-23. |
| Raymond, D.W.; PDC Bits Demonstrate Benefit Over Conventional Hard-Rock Drill Bits; Geothermal Resources Council Transactions; Sep. 2001, 10 pages. |
| E. Detournay, T. Richard, M. Shepherd; Drilling Response of Drag Bits: Theory and Experiment; International Journal of Rock Mechanics & Mining Sciences; 2008; 45; 1347-1360. |
| F. E. Dupriest; Comprehensive Drill Rate Management Process to Maximize Rate of Penetration; SPE 102210; SPE Annual Technical Conference and Exhibition; Sep. 2006; San Antonio, TX, 9 pages. |
| R. Teale; The Concept of Specific Energy in Rock Drilling; International Journal of Rock Mechanics and Mining Sciences and Geomechanics; 1965; 2; 57-73. |
| Freedonia Group; Drilling Products & Services; Study #3286; http://www.freedoniagroup.com/Drilling-Products-And-Services.html; 2015. |
| A. W. Eustes III; The Evolution of Automation in Drilling; 2007 SPE Annual Technical Conference; Nov. 2007; 1-5; Anaheim, California. |
| J. Dunlop, R. Isangulov, W. Aldred, H. A. Sanchez, R.L. Flores, J. Belaskie, et. al.; Increased Rate of Penetration Through Automation; Paper IADC/SPE 139897; SPE/IADC Drilling Conference and Exhibition; 2011; Mar. 1-3; Amsterdam, The Netherlands, 11 pages. |
| F.E. Dupriest and W.L. Koederitz; Maximizing Drill Rates with Real-Time Surveillance of Mechanical Specific Energy; SPE/IADC Drilling Conference; 2005; Amsterdam, The Netherlands, Feb. 23-25, 10 pages. |
| C. D. Chapman, J. L. S. Flores, R. D. L. Perez, H. Yu; Automated Closed-loop Drilling with ROP Optimization Algorithm Significantly Reduces Drilling Time and Improves Downhole Tool Reliability; Paper IADC/SPE 151736; SPE/IADCDrilling Conference and Exhibition; 2012; Mar. 6-8; San Diego, California, 7 pages. |
| D. Sui, R. Nybo, V. Azizi; Real-time Optimization of Rate of Penetration during Drilling Operation; 2013 10th IEEE International Conference on Control and Automation; Jun. 12-14, Hangzhou, China, pp. 357-362. |
| A. T. Bourgoyne, F.S. Young; A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection; Journal of the Society of Petroleum Engineers; 1974, 371-384; vol. 14(4). |
| G. Boyadjieff, D. Murray, A. Orr, M. Porche, P. Thompson; Design Considerations and Field Performance of an Advanced Automatic Driller; Paper SPE/IADC 79827; SPE/IADC Drilling Conference; Feb. 2003; 1-11; Amsterdam, The Netherlands. |
| R. Jorden, O. Shirley; Application of Drilling Performance Data to Overpressure Detection; Paper SPE 1407; SPE Symposium on Offshore Technology and Operations; Nov. 1966; pp. 1387-1394; New Orleans, Louisiana. |
| W.A. Hustrulid and C. Fairhurst; A Theoretical and Experimental Study of the Percussive Drilling of Rock; Int. J. Rock Mech. Min. Sci.; 1971-2; 8:311-356 and 9:417-449; parts I-IV. |
| G. L. Cavanough, M. Kochanek, J.B. Cunningham and I.D. Gipps; A Self-Optimizing Control System for Hard Rock Percussive Drilling; IEEE/ASME Transactions on Mechatronics; 2008; 13(2):153-157. |
| F.B.E Depouhon; Integrated Dynamical Models of Down-the-Hole Percussive Drilling; PhD Dissertation; 2014; University of Minnesota, 205 pages. |
| M. Amjad; Control of ITH Percussive Longhole Drilling in Hard Rock; PhD Thesis; 1996; McGill University; Montreal Canada, 83 pages. |
| P. Beater; Pneumatic Drives; Springer-Verlag Berlin Heidelberg; 2007; 325 pages. |
| M. Sorli, G. Figliolini, and S. Pastorelli; Dynamic Model and Experimental Investigation of a Pneumatic Proportional Pressure Valve; IEEE/ASME Transactions on Mechatronics; 2004; 9(1):78-86. |
| G. Chowdhary, T. Yucelen, M. Muhlegg and E.N. Johnson; Concurrent Learning Adaptive Control of Linear Systems with Exponentially Convergent Bounds; Int. J. Adaptive Control and Signal Processing; 2013; 27:280-301. |
| D. Raymond, M. Mesh and S. Buerger; Dynamic Substructuring of Drillstring Computational Models for Exploration of Actuator Alternatives; Third Intl. Colloq. on Nonlinear Dynamics and Control of Deep Drilling Systems; 2014; Minneapolis, Minnesota, 14 pages. |
| D.W. Raymond, S.P. Buerger, A. Cashion, M. Mesh, W. Radigan and J.-C. Su; Active Suppression of Drilling System Vibrations for Deep Drilling; Sandia National Laboratories Report; 2015; SAND2015-9432, 280 pages. |
| S.P. Buerger, M. Mesh and D.W. Raymond; Port Function Based Modeling and Control of an Autonomously Variable Spring to Suppress Self-excited Vibrations While Drilling; American Control Conference; 2017; May 24-26; Seattle, WA; 6 pages. |
| N. Hogan; Impedance control: An Approach to Manipulation; ASME Journal of Dynamic Systems, Measurement and Control 107; 1985; 1-24. |
| N. Hogan and S. Buerger; Impedance and Interaction Control; Robotics and Automation Handbook; 2005; 19-1; CRC Press, New York. |
| J. Kiefer; Sequential Minimax Search for a Maximum; Proc. Amer. Math. Soc.; 1953; 4(3):502-506. |
| Basuray, P.K., B.K. Misra, and G.K. Lal; Transition from Ploughing to Cutting During Machining with Blunt Tools; Wear; 1997; 43; 341-349. |
| V. N. Vapnik and A. Y. Lerner; Pattern Recognition Using Generalized Portraits; Automation and Remote Control; 1963; 24(6):774-780. |
