Plug the value leak: Fix your drilling data

Transcription

1 Originally appeared in World Oil OCTOBER 215 issue, ps Posted with permission. SPECIAL FOCUS: ADVANCES IN DRILLING Plug the value leak: Fix your drilling data Today, drilling data are marred by a lack of quality assurance, with potential costly consequences for analytics and real-time decisions made from faulty data. A new system allows operators to boost efficiency through the validation of real-time or historical data, unlocking significant reductions in both non-productive time and invisible lost time. ŝŝ PRADEEPKUMAR ASHOK, Sentinel Real Time; ADRIAN AMBRUS and ERIC VAN OORT, The University of Texas at Austin; NATHAN ZENERO, Chesapeake Energy; and MICHAEL BEHOUNEK, Apache Corp. Recent studies suggest that many rig sensors are delivering inaccurate data. Image: Apache Corp. Why do drilling automation systems, real-time operating systems, and drilling analysis and design systems fail so often, or deliver less value than expected, when analogies from other industries show that these approaches have significant, clear and present value? The first and foremost answer is surprisingly simple all drilling systems rely on data that are demonstrably bad. If we could supply our tools with the necessary and sufficient quality data, our controls, analytics and engineering would be much more trustworthy, both in the office and in the field. With the combination of harsh environments, complex well geometries and shrinking economic margins (especially in the U.S. land market), a growing importance is now being placed on the accuracy of data delivered from pressure, pit level, hookload, torque, flowrate, and other equally indispensable, yet largely antiquated, rig sensors. Another equally important, but perhaps not as well understood opportunity is the Big Data movement taking root in the industry. As we attempt to apply new, complex analytical methods, we realize that data quality is now much more important. Whereas most conclusions could, previously, be reached stochastically, or through less sophisticated methods, complex drilling analytics must be physically consistent. Thus, it is important to cleanse the data a priori, and in real time, when possible and practical. Many E&P companies have begun to take a critical look at this aspect of their operation; however, they have focused largely on sensor capability and new sensor technology. What is largely missing is a method to detect sensor faults and failures in real time, and the ability to estimate the correct value of a measurement when an error occurs in their existing sensor networks. This article discusses an oil field-specific application of a proven analytical technology, used in ultra-critical U.S. Office of Naval Research systems. The collaborative initiative takes the form of the next-generation Sentinel Real-Time (RT) system, which leverages a Bayesian probabilistic network model. This provides operators with an World Oil / OCTOBER

2 Fig. 1. A holistic Bayesian network of rig sensors integrates all data to identify sensor faults, defective equipment or process dysfunctions. MR block RT 1S hole MR pump 1 MR pump 1 depth previous liner stroke 1 unit 2 unit MR HWDP unit MR drill pipe unit MR NMDC 1 MR HWDP RT 1S block height RT 1S hole depth RT 1S block height previous MR plastic viscosity MR yield point RT 1S bit depth previous RT 1S bit depth Calc string MR pump 1 efficiency MR BHA Calc drill collar Calc bit pressure drop RT 1S hook load 2 ID 1 ID 2 OD 1 OD RT 1S strokes RT 1S pump 1 liner stroke efficiency per minute 1 total strokes previous 1 MR nozzle total flow area drill collar 1 drill collar 2 MR HWDP OD MR mud HWDP MR HWDP ID drill pipe RT 1S total pump output RT 1S standpipe pressure MR bit size RT 1S flowrate out RT 1S pump 1 total strokes pressure drop MR drill pipe OD RT 1S total mud volume MR drill pipe ID RT 1S pump 2 total strokes previous RT 1S pump 2 total strokes RT 1S differential pressure RT 1S strokes per minute 2 RT 1S total mud volume previous RT 1S on bit RT 1S surface RPM RT 1S surface torque Fig. 2. After a spike in flow out (blue line) during drilling activity indicates a faulty flow out sensor, close approximation of the true flow out value (green line) can be derived from the Bayesian network T8:57:16Z Flagged as sensor fault flow out sensor T9:5:12Z Sensed flow out, % Model-derived flow out, % Flow out trustworthiness Total mud volume, bbl Pump rate, gpm , T9:13:8Z. unobtrusive methodology that high-grades real-time data streams, and identifies existing and impending sensor and/or process faults. Thus, the elevated confidence in the fidelity of real-time data sets facilitates sound decisions that foster safer, more cost-effective well delivery, with reduced instances of non-productive time (NPT) and invisible lost time (ILT). The latter two items, together, have been shown to account for up to 5% of total drilling time. Specifically, the powerful data analytics engine links all surface and subsurface sensors into a holistic, self-auditing network, wherein each sensor checks off the others to determine if they are malfunctioning, Fig. 1. Once a defect is exposed, before any inferior data are fed into the real-time event detection and data analytics software, it can be replaced with properly calibrated, predictive values, based on the model that links all the sensors together. Along with the obvious, and very important, safety and environmental safeguards (think pressure, flow and pit volume sensors used for maintaining well control), the capacity to isolate a sensor fault makes the system ideally suited for condition-based monitoring and maintenance. By providing uninterrupted oversight of rig activities, be it for the rig in its entirety or individual components, the technology provides early alerts for equipment deficiencies, allowing ample time for intervention before the failure of a key component and the resulting NPT. Moreover, due to its intrinsic redundancy and, subsequently, higher confidence in data integrity, the patent-pending system is well-positioned as an enabling technology for drilling automation. With the technology functioning as a virtual guard dog, it prevents the potentially catastrophic inclusion of inferior data in critical control algorithms. BAD DATA EPIDEMIC The industry s grappling with data volumes that have reached epic proportions has become the subject du jour these days. 1 However, comparatively less focus has been placed on validating the quality of the data sets being mined. When it comes to well construction, which continues to rely mostly on rig sensors introduced decades ago, the time-worn truism garbage-in, garbageout is particularly relevant, leaving operators little choice but to cross their fingers and hope for the best. Unfortunately, relying on best-guess scenarios can lead to realtime decisions that are based on bad data, thereby amplifying the risks of disastrous safety and economic implications. Further, the importance of high-integrity data has become even more magnified by the increasing number of centralized operating centers established to remotely monitor offshore and, more recently, onshore drilling operations. The recent acceleration in adoption of advanced surface and downhole sensors was intended to enhance safety and reduce NPT, but along with higher sampling rates that make human 36 OCTOBER 215 / WorldOil.com

3 Table 1. Average error due to bad calibration. Sensor Rig A Rig B Rig C Rig D Rig E Rig F Rotary torque, % Makeup torque, % Rotary RPM, % Pump rate, % Block position, in. 6 <.5 <.5 72 <.5 <.5 Hookload, % 11 n/a 18 n/a 12 n/a Pit volumes, % Pump pressure, % Source: Chesapeake Energy Fig. 3. Judging from the actual reading shown by the hookload sensor (blue line), the model detects a potential over-pull situation and does not confuse this as a faulty sensor reading T 13:1:49Z T 13:12:5Z T 13:13:21Z Flagged as process fault overpull T 13:14:37Z Actual hook load, klb Model derived hook load, klb Block height, ft T 13:15:53Z real-time monitoring enormously difficult, a contagion of substandard data continues to stream unabated through rig analytical software. Recent studies by Chesapeake Energy suggest that upwards of 5% of the rig sensors in service are delivering inaccurate data, with sensor reading variances of up to 5%, high or low, compared to a correct, calibrated reading. As a case-in-point, the inherent value of a technology capable of instantly recognizing improperly calibrated data and replacing it with the correct figures was reinforced by an independent operator s collection of data from eight primary surface sensors, on six rigs, in its onshore drilling fleet. What it found was average errors, from bad calibration, of up to 6% in the case of makeup torque, with only the RPM sensors shown to be consistently within the proper calibration specifications, Table 1. MODEL CONSTRUCT The concept of developing a seamless methodology, with intrinsic relational redundancies to validate drilling sensor data, was germinated within the Drilling and Rig Automation Group of the University of Texas at Austin s (UT Austin) Department of Petroleum and Geosystems Engineering. 2 The original version of the holistic data validation technology was developed and matured through funding by the U.S. Office of Naval Research, which sought technology to verify the accuracy of sensor data in critical electro-mechanical actuator systems, in submarines and ships. 3 With UT researchers exploring technology transfer opportunities in the civil, mechanical, and aerospace engineering disciplines, it was recognized quickly that the validation software used effectively in the defense sector had direct implications for improving the safety and economic performance of oil and gas drilling operations. What resulted was a behind-the-scenes model that integrates all rig sensor data with built-in variables capable of instantly identifying sensor and/or process faults, as well as malfunctioning equipment. Since all the sensors are joined in an integrated network, the cross-checking mechanism ensures all are in proper working order. Specifically, the now-transferred technology is built around a Bayesian network model of sensed parameters to facilitate easier and complete enumeration of all the relational redundancies inherent in a particular system. Processed from commonly used WITS, Modbus, and WITSML data transmission standards, all the information collected is closely related with the built-in redundancies detecting any anomalies, either with the individual sensor or with the top drive, mud pump, mud motor or other rig component. Once intertwined as a seamless network, all of the sensors are logically linked together, thus the transmission of independent, or unrelated, measurements would suggest one or more of the sensors are either not relaying the proper data, or something is amiss in the process. For instance, if mud is flowing in at a certain rate, it also should flow out at the same rate (assuming no kick or lost circulation scenario), with any deviation from that norm likely indicating a faulty flow out sensor, Fig. 2. Once the fault is identified, the Bayesian network derives a very close approximation of what the actual flowrate should be. In that same way, the network model would pick up process faults, such as kicks, lost circulation and other drilling dysfunctions, and would be able to alert the user accordingly. Consider, for instance, the case of a process fault, such as an unexpected tensile load, as indicated by the hookload sensor, which degrades the process confidence level to zero. In this particular case, the software would raise an alarm of a potential overpull condition, representative of a pipe sticking event, rather than a defective sensor, Fig. 3. Most importantly, with the steadily increasing development of control algorithms to advance automated drilling, tripping, and managed pressure drilling (MPD), which key off the assimilated data, the value of knowing if a sensed data can be trusted, cannot be understated. 4 Here, trustworthiness carries enormous, considering the repercussions should automated decisions be based on inferior data being included in the control algorithm. As such, the new validation software continually re-processes the data and checks whether they are trustworthy, or should be discarded before the data can be engineered into an automated closed-loop control routine. Furthermore, the system s modularity makes it extremely flexible in accommodating the wide variances in number and type of sensors used from rig to rig. The model architecture is such that it can be easily scalable to meet individual requirements and objectives, either for use on only the top drive, fluid circulation or other subsystems, or a single network that encompasses all of the rig s surface and subsurface sensors. While the robust system enhances the value derived from simple surface sensors without the addition of sophisticated downhole telemetry data, it is easily updated to include loggingwhile-drilling (LWD), measurement-while-drilling (MWD) and other downhole measurement tools. RIG NPT MINIMIZATION Breakdowns on the rig involving key pieces of equipment not only can jeopardize well integrity, but also, as Fereday 5 and others have noted, represent one of the primary causes of NPT. Typi- 38 OCTOBER 215 / WorldOil.com

4 cally, operators and contractors are at the mercy of planned maintenance schedules that are based on the equipment s specified operational life expectancy, workloads, and targeted operating environment. However, failure risks are compounded appreciably, when maintenance is performed in accordance with parameters that may be inaccurate or unrealistic, given ever-changing drilling conditions, unforeseen events or revised well objectives. However, the continual oversight of sensor output throughout the actual drilling process delivers a proactive, conditionbased response that goes beyond data quality by flagging rig or equipment deficiencies well before catastrophic failure occurs. Furthermore, the advanced algorithms written into the validation model consider all the possible data generated by the system to computationally triangulate how much useful life remains in a particular piece of equipment. Ultimately, these decisions eliminate failure-related NPT, or at least minimize the duration of downtime for equipment repair or replacement. Since the faults identified in different rig components also generate different belief patterns, the holistic unification of all the sensors enables immediate isolation of the problem area, which would be very difficult, if not impossible, if only a single sensor was considered, Fig. 4. Again, in the case of a flow line sensor, the network would determine, in real time, if a fault was in the sensor, the mud pump or the flow line (i.e. leakage). Consider also that if the calculated real-time efficiency of the mud pump was shown to be deviating from its nominal value, the identified efficiency decline would strongly suggest deterioration, giving personnel plenty of lead time to make fact-based decisions, on when to take the mud pump off-line for repair or replacement. Fig. 4. Since different faults produce different belief patterns, the data analytics engine goes beyond data quality to isolate specific equipment and/or process deformities Flow sensor failure belief pattern Mud pump failure belief pattern Flow line failure belief pattern Fig. 5. An example of data being cleansed by the software to enable further processing. 43 1,94 3,46 4,97 6,5 8, 43 1,94 3,46 4,97 6,5 8, 8/2/215 8/2/215 Bad data Cleansed data 8/5/215 8/5/215 Time Actual data Cleansed data 8/8/215 8/8/215 Hole depth, ft DRILLING EFFICIENCY AND OPTIMIZATION More advanced sensor arrays, employed nowadays in the more critical drilling applications, deliver multiple sensor sampling rates that are extremely difficult, if not impossible, to humanly digest for real-time decisions. Therefore, at a time when low commodity prices make operators especially keen on any improvement that can help maximize asset value, opportunities to optimize drilling efficiency can be overlooked. Until now, real-time decisions to improve efficiency fell mostly on the most experienced driller and the individual intimately familiar with a particular rig and drilling theater. Hand in hand with condition-based monitoring, the datavalidation algorithms pick up deviations that can directly impair optimal efficiency, be it with the mud motor, bit and other BHA components, or else with a drilling (process) dysfunction, such as a sensed increase in axial, lateral or torsional vibrations. By tracking and analyzing RPM, torque, -on-bit, differential pressure, and rate of penetration data, the holistic analytics engine can isolate, for example, if the mud motor is defective, or if there is a process fault, such as a dull bit. Through continual updating of industry-standard, Mechanical Specific Energy (MSE) calculations used for performance evaluations, the software provides objective assessments for real-time efficiency improvements. Along with optimizing real-time drilling efficiency, the software also provides beneficial insight to aid in field development. Generating reports for individual wells, the analytical software processes historical drilling data, which are improved further by identifying, engaging, terminating, and ultimately, replacing faulty data with cleansed values derived from the model, Fig. 5. Hence, operators can use the final report to optimize post-drilling performance analysis, amplify data quality for analytical purposes, and enhance future well planning. Specifically, such reports also provide indication of sensors that need to be scheduled for re-calibration, or even require replacement. Currently, the software is in play analyzing operator data sets, cataloging bad data, and furthering the business case for improving data quality for increased drilling efficiency and safety. REFERENCES 1. Eaton, C., Oil patch works to harness a gusher of Big Data, Houston Chronicle, May 1, Ambrus, A., P. Ashok, and E. van Oort, Drilling rig sensor data validation in the presence of realtime process variations, SPE paper MS, presented at the SPE Annual Technical Conference and Exhibition, New Orleans, La., Sept. 3-Oct. 2, Krishnamoorthy, G., P. Ashok, and D. Tesar, Simultaneous sensor and process fault detection and isolation in multiple-input-multiple-output systems, Systems Journal, IEEE, vol.9, no.2, pp. 335, 349, June Ambrus, A., P. Pournazari, P. Ashok, R. Shor, and E. van Oort, Overcoming barriers to adoption of drilling automation: Moving towards automated well manufacturing, SPE-IADC paper MS, presented at the SPE/IADC Drilling Conference and Exhibition, London, UK, March 17-19, Fereday, K.,BP, Downhole drilling problems: Drilling mysteries revealed!, presented at American Association of Professional Landmen (AAPL) Annual Meeting, San Francisco, June 13-16, OCTOBER 215 / WorldOil.com

5 PRADEEPKUMAR ASHOK is a principal in Sentinel Real Time, LLC, a Houston-based company whose purpose is to develop drilling data-related technologies for oil and gas operators, and drilling contractors. He also is a research scientist in the Rig Automation and Performance Improvement in Drilling (RAPID) group at the University of Texas at Austin. There, he leans on his research experience in the robotics industry to focus on improving data quality in the upstream petroleum space, for the purpose of advancing drilling automation throughout the well delivery lifecycle. Dr. Ashok received his PhD in mechanical engineering from the University of Texas at Austin. ADRIAN AMBRUS is a graduate research assistant in the RAPID group at the University of Texas at Austin. After earning BS and MS degrees in mechanical engineering from Drexel University, he joined the University of Texas at Austin in 212, to pursue a PhD in mechanical engineering. His research focus is on drilling rig sensor data validation, event detection and design of control algorithms for various drilling automation tasks. Mr. Ambrus is responsible for developing the core data validation engine for Sentinel RT. ERIC VAN OORT is an expert in drilling and production, who serves as a professor in the Department of Petroleum and Geosystems Engineering at the University of Texas at Austin. He also serves as chairman of the board for Houston-based GenesisRTS, a real-time systems company that empowers operators with fit-for-purpose drilling optimization expertise, processes and tools. He previously served 2 years at Shell, where he was responsible for the implementation of new technology, fit-forpurpose rig development for well manufacturing, frac spread optimization, water management, rig automation and remote operations. He holds four U.S. patents and five international patents on drilling techniques and associated best practices, drilling fluids and wellbore stability, among others. Dr. van Oort holds a PhD in chemical physics from the University of Amsterdam. NATHAN ZENERO is a LEAN, Six-Sigma certified drilling engineer at Chesapeake Energy. He is co-founder of the Operators Group on Drilling Data Quality, the SPE- DSATS Data Quality Assurance (DQA) subcommittee and the IADC QA/QC committee. He specializes in applied technology, advanced analytics, real-time drilling solutions and drilling automation systems. He has developed innovative tools in the areas of iron roughneck calibration, BOP testing and rig information management. He received his bachelor s degree from Texas A&M University. MICHAEL BEHOUNEK is a senior drilling advisor with Apache Corp. in Houston, working in World Wide Drilling, supporting global operations and engineering, and pursuing technology advancement that can be leveraged to drive improvement and increase value to Apache. His 33 years of expertise lie in drilling engineering, operations, performance improvement, and contracting in over 25 countries. Recent technology developments include three years in drilling automation, and he now leads efforts to improve performance leveraging data, including data quality, integration, transformation, analytics-visual, advanced and predictive. Mr. Behounek is a degreed mechanical engineer with a master s degree in business. Article copyright 215 by Gulf Publishing Company. All rights reserved. Printed in U.S.A. Not to be distributed in electronic or printed form, or posted on a website, without express written permission of copyright World Oil holder. / OCTOBER