Real-Time Data Integration for

Transcription

1 Real-Time Data Integration for Strategic & Operational Intelligence Michael R. Brulé, PhD, P.E. Principal, Technomation 5 th International Conference on Integrated Operations in the Petroleum Industry Trondheim, Norway September 29-30, 2009

2 Speaker Bio Michael R. Brulé, PhD, P.E. Principal, Technomation Mike Brulé is principal of Technomation, a consultancy providing research, advisory, and implementation services for E&P information management, software development, and real-time systems integration. He spent the first half of his thirty years in the energy business with Kerr-McGee and Shell working in North Sea and Gulf of Mexico field development and operations, and in alternative fuels research. Brulé currently focuses on E&P enterprise architecture, information management, integrated oilfield modeling and optimization, i workflow automation, ti operational BI, MPP data warehousing, and predictive analytics for real-time decision-making and E&P business improvement. He holds a PhD in chemical engineering and an MBA from the University of Oklahoma. Contact: mike.brule@technomation.com

3 Agenda Real-Time Data Integration for Operational Intelligence The Vision of Operational BI (Business Intelligence) : Improve Production Surveillance & Optimization (PS&O) effectiveness and efficiency by reducing time-to-decision Findings and implications of SPE Digital Energy Study Group s survey on gaps in data management and application integration ti Waterflood and workover field examples: Looking at the business impact of having data ready-to-go Technology Enablers to achieve Operational BI: E&P Data Standards Integrated Asset Modeling AI Data Mining Remarkable examples outside the oil & gas industry Conclusion

4 SPE Digital Energy Study Group Mission Oilfield Integration / Real-Time Operations / Data Management & Standards Facilitate implementation of the digital oilfield through integration of information technology, people, and processes by: Identifying opportunities for improvement and supporting the development and implementation of information technology integration solutions, standards, and best practices spanning the business, surface, and subsurface domains Regulatory / Financial / Logistics Efficient Integration Reduce Cost Facilities / Operations / Production Effective Integration Produce More Oil and Gas Drilling / Reservoir / Geoscience

5 Time-Based Operational Processes: Multi-Time-Scale & Multi-Subject A Fundamental Tenet: Decisions drive Actions that create Value Unneland, T. and Hauser, M Real-Time Asset Management: From Vision to Engagement An g Operator s Experience Paper SPE presented at the Annual Technical Conference and Exhibition, Dallas, 9 12 October. Brulé, M., Charalambous, Y., Crawford, M., and Crawley, C Reducing the Data Commute Heightens E&P Productivity JPT September.

6 Contexts of Oilfield Integration SPE subcommittees on Real-Time Operations & Oilfield Integration study the following: People integration real-time real time collaboration, 24x7, around the globe Data integration needs to be accessible to everyone, and consumable in applications. Operations Monitoring & Control integration RTOCS operations centers, 24x7 ubiquitous monitoring portals Disciplines integration The leverage of experts across disciplines like geoscience, drilling, reservoir, and production engineering Integrated Asset Modeling composite modeling and simulation, integrating more operations that comprise the business Process integration Combining work processes for greater efficiency SOA and industry standards applications applications integration, integration for interoperability Data stand out as fundamental and foundational to all the other integration contexts.

7 SPE Digital Energy Survey Summary The Data Commute is Costly to the E&P Industry Anecdote: Engineers spend half their time chasing data Statistics: 44 percent of an E&P professional s time for production surveillance is spent gathering and preparing data for analysis. Only 14 percent of the surveyed people had more than half of their time available for doing analysis. People time and many millions of dollars are lost to nonproductive data- handling activities Big Crew Change: Retiring-expert knowledge must somehow transfer to newcomer and crossover employees. Data Explosion: With each instrumentation advance, more data must be integrated, at different timescales, and across many disciplines Data management gaps impact workflow automation, process transformation, and application rationalization. With a whopping half the time lost on data chores, that s a 2x potential increase in the workforce with the same people! Sources: JPT, Sep 2009, Reducing the Data Commute Heightens E&P Productivity and SPE Bridging the Gap between Real-Time Optimization and Information-Based Technologies. SPE (SPE RTO TIG, CVX, HAL, SLB, others) SPE Continuing i Education Needs for Digital it Oil Fields. SPE XOM on PS&O. World Oil Apr 2008 (Shell-Drilling Surveillance), also Nov 2007 Improving E&P Performance with Right-Time Business Intelligence. SPE Production & Operations, Nov 2006 (XOM, HAL, CVX, COP, RDS, BP, others) Digital Energy Journal, Sep BP using connectivity to drive productivity and Using massively parallel processing databases

8 The Big Data Problem: Hundreds of silo d data and systems The Typical E&P Operator has data in many systems for many purposes. > Applications must retrieve data from these systems > Data types include Geophysics (structures, rock properties, migration patterns) Reservoir/production engineering i Platform (equipment) Manual (Worker input, e.g., data capture by pumpers) Enterprise (SAP, others) Enterprise (e.g., SAP) Operational & Equipment Data Exploration Production Manual Data Capture

9 Though popular, portal integration of data provides only Visibility, not Transparency Visual Portal for Monitoring Production KPIs Good for surveillance, but not for prediction Asset team Dashboards alert monitors asset team to a operations problem Asset Mgr Reservoir/ Prod Engr Reservoir/ Prod Engr Opns Supvsr Field personnel Field personnel See World Oil, Improving E&P Performance with Right-Time Business Intelligence, Nov2007. Asset team determines root cause, takes action, and reforecasts plan Reservoir/ Prod Engr Geology/petrophysics Opns Supvsr/Field personnel Planning analyst

10 Let s separate some of the effects and look only at the impact of making a high-quality decision faster Making the most of our science and engineering We are: NOT changing the amount or nature of the scientific or engineering work NOT changing the advances in the SPE, AAPG, SEG, AIChE, or rest of the industry in field surveillance, geoscientific study, and engineering best practices NOT exercising snap judgment or changing the quality of the decision NOT really doing anything differently than we would have otherwise We are just looking at removing the lost, non-value-adding adding time in getting to the decision, amplifying the productive time of engineers and geoscientists R i th t /l t k i t d Removing the entropy/lost work associated with the Data Commute

11 Waterflood Case History: Effect of Time to Decision Looking only at one effect: making the decision faster Spraberry Driver Unit, West Texas Production after primary recovery: 2,620 bbls/day Avg Production over waterflood period: 4,270 Minimum i waterflood study time: 8 mos. Improvement with data integrated and accessible: Time to Decision cut in half to 4 mos. 4 mos. 200,000 bbls accelerated production Worth $2 million/month at today s oil prices L.F. Elkins et al., Field Case Histories, Oil and Gas Reservoirs, SPE Reprint 4a, Dallas 1975.

12 Bigger Benefits: Visibility vs. Transparency of the E&P Business Surveillance without Optimization is a little like the frog in boiling water What about the Optimization in PS&O (Production Surveillance & Optimization)? Surveillance helps us see impending problems when they are happening or about to happen, through comparison of current trends with past trends Optimization helps us see problems before they happen, through comparison of trends with predictions from first-principles models or the new AI, i.e., statistical and stochastic analytics

13 What about the Optimization in PS&O? Opportunity Areas for Complete Oilfield Integration Accelerating and Uplifting through complete integration Cumu ulative Cash Flow 0 + Lower operating & intervention costs (time-shift acceleration) Increase ultimate recovery (optimization uplift) WO Accelerate & maximize production Earlier & Better Decisions GLO WF/ EOR - Project / Asset Lifecycle

14 Bigger Benefits: Transparency of the E&P Business Moving from the frog in boiling water to watching the asset like a hawk. Other benefits from complete integration will be realized that are much larger: With daily integrated optimization with data constantly tl fed to models, the need for a waterflood study will have been realized earlier (not just the time that the study would have taken). A study would have been initiated in, say, 2 years, instead of the traditional 3-5 yrs. Add at least another 3x plus to NPV. Surface Facilities would not have to be overdesigned to catch up on water injection. Safety and compliance issues would be greatly reduced

15 Tale of Two Forums: Theoretical vs. Empirical The Alignment Challenge: Aren t we talking about the same goals? Source:

16 Digital Energy Journal September 2009 There s room for Empirical plus Theoretical (p. 4): A growing area is data analytics services. That's an area that will really take off in the next few years. So for example, we can look at our pipeline and how measurements of wall thickness over time and how corrosion takes place - and use that to make empirical physics calculations as opposed to theoretical physics calculations. (p. 22): Look at what marquee companies outside of the oil & gas industry are doing very successfully. A multitude of opportunities exist to apply their mature approaches to Operational BI in E&P. Much work is being done in process transformation and application rationalization, but the low hanging fruit lies in improving data systems and processes. (p. 19): One of the problems which happens too often in the field is literally the lack of implementation detail available for decision-making, hence an obstacle to new technology adoption. Instead, the scenario which unfolds is that the individual describing the new technology is typically unable in lieu of a detailed implementation methodology to provide end-users an accurate picture of (a) exactly how the technology will be integrated into day-to-day operations and (b) how all the various risks to business interruption will be addressed in detail. DEJ can be downloaded from

17 Example Marquee Companies Successful at Real-Time Data Management, Predictive Analytics, & Optimization Looking Outside the Oil & Gas Industry Boeing and the US Air Force use operational business intelligence for aircraft engine design, fleet-wide aircraft maintenance, reliability and parts tracking, analysis, and 24x7 collaboration with service providers Caterpillar and Ford use them for supply chain and inventory analysis, early warning system, quality, & warranty analysis Wal-Mart, ebay, and amazon.com use them to take actions based on data combined across all enterprise subject areas, within seconds of a transaction anywhere in the world. Western Digital uses MPP database technology to mine millions of detailed data points streaming in real-time from many pieces of disk-drive manufacturing equipment and also data from their supply chain. The data are processed with a systematized and automated analytics factory approach, with in-database Weibull or gamma-distribution analytics and Western Electric rules, based on principals i of statistical ti ti process control. With each complex problem that is solved, it can be solved thousands of times again within a systematized and automated analytics factory.

18 Operational BI Maturity Stages Increas sing Query and Work kload Com mplexity Stage 1 REPORTING WHAT happened? Primarily Batch Stage 2 ANALYZING WHY did it happen? Increase in Ad Hoc Queries Stage 3 PREDICTING WHY will it happen? Analytical Modeling Grows Stage 4 OPERATIONALIZING WHAT Is Happening? Continuous Update & Time Sensitive Queries Become Important Stage 5 ACTIVE DECISIONING MAKING it happen! Event Initiated Actions Takes Hold, Closed-Loop Batch KPIs, Dashboards, Ad Hoc Complex AI Analytics, New Insights Continuous Model Update plus Real-Time Queries Complex Event-Based Triggering Increasing Data Detail, Volume, Integration & Schema Sophistication After Brobst, S. and Rarey, J. Proc. Data Warehousing (2002).

19 OPERATIONAL INTELLIGENCE Monitor and predict production trends monthly, weekly, daily, hourly, minutes, seconds, sub-second (see Digital Energy J. article) Make adjustments to smart wells to optimize production, lower costs, and increase profitability STRATEGIC INTELLIGENCE O&G Example: What was the production for a waterflood unit last month in a company s West Texas field? What caused the production decline in the waterflood unit? (e.g., World Oil article) Would a workover program for production wells result in restored production? OPERATIONALIZING WHAT IS happening now? ACTIVATING MAKE it happen! REPORTING WHAT happened? ANALYZING WHY did it happen? PREDICTING WHAT WILL happen? Integrated Production Operations yields better forecasts that drive profitability through higher asset performance levels (increased production) and lower overall costs, including a reduction in safety and compliance events. Chevron i-field staircase, Unneland & Hauser (2006)

20 Three High-Level Data Capabilities Crucial to Operational BI in E&P Multi-Time-Scale/Multi Subject Models and Mining 1. Integrating g data of different timescales: Drilling, reservoir, and production engineering would all benefit from being able to combine historical (accumulated over years), tactical (weeks to months), and high-frequency data from historians (sub- seconds to seconds to days) To cover more of the enterprise, include data from other disciplineoriented source systems, including the underlying data stores of shared-earth-modeling suites, and financial data from ERP systems such as SAP 1. Integrating data across disciplines and across multiple subject areas, i.e., Multi-Scale/Multi-Subject 2. Implementing a systematized and sustainable data factory approach to augment the industry s traditional full-physics modeling methods with statistical and stochastic methods, i.e., Models and Mining

21 Integrating Data of Different Timescales Most current production optimization suites are based on historians, which slip-stream a sampling of their data to relational databases Integration of all the data at different timescales Source: Reid et al., Holistic Production Optimization Achieved One Workflow at a Time, JPT, April > Reveals statistically important events > Provides more data for modeling of drilling and production processes > Gives geoscientists, engineers, and operations professionals consistent and integrated data for more informed decision-making > Brings the engineers closer to the operations people

22 Cross-Discipline Collaboration Source: TED stands for Technology, Entertainment and Design, although it represents much, much more. Since its inception as a conference in 1984, it has emerged as a premier site for ideas worth sharing.

23 Multidisciplinary Collaboration and Data Integration ti in E&P Traditional Contemporary Geophysics Petrophysics Geophysics Geology Geology Petrophysics Reservoir Reservoir Engineering Engineering g Reservoir Model Reservoir Model After Cosentino, L. Integrated Reservoir Studies, IFP Publications, 2001.

24 Combining PVT with seismic to locate oil Acoustic impedance depends on rock and fluids, so take the effect of the rock out of the seismic data. Cannot tell the difference between all possible oil, gas, and water mixtures. Simulation showed that o/g/w or g/w could not be flowing at the same place, at the same time (have o/w or o/g because of segregation) If AI overall >AI Oil =>it s wet. If AI overall <AI Oil => it s O/G, good completion potential! Water Gas Oil Completion Potential Wet Addy et al., Determining the Location of Remaining Oil Using Acoustic Impedance: Poza Rica Field, Mexico, SEG Workshop 2003.

25 Integrated Asset Modeling Source: Juell et al., Model-Based Integration & Optimization. SPE IE 2009, Amsterdam. IAM provides holistic view of combined operations and of the business Provides more insight than independent models and simulations > In analogy with looking at all the data in AI data mining, the IAM sum could be greater than the individual model parts > Induces more collaboration among disciplinesi Implications of SPE Digital Energy Survey on IAM: > 83 percent of people surveyed perceive gaps in application integration that impede their daily work. > Tools are format-sensitive; constantly repackaging data from one application to the next > Tools cannot be run in automatic mode; require specialists > Commercial unified tools suites exist, but many people still resort to Excel. To be successful, IAM must have: > A pluggable and extendible integration framework that is sustainable Leverage of industry-standard standard XML protocols such as Energistics WITSML and PRODML Robust connectivity for applications, ensuring reliable material and energy balancing Rank order the most important control variables Bring together disciplines to compare variables on a common ground, e.g., business and economic goals > Easily interchangeable models detaileddetailed full-physics to simple approximations, with models changeable within time domains and need for prediction reliability E.g., for transient pipeline during facility start-up, switch to OLGA; after steady-state, GAP Quick change-out from detailed compositional reservoir simulation to simple black oil model > Easier-to-use interfaces with application interconversion and interoperability nearly automatic Support of proxy models capable of representing more detailed simulation models Choice of models, not dictated by one brand or company => able to embed internal know-how

26 Important BIOs* in Surveillance & Optimization *Business Improvement Opportunities 1. Predictive Equipment Maintainability & Reliability Employs predictive methods of equipment reliability and maintainability practiced with high success in other industries 2. Drilling Surveillance & Optimization Operations and Cost transparency in drilling operations Predict costly events such as stuck-pipe incidents 3. Well & Field Asset Surveillance Operations and Cost transparency in oilfield operations Predict operational problems such as overheating of equipment 4. Production Optimization Models & Mining : full-physics models facilitated by cross-disciplines integration and in-database statistical analytics

27 Summary: A Modern Approach to Reduce Time-to-Decision and Improve E&P Operations Optimization Systematically Managing Data & Combining Models and Mining SPE survey showed Data to be a top problem for integrated operations Data management must be standardized, systematized, and automated Application interoperability would benefit from IAM systems that are flexible, pluggable, and extendible Looking at other industries, industry data standards and IT tools for BI (business intelligence) is helpful: l E&P industry standards such as Energistics WITSML/PRODML for XML Web services protocols in an SOA For scale and performance, MPP in-database analytics is a powerful capability that enables systematized and automated management of data and tactical decisionmaking. Statistical and Stochastic in-database predictive analytics can be used when there are too many effects for full-physics models Combine IAM full-physics modeling with in-database mining to augment the industry s traditional use of models and simulation. People challenges include the need for management leadership to drive data integration directives, which the leaders of companies outside the oil & gas industry have done with high success.

28 Questions? Thank You!