Knowledge Management Application In Petroleum Industry


Published on


Published in: Business
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Knowledge Management Application In Petroleum Industry

  1. 1. DON’T JUST CAPTURE KNOWLEDGE : <br /> PUT IT INTO WORK<br />KNOWLEDGE MANAGEMENTAPPLICATION IN PETROLEUM INDUSTRY<br />By-<br />Saurabh Jain (31)<br /><br />University of petroleum & energy studies<br />
  2. 2. Knowledge Management<br />"We learned that we could use knowledge to drive learning and improvement in our company. We emphasize shopping for knowledge outside our organization rather than trying to invent everything ourselves. Every day that a better idea goes unused is a lost opportunity. We have to share more, and we have to share faster." - Ken Derr, Chevron <br /> "All companies face a common challenge: using knowledge more effectively than their competitors do." - John Browne, BP Amoco <br />"We must become experts in capturing knowledge, integrating and preserving it, and then making what has been learned quickly and easily available to anyone who will be involved in the next business decision." - D.E. Baird, Schlumberger <br />Its all about how to use knowledge to generate results.<br />
  3. 3. Four Practical Steps <br />Create a setting for sharing knowledge<br />Eliminate Communication filters<br />Prioritize the tasks.<br />Keep Time Budgets.<br />HBR Newsletter – by Diane McFerrins Peters <br />
  4. 4. Knowledge Management System<br />A database system that allows mangers and employees to share the right information in a timely and efficient fashion.<br />An organizational language subsystem that allow individual to understand the meaning of the things.<br />A networking subsystem that allows individuals to retrieve and acquire information and knowledge from sources both internal and external to the firm.<br />The transfer subsystem where systematic knowledge is either directly transferred between individuals or new knowledge is created by the unique combination of information with individual’s experience base.<br />
  5. 5. Knowledge Management System (2)<br />Process of Knowledge creation & innovation<br />
  6. 6. Introducing T-Shaped Managers : Knowledge Management’s Next Generation<br />The knowledge economy demands a new kind of executive, one who freely shares ideas and expertise across the company while remaining fiercely committed to business unit performance.<br />T-shaped managers executives who share ideas and expertise across the company (the horizontal part of the “T”) while also focusing on their own unit performance (the vertical part of the“T”).<br />The world’s most profitable oil company, BPAmoco, has over 100,000 employees and 50 separate business units operating in 100 countries. Surprisingly, its unit managers are equally committed to both their company’s overall success and their own units’ performance.<br />
  7. 7.
  8. 8. HUMAN PORTALS AT WORK<br />From: Ian French<br />To: Nigel Wallace<br />Subject: RE Lightning Protection<br />Les Owen, who works for BP Pipelines in Alaska, has been asked by Larry Watson, an engineer at the pipeline unit in Siberia, about recommended practices for lightning protection. Could you respond with any thoughts? They’re likely to be particularly interested in equipment and the associated electronics needed for protecting pipeline pumping facilities.<br />From: Larry Watson<br />To: Les Owen<br />Subject: Lightning Protection<br />Do you know anyone well versed in lightning protection equipment and practices? We’ve had a lot of problems recentlywith lightning strikes damaging our pumping facilities.<br />1<br />2<br />3<br />From: Les Owen<br />To: Ian French<br />Subject: RE Lightning Protection<br />Hi, I thought you might be able to help with the attached request for information, given your past experience in Larry’s part of the world and with lightning related problems<br />From: Nigel Wallace<br />To: Ian French<br />Subject: RE Lightning Protection<br />There are many claims for exotic lightning protection systems. Approach the issue with good earthing and earth-bonding practice and the protection of particularly vulnerable components with surge diverter devices. I attach a copy of some useful presentation slides from the BP Electric Network forum in Windsor in November. I’m always happy to provide help in the area of lightning protection.<br />From: Les Owen<br />To: Larry Watson<br />Subject: RE Lightning protection<br />Received the following reply on lightning. I suggest you contact Nigel and Ian directly, who are both in Houston, if you have questions.<br />5<br />4<br />
  9. 9. BP cultivates what we call “human portals,” a particular type of Tshaped Manager who helps people identify third parties in organisations that can provide needed information.<br /> Les Owen, a BP engineer in Alaska, is such a manager; like those of his counterparts across the company, his role is informal and in addition to his regular business unit responsibilities. Recently, Owen put a BP engineer seeking information about protecting pipeline facilities from lightning strikes in touch with two engineers elsewhere in the company who were able to help. <br />The exchange—reproduced here in an edited version in which names, locations, and commercially sensitive facts have been changed—is noteworthy not because of its extraordinary results but rather because it’s typical of the way Owen and many people like him throughout BP regularly serve as information matchmakers.<br />
  10. 10. TOWARDS INTELLIGENT SYSTEMS<br /><ul><li>Terms Involved.
  11. 11. Real Live Exanples</li></li></ul><li>Data Mining<br />Data mining is the process of sorting through large amounts of data and picking out relevant information. <br />It is normally used by large corporations employing Business Intelligence integrated with an ERP system to help make managerial decisions based on the patterns and forecasts generated from the data collected.<br />It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases."<br />
  12. 12. Soft Computing<br />Soft computing refers to a collection of computational techniques in computer science, machine learning and some engineering disciplines, which study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. <br />Soft Computing uses soft techniques contrasting it with classical artificial intelligence hard computing techniques<br />
  13. 13. Artificial Intelligence <br />The science and engineering of making intelligent machines. Subfields of AI are organized as the application of particular tools and around long standing theoretical differences of opinion. <br />The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. <br />AI techniques include<br /> Artificial Neural Networks<br /> Fuzzy Logics<br />Clustering Techniques<br />
  15. 15. Some of the example where AI tools already demonstrated benefits to petroleum industry<br />Permeability and Porosity prediction<br /> Characterization of gas reservoirs<br /> Rock mechanics properties<br /> Optimize drilling operation and hydraulic fracture designs<br /> History matching and Gas Storage Management<br /> Formation lithology Identification from Well logs<br /> Porosity Image from Well Logs,<br /> Identification of high porosity reservoir sands from 3D-seismic<br />attributes,<br /> Well Drilling and Petroleum Production<br /> Intelligent System for Start-up of a Petroleum Offshore Platform<br /> Estimate Monthly Production and Production Performance<br />Monitoring Workflow<br />
  16. 16. Reservoir Characterization of the Cotton Valley Formation, <br />East Texas<br />
  17. 17. Intelligent Systems at East Texas<br />The Cotton Valley formation in east Texas is known for its heterogeneity and the fact that well logs and reservoir characteristics cannot be correlated from well to well.<br /> In a recent study, hybrid intelligent systems were used to characterize the Cotton Valley formation by developing synthetic magnetic-resonance-imaging (MRI) logs from conventional logs.<br /> This technique is capable of providing a better image of reservoir-property (effective porosity, fluid saturation, and permeability) distribution and more-realistic reserves estimation at a much lower cost.<br />
  18. 18. The study area included 26 wells. MRI logs were available from only six wells, while the other 20 wells had conventional logs but no MRI logs.<br />The idea is to use the six wells that have MRI logs and develop a series of intelligent models for Cotton Valley’s effective porosity, fluid saturation, and permeability. <br />The inputs to the model would be well location and conventional logs (such as gamma ray, SP, induction, and density).<br />The intelligent model for this study was developed with five of the wells, MR-2 through MR-6. The MRI logs from Well MR-1 were used as blind well data to validate the applicability of the intelligent model to other wells in the field.<br />
  19. 19. Fig. 8 shows the actual and virtual MRI logs (MPHI—effectiveporosity, and MBVI—irreducible water saturation) for Well MR-1.<br />
  20. 20. Results and Inferences<br />The logs shown in Fig. 8 were used to estimate reserves for this formation.<br />Using the virtual MRI logs, the estimated reserves were calculated to be 138,630 Mscf/acre; while using the actual MRI logs, the calculated reserves estimates were 139,324 Mscf/acre for the 400 ft of pay in this well. The difference between the two reserves estimates is approximately 0.5%.<br />The small difference in the calculated reserves estimates based on virtualand actual MRI logs, respectively, demonstrates that operators can use this methodology effectively to reach reserves estimates with much greater accuracy at a fraction of the cost. <br />This will allow operators to make better reserves-management and operational decisions.<br />
  21. 21. Surface-Facility Modeling<br />Prudhoe Bay<br />
  22. 22. Prudhoe Bay<br />Prudhoe Bay has approximately 800 producing wells flowing to eight remote, three-phase separation facilities (flow stations and gathering centers). High-pressure gas is discharged from these facilities into a cross-country pipeline system flowing to a central compression<br />plant. <br />Fig. 1 illustrates the gas-transit network between the separation facilities and the inlet to the compression plant.<br />
  23. 23. Ambient temperature has a dominant effect on compressor efficiencyand, hence, total gas-handling capacity and subsequent oilproduction.<br />
  24. 24. Fig. 3 is a curve fit of total shipped-gas rate to the compressionplant vs. ambient temperature for 2001. A significant reduction in gas-handling capacity is observed at ambient temperatures above 0°F<br />
  25. 25. Individual well gas/oil ratio (GOR) ranges between 800 and 35,000 scf/STB, with the lower-GOR wells in the water flood area of the field and higher-GOR wells in the gravity-drainage area. <br />Gas-compression capacity is the major bottleneck to production at Prudhoe Bay, and, typically, field oil rate will be maximized by preferentially producing the lowest-GOR wells.<br />As the ambient temperature increases from 0 to 40°F, the maximum (or “marginal”) GOR in the field decreases from approximately 35,000 to 28,000 scf/STB. <br />A temperature swing from 0 to 40°F in 1 day equates to an approximate oil volume reduction of 40,000 bbl, or 1,000BOPD/°F rise in temperature.<br />
  26. 26. The ability to optimize the facilities in response to ambient-temperature swings, compressor failures, or planned maintenance is a major business driver for this project.<br />Proactive management of gas production also reduces unnecessary emissions.<br />As part of a two-stage process to maximize total oil rate under a variety of field conditions, it first is necessary to understand the relationship between the inlet gas rate and pressure at the central compression plant and the gas rates and discharge pressures into the gas-transit pipeline system at each of the separation facilities. <br />Therefore, the first stage of this study was to build an intelligent model that is capable of accurately predicting the state of this dynamic and complex system on a real-time basis.<br />
  27. 27. Fig. 4 shows the accuracy of the predictive model that was built forthe pressure at the central compression plant. Similar models weredeveloped for rate and pressure of all the involved separation facilities.<br />
  28. 28. A state-of-the-art genetic-algorithm-based optimization tool is built on the basis of neural-network models to optimize the oil rate. <br />The goal of the optimization tool is to determine the gas-discharge rates and pressures at each separation facility that will maximize field oil rate at a given ambient temperature, using curves of oil vs. gas at each facility.<br />
  29. 29. Some typical results of fuzzy c-mean clustering analysis are shown in Fig. 5.<br />
  30. 30. The model developed for the separation facility can predict the rate at FS1 as a function of all the parameters that directly influence its behavior. Fig. 6 shows the behavior of the rate at FS1 and FS1A as a function of temperature.<br />
  31. 31. Final Results<br />Similar models were developed and analyses were performed for each of the components of the facility as shown in Fig. 1.<br />When applied together, they provide an accurate picture of the system’s dynamics. Gas-capacity constraints start to affect oil production at approximately 0°F, with increasing effect as the temperature increases<br />The estimated benefit of this tool for optimizing oil rate during temperature swings and equipment maintenance is 1,000 to 2,000 BOPD for 75% of year<br />
  32. 32. Conclusion<br />The major task for the petroleum professional is to identify the type of problems that are going to benefit the most from artificial intelligence. An integrated intelligent system, like any other technology, is not going to be the panacea of our industry, but it will play an important role in moving it into the frontiers of information technology. Our industry still awaits the commercialization of software applications that can bring the power of integrated intelligent systems into the mainstream of the oil and gas profession. Implementation of integrated intelligent systems in our daily problem-solving efforts is only a matter time. Companies that recognize the importance of investing in this technology now will be the vanguard that will reap its benefits sooner than others.<br />
  33. 33. THANK YOU<br />References<br /><ul><li>HBR Articles</li></ul>1 K.M Philosophy, Processes & Pitfalls<br />2 Four Practical steps in K.M.<br />3 Introducing T Shaped Managers<br /><ul><li>Recent developments in application of artificial intelligence in petroleum engineering - Shahab D. Mohaghegh, West Virginia U. and Intelligent Solutions Inc.</li>