The Intelligent Oil Field and Advanced Production Automation The. Challenge

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1 The Intelligent Oil Field and Advanced Production Automation The However, as the old saying goes Everybody loses when oil and gas wells aren t producing. Challenge

2 Big Oil,,,tough it out or Business Model Reboot? Oil and gas companies traditionally approached exploration by drilling one s way to profitability, instead of focusing on efficiency. The amount of lost production from downtime typically amount to 4%, hence total savings on a major facility can amount to millions of dollars,. It s cutting costs, it s getting more for every dollar you spend, it s getting more from each well and getting it out faster Automation is fundamental to optimize processes, especially in the current low oil prices environment,. Particularly, in this climate, operators are pursuing the kinds of automation strategies that can aggressively eliminate nonproductive time and unnecessary costs from an increasingly lean system Low oil prices require low cost solutions,

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4 50 $ bbl 0 $ 100 $ REDUCE OPERATING COSTS PRICE EFF Proven Reserves MINIMIZE DOWNTIME SAFETY & REGULATORY COMPLIANCE

5 HIGHLY COMPLICATED

6 Here s the Challenge Reducing costs, increasing safety! Leveraging the rich stream of data coming from today s oilfields can help companies save millions of dollars. Intelligent oilfield solutions can increase production and cut costs while reducing the risks for both the company and its employees. In fact, we ll soon see oilfields that monitor themselves and can be run by a virtual team of experts based anywhere in the world. That s the promise of the intelligent/smart oilfield.

7 WHAT S THE NEW NORM? Total miles driven by all staff in a large oil field spread over hundreds of miles.=10 million last year. If I drill 2 thousand more wells = millions more miles must I drive? 1. How can technology help me? 2. What is the best technology? 3. How much will this cost?

8 Let s have a Look at Where We are Today The increase in automation and density of the sensors and field equipment does reduce levels of maintenance. A typical large operational field/reservoir can create up to a Petabyte of data per day. Owner /operators are looking for solutions that can address efficiency, reliability, downtime and manpower issues. Advanced Automation, Wireless Technology and Digitalization can increase efficiency by 3-5 %.

9 8 wells x 1,500 bbls/day=12,000 What is at stake? bbls/day /day@ $/bbl = $600,000 /day X 365 days = $219,000,000 year The Cost of an Outage Due to Automation/ Network, Communication System Power Failure Based on the quantity of Production Wells is very costly! DOWNTIME VERSUS EFFICIENCY

10 Technology Changes in the new Shale plays in North America Eco Pad based Flow Station > 8 Wells

11 Key Automation Technology Trends Affecting the Oil & Gas Industry Intelligent Devices The solution: device drivers (FDT/DTM) Fieldbus Wireless Technology Asset Management Remote Monitoring and Control Remote Diagnostics Remote Maintenance A Fully Integrated Solution

12 Automating Well Site Shutdown/ Startup Monitoring and Control Systems Typical Wellheads Design Summary and Considerations to be taken for Typical Wireless Wellhead Systems Today we have the capability of remotely shutting in a well or group of wells using remote wireless technology for abnormal operation and critical situations. Automation does not only allow to remotely control wells it allows for the continuous flow of information coming in from those wells. We can now use this real time information to identify problems as they occur.

13 Wireless Capability at the Padsite enhances Real Time Surveillance and Control Diagnostic and Optimization Alarms, Trip points and Notifications Operation Support: Direct Communication with field operations Remote Control Performance Tracking and Reporting

14 The Digital Oil Field Of Today GSM/GPRS Network Multiple Wireless Field Networks Wireless Field Networks connected via GSM/GPRS to Central Dispatch

15 Data Flow across the Global Enterprise MAXIMIZE $ Corporate Planning and Scheduling Petabyte / Exabyte Global Information Systems More Data! More Sensors! More Connections!

16 Technology for the Future 2017 and Beyond The Digital Oilfield Already Exists. IIOT, Industrial 4.0 We have been connecting sensors for a long time now. The Intelligent/Smart Field is in the Prototype Planning Stage.

17 TODAY SMART INTELLIGENT EXPERTISE

18 s Data THE KNOWLEDGE AGE s Information + Structure This technology prevents the KNOWLEDGE that a company has paid a lot for, and which differentiates a company from its competitors from being lost when people leave the organization for whatever reason. 21 st Century KNOWLEDGE

19 INTELLIGENT (SMART) OILFIELD The Intelligent (Smart) Oilfield integrates people, work processes, systems, and technology to drive quicker, smarter decisions, people efficiency, production optimization, and risk management. The Intelligent (Smart) Oilfield Requires Data Knowledge Expertise Expertise Artificial Intelligence /AI Big Data

20 What has changed? What will change? Big data at the end, is just data Watson can process 500 gigabytes, the equivalent of a million books, per second BIG DATA + BIG POWER =

21 BIG DATA + BIG POWER + KNOWLEDGE = Artificial Intelligence ( AI ) Marvin Minsky of MIT,one of the pioneers of AI,defined artificial intelligence as the science of making computers do things that would require intelligence if done by people.

22 Experience + Knowledge x Time = EXPERTISE The EXPERTS One who has made all the mistakes which can be made in a very specific field.

23 Knowledge is Expertise Data Information Data Information Knowledge Knowledge comes from very many sources

24 Building Expert Systems Domain Expert Knowledge Engineer End User Explanations Knowledge Base Rules / Debugging Inference Engine Explanations / Control User Interface Database Workspace Data/Answers 25

25 Computer Reasoning Human Reasoning Rule - based Reasoning Frame Concept Case -based Pattern Recognition Neural Networks Genetic Algorithms Specific Rules Categorization Heuristics Past Experience Expectations 26

26 Artificial Intelligence Today The grandiose thoughts of the 50 s of an electronic brain have been refocused on more attainable objectives. -Robotics -Machine Vision -Interpretation -Neural Nets Big Data Expertise Knowledge Real-Time Automation and Information Solutions AI Systems 27

27 PRODUCTION OPTIMIZATION PROJECT Rule Based Expert Advisor CENTRAL DATA BASES GUI DB DR WELL HEAD MODELLING, TEST TROUBLESHOOTING GAS LIFT OPTIMIZATION SYSTEM GEOMETRY DR CDMS PROCESS GUI DB DR P/L LEAK DETECTION P/L MODELS GEOMETRY 100 Production oriented Rules from Wellhead to Terminal GUI DB DR GUI DB DR PLANNING & SCHEDULING CRUDE BLENDING OM&S SYSTEM GEOMETRY DATA RECONCILLIATION MASS BALANCE GUI DB DR 28 TO SCADA S G2 TOOLS (NoN, GDA, GMB SYSTEM GEOMETRY WELL PROBLEM DETECT WELL NN CHARACTERIZATION ASM/PROBLEM HANDLER

28 Expert /AI System Advantages / Benefits Increased Timeliness in Decision Making Increased Productivity of Organizational Experts Improved Consistency in Decisions Improved Understanding and Explanation Improved Management of Uncertainty Formalization of Organizational Knowledge 29 Quick Problem Solving Consistent Easily replicated No cost when not used Free up staff for other tasks Train Novices Integration of several Experts

29 Problems / Limitations & Situations we need to Face The required knowledge is not always available. Common Sense is a reality Expertise is difficult to capture Limited Sensory Capability vs Humans Data Dependent --inaccurate, noisy, missing. How do we retain the accumulated expertise so that we don t start from ground level zero each cycle? How can we reduce the time to needed to train our staff to function as experts using today s technology? 30

30 Expert Systems Technology and Legal Issues Who is liable? A) The Developer who created the system? B) The Expert who supplied the knowledge? C) The User who placed too much faith in the system? D) All of the above and many others? The Correct Answer is D 31

31 The Future of Expert Systems Requires improvement which enhances the interoperability and integration between Knowledge Representation and Reasoning with Database Data Models and processing to produce a super ( Meta ) model. Requires improvement in the field of Knowledge Acquisition, Management and Representation. 32

32 Examples of where Expert System Applications have been Successful Offshore Facilities Onshore Facilities Pipelines Fault Diagnosis Process Hazards Review Emergency Response Chemical Spill Response Maintenance Scheduling FPSO Bulk Terminals 33

33 The Virtual Expert System Knowledge Database must be accessible across the Entire Enterprise 34

34 The Virtual Expert Team 35

35 Using Virtual Reality (VR ) Technology one could immerse into a totally simulated environment. Learning through experience. Reality versus Virtual Reality. 36

36 Some Things to Remember Who did it first will not be an acceptable as the first question. The future successful producer will compete on a playing field of knowledge competency and successful managers will be those who can harness the power of technology to support the complex problems that will have to be solved. Creativity and Entrepreneurism will be his Trademark.

37 THE ROBOTIZED OIL WELL: CONSCIOUS MODEL