November Blog - Our WITS Opening Night Dinner at SPE ATCE was SOLD OUT! (full text) by Michael Edwards
Our well integrity enthusiasts showed up in a big way; from the service sector, to operators to academic institutions and many others participated in a dinner networking event, keynote and lively discussion highlighting the latest on well integrity practices, regulatory policies, technologies and services. We highlighted a panel discussion to discuss how we are empowering the workforce of the future. Excellent participation by our audience.
The Well integrity discipline, expertise, life-cycle processes and risk verification methodology are becoming more sought after than ever before with the energy transition. From new wells, P&A, reentry to repurposing for CCUS, geothermal and even hydrogen storage, the need for well integrity expertise, life cycle processes, standards, and risk management all have a role to play.
Considering the industry’s large investment in building the well integrity multi-discipline … it was timely and essential for us to relook at how we are empowering our workforce of the future.
For many years the industry has invested in well integrity expertise. This has historically been built with the exceptional 25-40 years experienced engineers that had a wealth of onsite, engineering and multi-discipline experiences. Losing this capability is a high cost and risk for our corporations and industry at large. This discipline is not taught as a single discipline in our universities and rarely in most companies. Well integrity engineers solve a wide diversity of problems, and with new digital tools have the ability to leverage historical data to train faster and improve decision making over a shorter period of time.
Our dinner keynote, Alexander Spuskanyuk leads the Well Integrity discipline for ExxonMobil and discussed their approach to well integrity through a well life cycle. He also discussed how they are shaping our new well integrity tools and training programs to empower the workforce of the future.
A special thanks to our sponsors Vallourec, Volant, GoWell and Viking Engineering.
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September Blog - The Next Generation of WI Engineers (full text) by Manish Srivastava
The future is very promising for Well Integrity Engineers. With the scope of projects increasing as we continue to progress as ‘bridging’ agents into ‘new energy’ integrity world in the energy transition. Oil and gas will continue to remain pertinent and the skills gained will transfer to key and growing industries, such as CCUS, geothermal, H2 storage, and so on for well integrity.
During the transition, skill repurposing will be required to fulfil the growing energy industry demands. Engineers will be armed with massive amounts of data that must be structured and analysed to make useful assessments.
Digitization combined with AI/ML is going to be necessary. Well integrity core skills will have to be coupled with IT skills to exploit tools such as IOT, cloud computing, and digital twin to make scalable models for efficient decision making.
It is evident that all the skills and competence required for a well-integrity domain cannot be covered by a single individual. Future WI engineers may be specialized for different life cycle stages or have a governance / regulatory role. The future is bright!
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August Blog - Digitalization and Artificial Intelligence in Well Integrity (full text) by Ainur Kaken
Over the last few years, the well integrity discipline experience has seen a massive decline, with experts retiring and leaving the industry. At the same time, digitalization and artificial intelligence (AI) have rapidly evolved to become a strategic opportunity for our discipline, allowing Well integrity experience to be gained across various disciplines between completions, reservoir engineering, with exposure in drilling, production and flow assurance.
Digital tools today are a major opportunity for our well integrity discipline and are increasing the pace of learning and exposure to well integrity issues that would otherwise not be available without years of well onsite experience. Digitalization of historical log data supports the growing use of latest AI tools.
Data collection and analytics are already widely used to optimize Well Integrity Management throughout the whole well lifecycle from Basis of Design to Abandonment. Since different disciplines and expertise are involved in WI, digitalization allows optimal use of data by all parties involved, establish common language, clear communication, and have a complete workflow of all processes that can be easily visualized and updated as required. Digitalization of the daily Well Integrity work is expected to make it more robust in human error prevention and built for sharing experience across assets and organizations.
Advanced data analytics can also support in making highly complex decisions by analyzing vast amounts of data in short periods of time, helping troubleshoot equipment or even take corrective actions in emergency events. Digital tools, whether it is Machine Learning (ML) and eventually AI tools, are targeted to improve overall decision-making process. These support tools are meant to supplement decision making, not to replace it.
Automation and AI can take over some of the Well Integrity work performed manually and repeatedly, such as retrieving, processing and reporting data.
Advanced analytics also enable the creation of digital replicas of the well (“digital twins”) that can be used to simulate and optimize well barriers design and evaluate its integrity.
To summarize, here are the main directions of Digitalization and AI development in Well Integrity:
- Robust database capturing information from well design and construction.
- Realtime monitoring and barriers test data uploaded in the Well Integrity database providing risk classification
- Logging data uploaded to cloud and ML model providing cement and corrosion evaluation
- Digital twin of well with 3D visualization capturing cement and corrosion logs along with geological / subsurface information (rock type and reservoir fluids)
- Reliability database for barriers and AI models predicting failure using ML models.
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