Monday, August 09
The session will explore an open and transparent sharing on the personal view of the SPE Presidents about the contribution of these technologies to our industry. Furthermore, it will address the role of SPEI organization and its SPE Presidents and Board of Directors be to promote a realistic application of these technologies to our industry.
This session will present recent advances applying MLS algorithms in reservoir characterization and modeling. Presentations will focus on areas of data integration including fibre well data, micro-seismic, petrophysics, core, and field diagnostics such as pressure fall-off to develop detailed static models of rock, fluid, and mechanical properties.
The session explores real world applications and methods in predicting well performance. Can data driven analytics go beyond automated history matching and pattern recognition? Case studies include the integration of production, geology and completions with discussions on successes and failures.
Without question, the analytics solutions are increasingly pervasive and sophisticated, but the data, uncertainties and fundamentals of the petroleum industry make the application of analytics distinct in the petroleum industry. This session will begin a presentation followed by an open discussion of utilizing AI/ML in the petroleum industry.
Tuesday, August 10
In this session, industry technical executives will share current states and challenges of progressing advanced analytics, cultural changes, and cross-discipline applications, giving their views on current battle lines and the next steps to address. Presentations will be from a range of companies followed by a panel discussion with the presenters.
In this session, industry experts share their experiences in analyzing filed data with AI and ML to undestand complex patterns and build predictive models which are used to optimize drilling, completion and field development operations.
In this session, the discussions will be on managing the human factor issues related to reducing demand on the human using automation and also the expectation of increased performance with the advent of digital information technologists.