on optimal resource development
with confidence under uncertainty

Making right decisions on the subsurface resource development is challenging due to the associated uncertainty and the lack of data. Evaluation of risks of such decision is vital to manage success in sustainable development of natural resources and to make confident decisions.

Here we demonstrate a range of solving practical decision-making problems based on optimisation under uncertainty:

• AutoWell – Automated Well Monitoring and Control
  Development of new methods for automated well monitoring using pressure and rate data to detect well performance and conformance issues and pave the way to on-the-fly well testing and automated injection control.
• Reserve confidence mapping
• Value of history matched models in infill drilling decisions
• Optimisation of infill well location under geological uncertainty
• Optimisation of development planning – a real field data
• Balanced decision making between maximising the value and minimising uncertainty (risk)
• Understand where more information is needed to make decisions

Reserve confidence mapping

A large mature field case study demonstrates how to build a reserve confidence map to support infill drilling based on ensemble of history matched models.

Value of history matched models in infill drilling decisions

This study demonstrates the how history matched models provide more accurate evaluation of recovery factors for optimisation on infill drilling under geological uncertainty across he variability of the possible model scenarios. The work evaluates the effect of performing stochastic well placement optimization in models that match the observed production data with different convergence levels in two industry benchmark cases. Optimisation of a new vertical well location and a horizontal well direction showed that the well-matched models provides a range of similar field oil and water productions at the optimized truth case.

Optimisation of infill well location under geological uncertainty

Multi-objective optimisation helps to find solutions that maximise the value to provide a range of trade-offs between development targets across multiple plausible geological models. The achieve infill drilling solutions balance engineering trade-offs & risks under geological uncertainty. The probabilistic solution provides the guidance for choosing optimal decision based the appetite for risk with respect to the risk from geological uncertainty.

Optimisation of development planning – a real field data

Balanced decision making between maximising the value and minimising uncertainty (risk)

Understand where more information is needed to make decisions