Optimal scheduling of field activities using constraint satisfaction problem theory
Abstract
The challenge of identifying problematic wells and planning their workover operations is
common in oil and gas fields. On top of this, the well intervention resources are seldom
easily accessible so it is crucial to target the right set of wells at the right time. Oil and
gas reservoirs are complex dynamic systems the production and injection patterns of
which can significantly affect the reservoir and well response. This represents a complex
mathematical optimisation problem where the overall life performance of the field
strongly depends on the workover planning decisions.
This work presents a reliable and effective tool that is able to screen and explore the large
search space of the potential work-overs that adds value to the reservoir management
process. The proposed solution considers the overall performance of the field throughout
a specified period while respecting all operational limitations as well as considering the
risks and costs of the interventions. The proposed workflow combines the commercial
optimiser techniques with constraint satisfaction problem optimiser to identify the
optimal workover scheduling. The schedule found is guaranteed to satisfy all predefined
field constraints. The presented results showed better performance achieved by the
proposed hybrid optimiser compared to classical gradient-free optimisation techniques
such as Genetic Algorithm in maximising the defined objective function. The suggested
workflow can greatly enhance the decisions related to field development and asset
management involved with large number of wells and with limited intervention resources.