This paper describes a case application of a software solution enabling prescriptive optimization of well delivery, using an industry-proven physics-informed machine-learning (ML) approach that requires no local training, for predictive identification and characterization of pending well-construction risks in a highly challenging, high-consequence operating environment. The solution is agnostic to geology, rig, bit, bottomhole assembly (BHA), or fluid, and requires only surface drilling parameters and trajectory data to provide advisory service for stuck-pipe avoidance, rate-of-penetration (ROP) optimization, and vibration monitoring.
Methodology and Processes
To leverage the advantages that an ML approach can bring to a complex system as a whole, it is advisable to strictly isolate specific concurrent operations and related challenges within that system and apply ML modeling to each individually. The output from each model, agent, or subsystem may be used as input for population of another alongside raw measured data, calculated engineering data, and traditional physics-based modeling.
This deconstructed approach is considerably more sophisticated than replicating the process of an exhaustive, pure physics-based model in ML form, and also brings practical benefits. The number of determinant parameters per agent is reduced substantially, meaning that each agent becomes agnostic to the idiosyncrasies of local external causal factors.
