A Real-Time Probabilistic Slide Drilling Dysfunction Advisory to Assist Remote Directional Drilling Operations

Current slide drilling practices rely heavily on the intuition of the directional drillers to identify and correct drilling dysfunctions. Monitoring numerous dysfunctions simultaneously requires more complex analysis than can be done manually in real-time. There is also currently a big shift towards remote directional drilling. And as such, there is the need for a tool that can, in real-time, diagnose slide drilling dysfunctions accurately and provide advisory to both the remote directional drillers and rig crew. This paper proposes a method for a real-time slide drilling advisory system consisting of a probabilistic model which computes the likelihood that various slide drilling dysfunctions are occurring and an algorithm that determines what corrective action, if any, should be taken as a result. The dysfunction types monitored include buckling, high friction, poor toolface control, stick slip, and bit bounce. The model employs a Bayesian network which uses evidence derived from transient drilling data trends to infer the probability that any of the five considered dysfunctions are taking place. Data trends known to correlate with each dysfunction type are considered simultaneously to ensure that all dysfunction types are monitored continuously. As dysfunction probabilities are calculated, the algorithm cross references them with current drilling parameters and contextual data to determine necessary corrective actions. Corrective actions are output in the form of simple drilling parameter changes shown on a customizable graphical display. The dysfunction beliefs calculated were validated using historical data gathered from North America land drilling operations. For high friction and poor toolface control, known instances of dysfunction were identified using information in drilling logs and expert opinion and used for validation. The validation process resulted in a further refinement of the model. The proposed model along with graphical advisory displays were deployed on rigs in several North American land well drilling operations, as well as in the remote directional drilling center. While there is a lot of prior work that enables identification of rotary drilling dysfunctions in real-time, this is the first method that diagnoses slide drilling dysfunctions in real-time. The approach combines physics based models with a Bayesian network to improve accuracy and robustness in dysfunction detection. Additionally, it considers both real-time drilling data as well as drilling data from the past when diagnosing dysfunctions and facilitates remote directional drilling.