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ABOUT Drilling Analytics…
Drilling parameters analysis aimed at performance prediction and consistency. The process delves into drilling mechanics and physics, application types, drilling modes, risks and implications, drilling system types, and functionalities. These developments facilitate identification of patterns, trends, behaviors, relationships, and consequences, from offset big data. This approach ensures remediation of identified dysfunctions through appropriate parameters selections and performance trend predictions. This process drives drilling automation, which ensures project planning/execution consistency and predictable results.
APPLICATION CLASSIFICATIONS
This requires the classification of projects into application groups, based on specific conditions and characteristics. These considerations include –formation drillability, casing depths, section TVDs, interval lengths, drilling fluid type, hole size, borehole configuration – single vs. dual diameter (Bit and Reamer), well/section profiles, DLS requirements, lateral section lengths, mud weights, pore pressure, and drive systems, etc. The interpreted effects of these elements on drilling trends, parameter responses, and behaviors, is the backbone of drilling analytics. (SPE/IADC 67698 and SPE/IADC 62349)
DRILLING SYSTEM EFFECTS
Characterization and grouping of applications dictate different drilling system designs. These drilling systems, considering the requirements to achieve cycle time factors (CTFs) as ranked, have dissimilar parameter ranges and roadmaps. Consequently, drilling parameter responses and behaviors will also be different. Intended benefits of analytics are achievable when drilling system contributions to parameter behaviors are analyzed and understood. (SPE/IADC 62781 and SPE/IADC 77280)
ANTICIPATED CHALLENGES
Application groupings lead to identification of project differences. These differences have positive or negative effects on performance drilling. Project planning and execution must develop appropriate solutions and strategies that capitalize on the positives, and also address the negatives. Such considerations will influence drilling system designs, thus drilling parameter ranges, roadmaps, and behaviors. These effects must be accounted for in drilling analytics, to ensure effectiveness and consistency of developed performance predictions.
field testing
Developed anticipated responses and trends, based on application categories, drilling system effects, and expected challenges must be field tested. Outcomes from the predictive analytic models must be compared to benchmark offset performances. It is important to re-visit the outlined process, specifically the application differences to ensure that the expected effects were correctly accounted for (modifications must be made and justified, if necessary). For a given field, possibly with different applications, statistical confidence must be achieved, as a validation requirement.
calibration & consistency
Performance predictability and consistency solutions, developed through drilling analytics, are not easily extendable to other fields. This is primarily due to applications differences, drilling system effects, and associated differences in parameter requirements, trends and behaviors. However, the processes that drive the solutions are applicable, and must be deployed with the right data and steps.
