A Digital Platform to Overcome Challenges when Analyzing Real-Time Drilling Performance with Surface Rig Parameters

Abstract The opportunity to improve drilling performance can be capitalized on, by timely and accurate representation of the physics applied to the bit while drilling. The mechanical specific energy (MSE) has usually been used to recognize potential drilling dysfunctions, unfortunately, without direct downhole measurements, this indicator is often poorly estimated. Real-time measurements such as weight-on-bit (WOB), torque-on-bit (TOB) and motor differential pressure, among others, can be obtained downhole and transmitted to surface while drilling with specialized tools, and when high data density is required, these tools are deployed with wired pipe. The cost associated with these technologies are hard to justify for the vast majority of development work around the world. In many cases, only surface measurements are available in real time, but these measurements are subject to several sources of bias, which if not properly processed, could lead to wrong interpretations. Due to these limitations, downhole drilling parameters could be better determined with algorithms. The goal for this publication is to explore the challenges faced when using drilling surface data and propose a new methodology to improve real-time identification of drilling dysfunctions. The first step of the process consists in, using surface measurements to identify and categorize at any given time, every single operation taking place at the rig. This rule-based algorithm segments the operational sequence, then takes surface measurements from several operations to automatically derive downhole drilling parameters such as WOB, TOB, and/or motor differential pressure. Finally, the MSE and other indicators such as drill strength (DS) are calculated and intuitively displayed to evaluate current drilling performance, facilitating the implementation of optimum drilling parameters. Miscalibrations of weight-on-bit and differential pressure was identified after analyzing several past wells datasets, and in most of cases, torque-on-bit was missing. By applying the algorithm, accurate recalculation of these downhole drilling parameters from surface data was performed. The results after the recalibration, showed a very good correlation with actual downhole data, when compared to available measurements captured with downhole BHA sensors. Moreover, these accurate results were then used to identify drilling dysfunctions and to recommend optimized drilling parameters for the next well. Additionally, this methodology is used in a real-time operating center, advising rigs with ongoing drilling operations. This algorithm, not only allows accurate estimation of downhole drilling parameters in real-time but, can also be applied to old datasets from offset wells. By removing human calibration errors, this methodology enables better understanding of the physics occurring at the bit. As our industry makes progress into drilling automation, this new model promotes consistency and performance.