Finally, a Low Cost and Autonomous Method of Generating Corrected Mechanical Specific Energy for Improved Decision Making, Drilling Performance, and Rock Property Characterization

Abstract

Abstract Despite decades of research focused on Mechanical Specific Energy (MSE), drilling operations still fail to operationalize MSE as the primary drilling-based performance indicator. This is partly because the availability of MSE in Electronic Drilling Recorders is usually calculated with surface measurement, which fails to isolate energy spent solely at the bit. Removing energy spent along the drill string is important because it allows operators to establish an expectation of the total energy necessary to overcome the confined compressive strength (CCS) of the rock. Once this baseline is established, an increase in MSE versus the CCS can be recognized as wasted energy and therefore, inefficient drilling. Methods to correct MSE exist but are time-consuming when done manually and may require specialized software. As a result, scaling corrected MSE can be challenging. To address these issues, this paper presents an accessible MSE at Scale workflow that provides a fully automated, physics-based drilling efficiency and design platform for full-fleet implementation. The methods included explore a wide breadth of optimization techniques that can be utilized by operational personnel and autonomous drilling rigs, thus also supporting the future of physics-based intelligent drilling systems. After discussing the data pipelines and methods of calculation, drilling results from over half a million lateral feet are presented. This analysis is separated into two workflows: Big Data MSE for design optimization and rock characterization, and real-time MSE streaming for operational guidance. A new MSE versus rate of penetration process is introduced, which will allow operators to determine a bit’s maximum formation specific performance (MFP), in one run, regardless of operating conditions. Additionally, generating high-resolution corrected MSE, per foot, provides an alternative and in-situ solution for CCS that does not require petrophysical software or log data. Lastly, a new Cumulative MSE process is introduced to offer a change of perspective in the way we understand and measure bottom hole assembly (BHA) durability. The latter half of this paper discusses derivations of the MSE equation to offer power-based metrics that are more intuitive than MSE and therefore, may be easier to adopt in field-operations. This is because BHA power, which is a function of rotational energy, presents a consistent and predictable diminishing of ROP returns. This relationship appears to behave similarly to the founder point, yet it is predictably adaptive to drilling parameters beyond solely weight on bit. Finally, this paper discusses a machine learning technique to detect real-time drilling performance that can potentially indicate failing or weakening BHA equipment.