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Intelligent Drilling Advisory System Optimizes Performance

In the current work, a rig advisory system is developed to continually improve rate of penetration (ROP) and drilling performance. An intelligent drilling advisory system (IDAS), based on a soft closed-loop solution with multiple regression analysis—called optimum parameters global retrieval—has been established. As an effective tool for further achieving the optimal depth of cut (DOC), the control system led to satisfactory outcomes that overcame drilling challenges in Saudi Arabia and China, thereby serving as a step toward automated drilling operations.

Introduction

The IDAS was developed to inform drillers of optimal weight on bit (WOB), rotary speed, and mud-flow rate to penetrate rock and achieve higher ROP and longer bit runs. Optimized drilling parameters are calculated and updated using a soft closed-loop solution, which monitors the ROP and energy input to the bit in real time by means of a mud logger and topdrive system controller on the rig site, and calculates parameters by maintaining an optimal relationship between the ROP and energy input. If the ROP is under expectation, it automatically evaluates new conditions and updates optimal parameters by maintaining a proper relationship between the ROP and energy input. The following ­features of the IDAS improve drilling performance:

  • A combination of evaluation factors [such as DOC and drilling specific energy (DSE)] that may be advantageous to drilling performance. Simple optimization for ROP improvement may not achieve maximum potential drilling performance given current drilling technologies and geology, and the consideration of ROP over all other factors generally leads to serious bit wear.
  • The system and solutions are capable of recommending operational changes during drilling operations while the limiter occurs (referred to as a “founder point”). The limiter identification uses probability distributions to determine whether the incoming data stream falls outside of a specified estimated probability-distribution-significance level. An outlier in comparison with the probability distribution space may indicate a change in drilling conditions, allowing the driller to detect environmental changes periodically during drilling.
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19269, “Optimization of Drilling Performance Based on an Intelligent Drilling Advisory System,” by Mahmoud Abughaban, SPE, and Amjad Alshaarawi, SPE, Saudi Aramco, and Cui Meng, Guodong Ji, and Weihong Guo, CNPC, prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission.
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Intelligent Drilling Advisory System Optimizes Performance

01 February 2020

Volume: 72 | Issue: 2

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