Large-Scale Deployment of a Closed-Loop Drilling Optimization System: Implementation and Field Results

Author:

Lai Stephen W.1,Ng James1,Eddy Aaron1,Khromov Sergey1,Paslawski Dan1,van Beurden Ryan1,Olesen Lars1,Payette Gregory S.2,Spivey Benjamin J.2

Affiliation:

1. Pason Systems

2. ExxonMobil Upstream Research Company

Abstract

Abstract On a land-based rig, the driller has many responsibilities and is rarely able to adjust the main process controls, WOB and RPM, on a continuous basis. The result is sub-optimal drilling and longer spud-to-TD time than necessary. Automatic, closed-loop optimization removes this burden and results in better and more consistent drilling performance. In this paper, a closed-loop drilling optimization system is presented with results from over 1700 wells in the field. The closed-loop system builds on industry-proven advisory technology and is designed uniquely for petroleum drilling. Since formation characteristics can change rapidly with depth, a fast optimization algorithm based on input signal dithering is used to continually adjust drilling parameters to search for the highest possible ROP and lowest possible MSE. Drilling dysfunctions, such as stick-slip and formation stringers, are treated as discrete time events and mitigated using software protocols triggered by accurate detection algorithms. Also, operation of the inner loop autodriller is of critical importance and controlled using a specialized set of autodriller management protocols. Over the past 2 years, the system has been deployed on over 270 rigs for construction of over 1700 land wells in North America. One of the main deployment challenges was the need for full organizational buy-in from both rig and office personnel. Another major challenge was the need for parameter limit roadmaps which define reasonable, but non-conservative, optimization limits to be used during the drilling of each well. Drilling performance on approximately 90 wells was analyzed and compared to equivalent offset wells. The result was an average ROP improvement of 6.5% and 7.6% in the vertical and lateral sections, respectively. A case study is presented highlighting improved ROP when the optimization system was used.

Publisher

SPE

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