scholarly journals An integrated prediction and optimization method for drilling rate of penetration

2021 ◽  
Vol 2030 (1) ◽  
pp. 012012
Author(s):  
Zhong Li ◽  
Yi Wu ◽  
Zhaoyu Pang ◽  
Jiaxuan Gao ◽  
Jie Cao
Keyword(s):  
Optimization Method ◽  
Drilling Rate ◽  
Rate Of Penetration

Micro-Testing While Drilling for Rate of Penetration Optimization: Experiments and Simulations

2021 ◽  
pp. 1-49
Author(s):  
Magnus Nystad ◽  
Bernt Aadnoy ◽  
Alexey Pavlov
Keyword(s):  
Optimization Method ◽  
Extremum Seeking ◽  
Constraint Handling ◽  
Drilling Process ◽  
Test Results ◽  
Rate Of Penetration ◽  
Drilling Rig ◽  
Model Free ◽  
Drilling Parameters ◽  
Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking (ES) is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. The ES algorithm is a model-free method which gathers information about the current downhole conditions by automatically performing small tests with the WOB and executing optimization actions based on the test results. In this paper, this optimization method is augmented with a combination of a predictive and a reactive constraint handling technique to adhere to operational limitations. These methods of constraint handling within ES application to drilling are demonstrated for a maximal limit imposed on the surface torque, but the methods are generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested with experiments on a downscaled drilling rig and simulations on a high-fidelity drilling simulator of a full-scale drilling operation. The experiments and simulations show the method's ability to steer the system to the optimum and to handle constraints and noisy data, resulting in safe and efficient drilling at high ROP.

Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

2019 ◽  
Vol 183 ◽  
pp. 106332 ◽  
Author(s):  
Luís Felipe F.M. Barbosa ◽  
Andreas Nascimento ◽  
Mauro Hugo Mathias ◽  
João Andrade de Carvalho
Keyword(s):  
Machine Learning ◽  
Drilling Rate ◽  
Learning Methods ◽  
Rate Of Penetration ◽  
Machine Learning Methods

scholarly journals Micro-Testing While Drilling for Rate of Penetration Optimization

2020 ◽  
Author(s):  
Magnus Nystad ◽  
Alexey Pavlov
Keyword(s):  
Noisy Data ◽  
Optimization Method ◽  
Extremum Seeking ◽  
Data Driven ◽  
High Fidelity ◽  
Drilling Process ◽  
Optimization Scheme ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. To avoid violation of constraints, the algorithm is adjusted with a combination of a predictive and a reactive approach. This method of constraint handling is demonstrated for a maximal limit imposed on the surface torque, but the method is generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested on a high-fidelity drilling simulator. The simulated scenarios show the method’s ability to steer the system to the optimum and to handle constraints and noisy data.

scholarly journals A solution for funding the development of technology in oilwell drilling

2020 ◽  
Author(s):  
Dalmo S. Amorim ◽  
Otto L.A. Santos ◽  
Ricardo C. Azevedo ◽  
Ana Carolina Chieregati
Keyword(s):  
New Products ◽  
Geological Environment ◽  
Drilling Rate ◽  
World Record ◽  
Rate Of Penetration ◽  
Optimization Program ◽  
Reliable Solution

Abstract This article proposes a novel methodology to solve an existing gap in benchmark definition by the adoption of statistically defined benchmarks as references to test products or technical procedures. In a win–win partnership, remuneration is made upon realistic bases of comparison being proportional to existing risks. However, establishing values for benchmarks is rarely unanimous if asked to different persons involved in drilling analysis. Conventional benchmarking, which enhances few results and leaves aside poor operational performances, produces references that do not properly represent the geological environment. Nonetheless, when testing new products, it serves as reference to remunerate suppliers. The review of an optimization program, which resulted in a world record of drilling rate of penetration, reveals the financial magnitude of the savings produced, proposing the method discussed as a reliable solution to the development of technology.

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