Successful Development and Deployment of a Global ROP Optimization Machine Learning Model

Robinson, T.; Batruny, Peter; Gomes, Dário; Hashim, Meor M Hakeem Meor; Yusoff, M. Hazwan; Arriffin, M. Faris; Mohamad, Azlan

doi:10.4043/31680-ms

Day 2 Wed, March 23, 2022 2022

DOI: 10.4043/31680-ms

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Successful Development and Deployment of a Global ROP Optimization Machine Learning Model

T. Robinson¹,

Peter Batruny

Dário Gomes³

et al.

Abstract: Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the ef… Show more

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Cited by 6 publications

References 28 publications

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Automated Detection of Rig Events from Real-Time Surface Data Using Spectral Analysis and Machine Learning

Robinson¹,

Revheim²

2023

Day 3 Thu, March 09, 2023

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The authors present a method for automated, high-fidelity detection of rig events characterized by complex temporal signals, such as downlinking, or wave-induced heave affecting floating rigs. These can adversely impact other systems utilizing relevant data streams, for example downlinking via mud pulse telemetry can interfere with detection of pressure changes that might indicate hole cleaning problems. Identifying these events using classification techniques applied to time-domain data is difficult, hence spectral (frequency domain) techniques, combined with Machine Learning (ML), were applied to solving this problem. Surface measurements from a variety of wells, fields, regions, service companies and operators were used to develop and validate the detection methods. Data was preprocessed using time-frequency analysis, and then input to discriminative classifiers to identify rig events of interest. For downlinking state detection, high recall and precision scores (both >93%) were achieved on independent holdout well data, and thus false positive rates were low. Successful detection was demonstrated on wells separate from the training data, hence the method is expected to generalize to new well operations. The detection method enhances situational awareness, and can actively support other software in improved automated decision-making by providing operational context in real-time, such as suppression of false warnings from monitoring pressure or modelled ECD for detecting signs of poor hole cleaning. These techniques are not limited to downlinking or heave detection, and can be applied more generally to scenarios with complex periodic signals.

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Automated Detection of Rig Events from Real-Time Surface Data Using Spectral Analysis and Machine Learning

Robinson¹,

Revheim²

2023

Day 3 Thu, March 09, 2023

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Field Test Results for Real-time ROP Optimization Using Machine Learning and Downhole Vibration Monitoring - A Case Study

Robertson¹,

Deans²,

Singh³

et al. 2023

Day 2 Wed, March 08, 2023

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A case study for a real-time field test of a machine learning (ML) ROP prediction and optimization algorithm and a vendor-neutral vibration monitoring system is presented for ten wells in Northeastern British Columbia, Canada. A novel auto-calibration feature adjusts the ML model in real-time to account for prediction bias. The paper is of interest to operators and service companies seeking to accelerate uptake of Artificial Intelligence (AI) by rig personnel. A ten-well campaign in three target formations was drilled from one rig in the lateral sections to test the ML ROP prediction/optimization system. During the first two laterals, the operator office engineers visited the rig to train the rig team and gain their buy-in. For the remaining wells, tests were run in real-time advisory mode. Field test objectives were to develop trust in the ML model, validate real-time vibration monitoring tool with real-time downhole vibration data, and obtain feedback from the rig and office on functionality to accelerate uptake of AI. Initially, the ML ROP model passed all success criteria in two of three formations, or four of six wells. One formation failed the ROP accuracy criterion because the predicted ROP was consistently too high, but the ML model accurately captured the variance. This led to the development of a novel automated calibration procedure that adjusts the "bias" of the machine learning ROP prediction in a manner like calibrating physics-based models (such as torque and drag hookload) using a calibration factor calculated in real-time by comparing predictions with actual ROP values. This enhancement enables meeting accuracy acceptance criteria and has opened the door for a broader application of the method in other formations and basins. To date the model has been successfully deployed for the lateral section and for bottomhole assemblies (BHAs) with a positive displacement motor. The vibration monitoring system successfully provided real-time data to the operator at the rig and office that is generally available only to the MWD provider in real time, which enables the operator to gain new insights for situational awareness and decision making. Feedback from rig personnel was very valuable and included new functionality, such as their ability to change parameter limits in the ROP system, which has been implemented and successfully used by the operator.

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Drilling in the Digital Age: Case Studies of Field Testing a Real-Time ROP Optimization System Using Machine Learning

Al-Riyami,

Revheim,

Robinson

et al. 2023

Day 1 Tue, May 23, 2023

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O&G operators seek to reduce CAPEX by reducing unit development costs. In drilling operations this is achieved by reducing flat time and bit-on-bottom time. For the last five years, we have leveraged data generated by drilling operations and machine learning advancements in drilling operations. This work is focused on field test results using a real-time global Rate of Penetration (ROP) optimization solution, reducing lost time from sub-optimal ROPs. These tests were conducted on offshore drilling operations in West Africa and Malaysia, where live recommendations provided by the optimization software were implemented by the rig crews in order to test real-world efficacy for improving ROP. The test wells included near-vertical and highly deviated sections, as well as various formations, including claystones, sandstones, limestones and siltstones. The optimization system consisted of a model for estimating ROP, and an optimizer algorithm for generating drilling parameter values that maximize expected ROP, subject to constraints. The ROP estimation model was a deep neural network, using only surface parameters as inputs, and designed to maximize generalizability to new wells. The model was used out-of-the-box, with no specific retraining for the field testing. During field-tests, increased average ROP was observed after following recommendations provided by the optimizer. Compared to offset wells, higher average ROP values were recorded. Furthermore, drilling was completed ahead of plan in both cases. In the Malaysian test well, following the software's advice yielded an increase in ROP from 10.4 to 31 m/h over a 136 m drilling interval. In the West Africa well, total depth was reached ∼24 days ahead of plan, and ∼2.4 days ahead of the expected technical limit. Importantly, the optimization system provided value in operations where auto-driller technologies were used. This work showcases field-test results and lessons learnt from using machine learning to optimize ROP in drilling operations. The final plug-and-play model improves cycle efficiency by eliminating model training before each well and allows instantaneous, real-time intervention. This deployable model is suitable to be utilized anytime, anywhere, with retraining being optional. As a result, minimizing the invisible lost time from sub-optimal ROP and reducing costs associated with on-bottom drilling for any well complexity and in any location is now part of the standard real-time operation solutions. This deployment of technology shows how further optimization of drilling time and reduction in well cost is achievable through utilization of real time data and machine learning.

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Successful Development and Deployment of a Global ROP Optimization Machine Learning Model

Cited by 6 publications

References 28 publications

Automated Detection of Rig Events from Real-Time Surface Data Using Spectral Analysis and Machine Learning

Automated Detection of Rig Events from Real-Time Surface Data Using Spectral Analysis and Machine Learning

Field Test Results for Real-time ROP Optimization Using Machine Learning and Downhole Vibration Monitoring - A Case Study

Drilling in the Digital Age: Case Studies of Field Testing a Real-Time ROP Optimization System Using Machine Learning

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