Real-Time Estimation of Downhole Equivalent Circulating Density ECD Using Machine Learning and Applications

Robinson, Tim; Gomes, Dário; Hashim, Meor M. Hakeem Meor; Yusoff, M. Hazwan; Arriffin, M. Faris; Mohamad, Azlan; Ezharuddin, Tengku; Othman, Eswadi Bin

doi:10.2118/208675-ms

Day 2 Wed, March 09, 2022 2022

DOI: 10.2118/208675-ms

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Real-Time Estimation of Downhole Equivalent Circulating Density ECD Using Machine Learning and Applications

Tim Robinson¹,

Dário Gomes²,

Meor M. Hakeem Meor Hashim

et al.

Abstract: Despite many drilling technology improvements during recent years, hole cleaning remains a significant challenge. The variation of equivalent circulation density (ECD) is a symptom of borehole instability. Therefore, the ability to accurately estimate ECD is a key consideration for preventing hole cleaning problems that may lead to a stuck pipe, and well pressure management more generally. In this work, we demonstrate a Machine Learning approach to estimating downhole ECD in real-time using a de… Show more

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Cited by 6 publications

References 16 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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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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Enhancing Stuck Pipe Risk Detection in Exploration Wells Using Machine Learning Based Tools: A Gulf of Mexico Case Study

Gomes,

Jaritz,

Robinson

et al. 2024

Day 2 Wed, March 06, 2024

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We present a case study on the utilization of a machine learning (ML)-based computational tool for detecting stuck pipe risks early in live operations. The system was used in two Gulf of Mexico (GoM) wildcat exploration wells. The risk detection approach is based on a novel technology using physics-informed machine learning models to analyze real-time data and detect potential stuck pipe incidents in live operations. The ML models were pre-trained on a variety of wells from different fields. The system was designed for out-of-the-box usage, which supports operational monitoring for exploration wells without pre-training on offset well data. The methodology and the process of integrating the computational tool into live operations, and the flow of data between the tool and the drilling operation is described. Additionally, the paper delves into drilling practices that helped to prevent stuck pipe and examine specific incidents that were unavoidable. The application ran stably throughout the operations, with high uptime and few false warnings in both wells; on average, fewer than one false alert was observed per day of operations. The pre-trained models proved effective, requiring no additional training; this generalizability is an important prerequisite for utility when applied to exploration wells, where offset data may be unavailable. However, due to lack of personnel to follow up the system's outputs in real-time, the benefits were limited. The first well was drilled without stuck pipe incidents. Some sticking risk symptoms were identified during the operation, especially in a fault zone. The post-well analysis indicates that good drilling practices were enough to mitigate the risks. The drilling practices responsible for the success of the operation will be discussed. In the second well, there were stuck pipe incidents. The application provided some indications of stuck symptoms but with some limitations for how far in advance the risk could be detected. The causes of the stuck incidents, the challenges in avoiding them, and updates to the risk detection system for identifying these, will be explored. Based on the experience described in the paper, the authors will offer recommendations for optimal technology utilization both from the application's and organizational perspectives.

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Real-Time Estimation of Downhole Equivalent Circulating Density ECD Using Machine Learning and Applications

Cited by 6 publications

References 16 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

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

Enhancing Stuck Pipe Risk Detection in Exploration Wells Using Machine Learning Based Tools: A Gulf of Mexico Case Study

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