Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells

Saghir, Fahd; Perdomo, M. E. Gonzalez; Behrenbruch, Peter

doi:10.2118/195905-ms

Cited by 3 publications

(1 citation statement)

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“…Hoday et al proposed a method based on abnormal monitoring to characterize PCP failures, maximize the information value of monitoring the operating conditions of each well, and minimize operating costs [5]. Saghir et al proposed to convert the features extracted from time series data into images, which helps to detect abnormal behavior of PCP autonomously [6]. Prosper and West proposed the use of a machine learning framework that can be used to customize each workover configuration to optimize the service life of PCP while considering the heterogeneity and life of wells [7].…”

Section: Introductionmentioning

confidence: 99%

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

et al. 2021

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Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.

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Section: Introductionmentioning

confidence: 99%

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

et al. 2021

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Application of streaming analytics for Artificial Lift systems: a human-in-the-loop approach for analysing clustered time-series data from progressive cavity pumps

Saghir

Perdomo

Behrenbruch³

2022

Neural Comput & Applic

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Assessing real-time performance of Artificial Lift Pumps is a prevalent time-series problem to tackle for natural gas operators in Eastern Australia. Multiple physics, data-driven, and hybrid approaches have been investigated to analyse or predict pump performance. However, these methods present a challenge in running compute-heavy algorithms on streaming time-series data. As there is limited research on novel approaches to tackle multivariate time-series analytics for Artificial Lift systems, this paper introduces a human-in-the-loop approach, where petroleum engineers label clustered time-series data to aid in streaming analytics. We rely on our recently developed novel approach of converting streaming time-series data into heatmap images to assist with real-time pump performance analytics. During this study, we were able to automate the labelling of streaming time-series data, which helped petroleum and well surveillance engineers better manage Artificial Lift Pumps through machine learning supported exception-based surveillance. The streaming analytics system developed as part of this research used historical time-series data from three hundred and fifty-nine (359) coal seam gas wells. The developed method is currently used by two natural gas operators, where the operators can accurately detect ten (10) performance-related events and five (5) anomalous events. This paper serves a two-fold purpose; first, we describe a step-by-step methodology that readers can use to reproduce the clustering method for multivariate time-series data. Second, we demonstrate how a human-in-the-loop approach adds value to the proposed method and achieves real-world results.

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Machine Learning for Progressive Cavity Pump Performance Analysis: A Coal Seam Gas Case Study

Saghir

Perdomo

Behrenbruch

2019

Proceedings of the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference

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Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells

Cited by 3 publications

References 5 publications

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

Application of streaming analytics for Artificial Lift systems: a human-in-the-loop approach for analysing clustered time-series data from progressive cavity pumps

Machine Learning for Progressive Cavity Pump Performance Analysis: A Coal Seam Gas Case Study

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