Case Study Toward Digital Oil Field: How the ESP Operation is Changing by Using Automatic Well Models in PDO's ESP Fields

Bimani, Atika; Kulkarni, Rahul; Lee, Chia Yee; Giuliani, Valerio; Musallami, Maryam; Awaid, Abdullah; Kamyani, Nada; Kumar, Nitish; Gala, Rahul

doi:10.2118/194414-ms

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Patterns corresponding to stable periods or failure periods will be compared against patterns trained by the PCA model to draw conclusions for making predictions. 18 By this means, any historical data in the ESP system can be detected as a stable operating zone or a failure zone. Figure 1 summarizes the comments above.…”

Section: Application Of Pcamentioning

confidence: 99%

“…The testing dataset is fed to the robust PCA model as a new score matrix ( T testing ) is obtained. Patterns corresponding to stable periods or failure periods will be compared against patterns trained by the PCA model to draw conclusions for making predictions . By this means, any historical data in the ESP system can be detected as a stable operating zone or a failure zone.…”

Section: Application Of Pcamentioning

confidence: 99%

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Predictive Approach to Perform Fault Detection in Electrical Submersible Pump Systems

et al. 2021

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It has been a great challenge for the oil and gas industry to timely identify any electrical submersible pump (ESP) abnormal performance to avoid ESP failure. Given the high cost of the ESP failure, more and more real-time surveillance systems are applied to monitor ESP performance to generate alarms in the case of failures. This paper presents a robust principal component analysis (PCA) model to perform fault detection for ESP systems continuously. A three-dimensional plot of scores of principal components was used to observe different patterns during the stable and failure periods. 47 cases of actual failure events and 40 cases of stable operating events were tested on the robust PCA model to generate prediction results. The testing results demonstrate that the robust PCA model has managed to identify 20 failure events before the actual failure time out of the 47 failure cases and has successfully distinguished all the 40 stable operating wells. This study has concluded that PCA has the potential to be used as a monitoring platform to recognize dynamic change and therefore to predict the developing failures in the ESP system.

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Section: Application Of Pcamentioning

confidence: 99%

Section: Application Of Pcamentioning

confidence: 99%

Predictive Approach to Perform Fault Detection in Electrical Submersible Pump Systems

et al. 2021

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ESP Well and Component Failure Prediction in Advance using Engineered Analytics - A Breakthrough in Minimizing Unscheduled Subsurface Deferments

Marin¹,

Busaidy²,

Murad³

et al. 2019

Day 2 Tue, November 12, 2019

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A failed Electrical Submersible Pump (ESP) well is generally identified when there is no flow to the surface. The process of reviving well production can take weeks leading to huge unwanted deferment. Through a Proof-Of-Concept (PoC), the objective is to prototype and evaluate the results of an early failure detection for ESP wells using Machine Learning (ML), without reserving focus on implementation. By demonstrating the feasibility of this approach and verifying that the concept has practical potential, the tool can be used to reduce deferment and identify failure prone component to either devise mitigation strategy for extending time-to-failure or work on an improved design before failure. The paper details all the work undertaken to develop a Predictive Analytics model based on ML algorithms using field sensor data, real time physics-based model calculated data and well failure history to predict ESP well failure and identify failed component in advance. The approach of database standardization, data pre-processing, machine-learning algorithm selection, supervised training and validation dataset creation shall be discussed. ESP domain knowledge used for Feature Engineering across multiple modeling iterations to consistently improve well and component level model metrics shall be detailed. After the evaluation by well owners at Petroleum Development Oman (PDO), refered as Operator's blind test, the prediction of the ML algorithm shows a good accuracy in its ability to capture historical failures ranging between days to months in advance. The Well Level Failure model captures failure prone wells with a precision of 90% and accuracy of 76%. The Component Level Failure model correctly identifies pump failure from other failures with a precision of 92% and accuracy of 88%. These numbers show the reliability of future predictions that could enable users to make high stake workover and operating envelope optimization decisions with confidence. Following benefits are estimated from both Well failure and Pump Component failure prediction models metrics respectively: 28.35% savings from total unscheduled ESP deferment1% increase in Overall Mean Time to Failure (MTTF) based on optimization of predicted pump component failure wells. In an organization where over thousand ESP wells are managed by limited production engineers, post ESP failure, the effort invested for hoist scheduling, raising new well proposal, rig mobilization, new ESP installation and commissioning utilizes huge time and leads to long undesired oil deferment. Implementation of engineered analytics to predict ESP failures and failed components in advance can support production engineers to plan early for workover operations, increase well run life and minimize oil deferment losses. Methodologically assessed by Senior Petroleum Engineers in selected clusters (using historical data and in the context of each failure and non-failure cases), the Predictive Analytics journey has started. It is ready to be operationalized at a small scale to build confidence as an advisory tool for Production Engineers in real-time to evaluate multiple wells’ failure probability on a daily basis and generate massive savings from well deferment. This agile journey focused on value generation is achieved with combined efforts between technology, domain knowledge and data.

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Real Time Implementation of ESP Predictive Analytics - Towards Value Realization from Data Science

Marin¹,

Balushi²,

Ghadani³

et al. 2021

Day 4 Thu, November 18, 2021

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Failure Prediction in Oil and Gas Artificial Lift Systems is materializing through the implementation of advanced analytics driven by physics-based models. During the Phase I of this project, two early failure prediction machine learning models were trained offline with historical data and evaluated through a blind test. The next challenge, Phase II, is to operationalize these models on Real-Time and re-assess their accuracy, precision and early prediction (in days) while having the assets focusing on either extending the runtime through optimization, chemical injection, etc. or proactive pump replacement (PPR) for high producers wells with triggered early prediction alarms. The paper details Phase II of live prediction for two assets consisting of 740 wells to enable data-driven insights in engineers’ daily workflow. In Phase I, a collaboration between SMEs and Data Scientists was established to build two failure prediction models for Electrical Submersible Pumps (ESP) using historical data that could identify failure prone wells along with the component at risk with high precision. Phase II entails the development of a Real-Time scoring pipeline to avail daily insights from this model for live wells. To achieve this, PDO leveraged its Digital Infrastructure for extraction of high-resolution measured data for 750 wells daily. A Well Management System (WMS) automatically sustains physics-based ESP models to calculate engineering variables from nodal analysis. Measured and engineered data are sampled, and referencing learnt patterns, the machine learning algorithm (MLA) estimates the probability of failure based on a daily rolling data window. An Exception Based Surveillance (EBS) system tracks well failure probability and highlights affected wells based on business logic. A visualization is developed to facilitate EBS interpretation. All the above steps are automated and synchronized among data historian, WMS and EBS System to operate on a daily schedule. From the Asset, at each highlighted exception, a focus team of well owners and SME initiate a review to correlate the failure probability with ESP signatures to validate the alarm. Aided by physics-based well models, action is directed either towards a) optimization, b) troubleshooting or c) proactive pump replacement in case of inevitable failure conditions. This workflow enables IT infrastructure and Asset readiness to benefit from various modeling initiatives in subsequent phases. Live Implementation of Exceptions from Predictive Analytics is an effective complement to well owners for prioritization of well reviews. Based on alarm validity, risk of failure and underperformance – optimizations, PPRs or workover scheduling are performed with reliability. This methodology would enable a Phase III of scaling up in Real-Time with growing assets wherethe system would be periodically retrained on True Negatives and maintained automatically with minimum manual intervention. It is experienced that a high precision model alone is not enough to reap the benefits of Predictive Analytics. The ability to operate in a production mode and embedding insights into decisions and actions, determines ROI on Data Science initiatives. Digital Infrastructure, a Real Time Well Modeling Platform and Cognitive adaptation of analytics by Well Owners are key for this operationalization that demands reliable data quality, computational efficiency, and data-driven decisions philosophy.

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Case Study Toward Digital Oil Field: How the ESP Operation is Changing by Using Automatic Well Models in PDO's ESP Fields

Cited by 5 publications

References 2 publications

Predictive Approach to Perform Fault Detection in Electrical Submersible Pump Systems

Predictive Approach to Perform Fault Detection in Electrical Submersible Pump Systems

ESP Well and Component Failure Prediction in Advance using Engineered Analytics - A Breakthrough in Minimizing Unscheduled Subsurface Deferments

Real Time Implementation of ESP Predictive Analytics - Towards Value Realization from Data Science

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