Saudi Aramco Real-Time Drilling Operation Activity Recognition &amp; Analysis Engine

Khudiri, Musab; Contreras, William; sanie, Faisal S. Al; Jovin, Tony; Hickin, Gary; Kashif, Muhammad

doi:10.2118/176799-ms

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…The data set related to this activity can be identified by comparing the bit depth to the well depth. 18 If it is increased, then it belongs to an actual drilling activity. Equations 1 and 2 are a mathematical representation of this classification.…”

Section: Current Practicesmentioning

confidence: 99%

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A Game-Theoretic Approach to Improve Energy-Related Data

et al. 2022

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With the increase in the energy demand, the magnitude of energy production operation increased in scale and complexity and went too far in remote areas. To manage such a big fleet, sensors were installed to send real-time data to operation centers, where subject matter experts monitor the operations and provide live support. With the expansion of installed sensors and the number of monitored operations, the operation centers were flooded with a massive amount of data beyond human capability to handle. As a result, it became essential to capitalize on the artificial intelligence (AI) capability. Unfortunately, due to the nature of operations, the data quality is an issue limiting the impact of AI in such operations. Multiple approaches were proposed, but they require lot of time and cannot be upscaled to support active real-time data streaming. This paper presents a method to improve the quality of energy-related (drilling) real-time data, such as hook load (HL), rate of penetration (ROP), revolution per minute (RPM), and others. The method is based on a game-theoretic approach, and when applied on the HL—one of the most challenging drilling parameters—it achieved a root mean square error (RMSE) of 3.3 accuracy level compared to the drilling data quality improvement subject matter expert’s (SME) level. This method took few minutes to improve the drilling data quality compared to weeks in the traditional manual/semiautomated methods. This paper addresses the energy data quality issue, which is one of the biggest bottlenecks toward upscaling AI technology into active operations. To the authors’ knowledge, this paper is the first attempt to employ the game-theoretic approach in the drilling data improvement process, which facilitates greater integration between AI models and the energy live data streaming, also setting the stage for more research in this challenging AI-data domain.

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Section: Current Practicesmentioning

confidence: 99%

“…This represents one of the most challenging activities in the drilling operation. The data set related to this activity can be identified by comparing the bit depth to the well depth . If it is increased, then it belongs to an actual drilling activity.…”

Section: Current Practicesmentioning

confidence: 99%

A Game-Theoretic Approach to Improve Energy-Related Data

et al. 2022

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“…To maximize the utility of this data, further analysis is necessary to calculate performance metrics for specific rig activities. This involves developing algorithms to automatically recognize drilling activities based on real-time surface parameters and then using this information to derive key performance indicators (KPIs) from observed operations [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

Elekhteiar,

Salem,

El-Fakharany

2024

Preprint

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A Comprehensive Approach to Measure the RealTime Data Quality Using Key Performance Indicators

Otalvora

Al-Khudiri

Al-Sanie

et al. 2016

Day 1 Mon, September 26, 2016

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After the setup of a real-time drilling data feed from the Rig site to the operations center, the obvious question is - How good is the data that is being streamed? Real-Time rig sensor data could be an effective input for drilling optimization, However, the confidence in the results of these analysis or interpretations is directly associated to the trustworthiness of the data acquired. Measuring the quality of the data has been a difficult issue for many years. This paper shows the approach used by a large oil & gas operator to measure the quality of the information received from the rig providers, which is obtained from the sensors installed on every rig. The Real Time Data Quality is measured by analyzing six different parameters: completeness, uniformity, sensibility, resolution, structure and format. The combination of these six indexes provides a deep understanding of the level of the quality of the data received, as well as the elements to be improved. Various KPIs including Footage KPI, Data QC Availability and Data QC Channels, are generated by the powerful engine of Automated Rig Activity Measurement (ARAM) to check the data quality of different vendors. Starting from having a check on data availability, these KPI's are capable of analyzing every row of time based data in real-time. Data generated by a proprietary system for these KPI's is also being used in a Real Time Data Quality Dashboard. This measurement provides the drilling engineers with a systematic way to filter analysis results based on data of poor quality by specifying their acceptable data quality range. This daily analysis also allows the operator to identify Rigs that are performing better and rigs that needs to be pushed for higher quality data.

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Saudi Aramco Real-Time Drilling Operation Activity Recognition & Analysis Engine

Cited by 5 publications

References 2 publications

A Game-Theoretic Approach to Improve Energy-Related Data

A Game-Theoretic Approach to Improve Energy-Related Data

A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

A Comprehensive Approach to Measure the RealTime Data Quality Using Key Performance Indicators

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