Enhanced Data Quality and Lowered Risk Utilizing Remote Operations

Thorsen, Arve K.; Saeverhagen, Erland; Svensson, Inge

doi:10.2118/167866-ms

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…The logistic practice approaches propose to develop new standards and improve the operation collaboration . The rig site practices propose to add/develop more sensors , and to standardize the transmission and text abbreviation . The administrative approaches propose using key performance indicators (KPIs) to evaluate the quality of data and to develop a data quality dashboard, real-time data quality center, , and others.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

A Game-Theoretic Approach to Improve Energy-Related Data

et al. 2022

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Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality

Al-Gharbi

Al-Majed

Elkatatny

et al. 2022

Journal of Energy Resources Technology

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Due to high demand for energy, oil and gas companies started to drill wells in remote environments conducting unconventional operations. In order to maintain safe, fast and more cost-effective operations, utilizing machine learning (ML) technologies has become a must. The harsh environments of drilling sites and the transmission setups, are negatively affecting the drilling data, leading to less than acceptable ML results. For that reason, big portion of ML development projects were actually spent on improving the data by data-quality experts. The objective of this paper is to evaluate the effectiveness of ML on improving the real-time drilling-data-quality and compare it to a human expert knowledge. To achieve that, two large real-time drilling datasets were used; one dataset was used to train three different ML techniques: artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), the second dataset was used to evaluate it. The ML results were compared with the results of a real- time drilling data quality expert. Despite the complexity of ANN and good results in general, it achieved a relative root mean square error (RRMSE) of 2.83%, which was lower than DT and SVM technologies that achieved RRMSE of 0.35% and 0.48% respectively. The uniqueness of this work is in developing ML that simulates the improvement of drilling-data- quality by an expert. This research provides a guide for improving the quality of real-time drilling data.

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Statistical Methods to Improve the Quality of Real-Time Drilling Data

Al-Gharbi

Al-Majed

Abdulraheem

et al. 2022

Journal of Energy Resources Technology

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The age of easy oil is ending, the industry started drilling in remote unconventional conditions. To help produce safer, faster, and most effective operations, the utilization of artificial intelligence and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only if the input data are good, the AI/ML generated analytical and prediction models will be good. Improving the real-time data is therefore critical to the drilling industry. The objective of this paper is to propose an automated approach using eight statistical data-quality improvement algorithms on real-time drilling data. These techniques are Kalman filtering, moving average, kernel regression, median filter, exponential smoothing, lowess, wavelet filtering, and polynomial. A dataset of +150,000 rows is fed into the algorithms, and their customizable parameters are calibrated to achieve the best improvement result. An evaluation methodology is developed based on real-time drilling data characteristics to analyze the strengths and weaknesses of each algorithm were highlighted. Based on the evaluation criteria, the best results were achieved using the exponential smoothing, median filter, and moving average. Exponential smoothing and median filter techniques improved the quality of data by removing most of the invalid data points, the moving average removed more invalid data-points but trimmed the data range.

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Enhanced Data Quality and Lowered Risk Utilizing Remote Operations

Cited by 4 publications

References 4 publications

A Game-Theoretic Approach to Improve Energy-Related Data

A Game-Theoretic Approach to Improve Energy-Related Data

Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality

Statistical Methods to Improve the Quality of Real-Time Drilling Data

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